Delete SLACK_SUMMARY.md

Co-authored-by: Rip&Tear <theCyberTech@users.noreply.github.com>

docs/en/changelog.mdx

@@ -4,39 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="Mar 11, 2026">
-  ## v1.10.2a1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## What's Changed
-
-  ### Features
-  - Add support for tool search, saving tokens, and dynamically injecting appropriate tools during execution for Anthropics.
-  - Introduce more Brave Search tools.
-  - Create action for nightly releases.
-
-  ### Bug Fixes
-  - Fix LockException under concurrent multi-process execution.
-  - Resolve issues with grouping parallel tool results in a single user message.
-  - Address MCP tools resolutions and eliminate all shared mutable connections.
-  - Update LLM parameter handling in the human_feedback function.
-  - Add missing list/dict methods to LockedListProxy and LockedDictProxy.
-  - Propagate contextvars context to parallel tool call threads.
-  - Bump gitpython dependency to >=3.1.41 to resolve CVE path traversal vulnerability.
-
-  ### Refactoring
-  - Refactor memory classes to be serializable.
-
-  ### Documentation
-  - Update changelog and version for v1.10.1.
-
-  ## Contributors
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
 <Update label="Mar 04, 2026">
   ## v1.10.1
 

docs/en/tools/search-research/bravesearchtool.mdx

@@ -1,316 +1,97 @@
 ---
-title: Brave Search Tools
-description: A suite of tools for querying the Brave Search API — covering web, news, image, and video search.
+title: Brave Search
+description: The `BraveSearchTool` is designed to search the internet using the Brave Search API.
 icon: searchengin
 mode: "wide"
 ---
 
-# Brave Search Tools
+# `BraveSearchTool`
 
 ## Description
 
-CrewAI offers a family of Brave Search tools, each targeting a specific [Brave Search API](https://brave.com/search/api/) endpoint.
-Rather than a single catch-all tool, you can pick exactly the tool that matches the kind of results your agent needs:
-
-| Tool | Endpoint | Use case |
-| --- | --- | --- |
-| `BraveWebSearchTool` | Web Search | General web results, snippets, and URLs |
-| `BraveNewsSearchTool` | News Search | Recent news articles and headlines |
-| `BraveImageSearchTool` | Image Search | Image results with dimensions and source URLs |
-| `BraveVideoSearchTool` | Video Search | Video results from across the web |
-| `BraveLocalPOIsTool` | Local POIs | Find points of interest (e.g., restaurants) |
-| `BraveLocalPOIsDescriptionTool` | Local POIs | Retrieve AI-generated location descriptions |
-| `BraveLLMContextTool` | LLM Context | Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines. |
-
-All tools share a common base class (`BraveSearchToolBase`) that provides consistent behavior — rate limiting, automatic retries on `429` responses, header and parameter validation, and optional file saving.
-
-<Note>
-  The older `BraveSearchTool` class is still available for backwards compatibility, but it is considered **legacy** and will not receive the same level of attention going forward. We recommend migrating to the specific tools listed above, which offer richer configuration and a more focused interface.
-</Note>
-
-<Note>
-    While many tools (e.g., _BraveWebSearchTool_, _BraveNewsSearchTool_, _BraveImageSearchTool_, and _BraveVideoSearchTool_) can be used with a free Brave Search API subscription/plan, some parameters (e.g., `enable_snippets`) and tools (e.g., _BraveLocalPOIsTool_ and _BraveLocalPOIsDescriptionTool_) require a paid plan. Consult your subscription plan's capabilities for clarification.
-</Note>
+This tool is designed to perform web searches using the Brave Search API. It allows you to search the internet with a specified query and retrieve relevant results. The tool supports customizable result counts and country-specific searches.
 
 ## Installation
 
+To incorporate this tool into your project, follow the installation instructions below:
+
 ```shell
 pip install 'crewai[tools]'
 ```
 
-## Getting Started
+## Steps to Get Started
 
-1. **Install the package** — confirm that `crewai[tools]` is installed in your Python environment.
-2. **Get an API key** — sign up at [api-dashboard.search.brave.com/login](https://api-dashboard.search.brave.com/login) to generate a key.
-3. **Set the environment variable** — store your key as `BRAVE_API_KEY`, or pass it directly via the `api_key` parameter.
+To effectively use the `BraveSearchTool`, follow these steps:
 
-## Quick Examples
+1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **API Key Acquisition**: Acquire a Brave Search API key at https://api.search.brave.com/app/keys (sign in to generate a key).
+3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool.
 
-### Web Search
+## Example
+
+The following example demonstrates how to initialize the tool and execute a search with a given query:
 
 ```python Code
-from crewai_tools import BraveWebSearchTool
+from crewai_tools import BraveSearchTool
 
-tool = BraveWebSearchTool()
-results = tool.run(q="CrewAI agent framework")
+# Initialize the tool for internet searching capabilities
+tool = BraveSearchTool()
+
+# Execute a search
+results = tool.run(search_query="CrewAI agent framework")
 print(results)
 ```
 
-### News Search
+## Parameters
+
+The `BraveSearchTool` accepts the following parameters:
+
+- **search_query**: Mandatory. The search query you want to use to search the internet.
+- **country**: Optional. Specify the country for the search results. Default is empty string.
+- **n_results**: Optional. Number of search results to return. Default is `10`.
+- **save_file**: Optional. Whether to save the search results to a file. Default is `False`.
+
+## Example with Parameters
+
+Here is an example demonstrating how to use the tool with additional parameters:
 
 ```python Code
-from crewai_tools import BraveNewsSearchTool
+from crewai_tools import BraveSearchTool
 
-tool = BraveNewsSearchTool()
-results = tool.run(q="latest AI breakthroughs")
-print(results)
-```
-
-### Image Search
-
-```python Code
-from crewai_tools import BraveImageSearchTool
-
-tool = BraveImageSearchTool()
-results = tool.run(q="northern lights photography")
-print(results)
-```
-
-### Video Search
-
-```python Code
-from crewai_tools import BraveVideoSearchTool
-
-tool = BraveVideoSearchTool()
-results = tool.run(q="how to build AI agents")
-print(results)
-```
-
-### Location POI Descriptions
-
-```python Code
-from crewai_tools import (
-    BraveWebSearchTool,
-    BraveLocalPOIsDescriptionTool,
+# Initialize the tool with custom parameters
+tool = BraveSearchTool(
+    country="US",
+    n_results=5,
+    save_file=True
 )
 
-web_search = BraveWebSearchTool(raw=True)
-poi_details = BraveLocalPOIsDescriptionTool()
-
-results = web_search.run(q="italian restaurants in pensacola, florida")
-
-if "locations" in results:
-    location_ids = [ loc["id"] for loc in results["locations"]["results"] ]
-    if location_ids:
-        descriptions = poi_details.run(ids=location_ids)
-        print(descriptions)
-```
-
-## Common Constructor Parameters
-
-Every Brave Search tool accepts the following parameters at initialization:
-
-| Parameter | Type | Default | Description |
-| --- | --- | --- | --- |
-| `api_key` | `str \| None` | `None` | Brave API key. Falls back to the `BRAVE_API_KEY` environment variable. |
-| `headers` | `dict \| None` | `None` | Additional HTTP headers to send with every request (e.g., `api-version`, geolocation headers). |
-| `requests_per_second` | `float` | `1.0` | Maximum request rate. The tool will sleep between calls to stay within this limit. |
-| `save_file` | `bool` | `False` | When `True`, each response is written to a timestamped `.txt` file. |
-| `raw` | `bool` | `False` | When `True`, the full API JSON response is returned without any refinement. |
-| `timeout` | `int` | `30` | HTTP request timeout in seconds. |
-| `country` | `str \| None` | `None` | Legacy shorthand for geo-targeting (e.g., `"US"`). Prefer using the `country` query parameter directly. |
-| `n_results` | `int` | `10` | Legacy shorthand for result count. Prefer using the `count` query parameter directly. |
-
-<Warning>
-  The `country` and `n_results` constructor parameters exist for backwards compatibility. They are applied as defaults when the corresponding query parameters (`country`, `count`) are not provided at call time. For new code, we recommend passing `country` and `count` directly as query parameters instead.
-</Warning>
-
-## Query Parameters
-
-Each tool validates its query parameters against a Pydantic schema before sending the request.
-The parameters vary slightly per endpoint — here is a summary of the most commonly used ones:
-
-### BraveWebSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting (e.g., `"US"`). |
-| `search_lang` | Two-letter language code for results (e.g., `"en"`). |
-| `count` | Max number of results to return (1–20). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter: `"pd"` (past day), `"pw"` (past week), `"pm"` (past month), `"py"` (past year), or a date range like `"2025-01-01to2025-06-01"`. |
-| `extra_snippets` | Include up to 5 additional text snippets per result. |
-| `goggles` | Brave Goggles URL(s) and/or source for custom re-ranking. |
-
-For the complete parameter and header reference, see the [Brave Web Search API documentation](https://api-dashboard.search.brave.com/api-reference/web/search/get).
-
-### BraveNewsSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–50). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter (same options as Web Search). |
-| `goggles` | Brave Goggles URL(s) and/or source for custom re-ranking. |
-
-For the complete parameter and header reference, see the [Brave News Search API documentation](https://api-dashboard.search.brave.com/api-reference/news/news_search/get).
-
-### BraveImageSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–200). |
-| `safesearch` | Content filter: `"off"` or `"strict"`. |
-| `spellcheck` | Attempt to correct spelling errors in the query. |
-
-For the complete parameter and header reference, see the [Brave Image Search API documentation](https://api-dashboard.search.brave.com/api-reference/images/image_search).
-
-### BraveVideoSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–50). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter (same options as Web Search). |
-
-For the complete parameter and header reference, see the [Brave Video Search API documentation](https://api-dashboard.search.brave.com/api-reference/videos/video_search/get).
-
-### BraveLocalPOIsTool
-
-| Parameter | Description |
-| --- | --- |
-| `ids` | **(required)** A list of unique identifiers for the desired locations. |
-| `search_lang` | Two-letter language code for results. |
-
-For the complete parameter and header reference, see [Brave Local POIs API documentation](https://api-dashboard.search.brave.com/api-reference/web/local_pois).
-
-### BraveLocalPOIsDescriptionTool
-
-| Parameter | Description |
-| --- | --- |
-| `ids` | **(required)** A list of unique identifiers for the desired locations. |
-
-For the complete parameter and header reference, see [Brave POI Descriptions API documentation](https://api-dashboard.search.brave.com/api-reference/web/poi_descriptions).
-
-## Custom Headers
-
-All tools support custom HTTP request headers. The Web Search tool, for example, accepts geolocation headers for location-aware results:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(
-    headers={
-        "x-loc-lat": "37.7749",
-        "x-loc-long": "-122.4194",
-        "x-loc-city": "San Francisco",
-        "x-loc-state": "CA",
-        "x-loc-country": "US",
-    }
-)
-
-results = tool.run(q="best coffee shops nearby")
-```
-
-You can also update headers after initialization using the `set_headers()` method:
-
-```python Code
-tool.set_headers({"api-version": "2025-01-01"})
-```
-
-## Raw Mode
-
-By default, each tool refines the API response into a concise list of results. If you need the full, unprocessed API response, enable raw mode:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(raw=True)
-full_response = tool.run(q="Brave Search API")
+# Execute a search
+results = tool.run(search_query="Latest AI developments")
+print(results)
 ```
 
 ## Agent Integration Example
 
-Here's how to equip a CrewAI agent with multiple Brave Search tools:
+Here's how to integrate the `BraveSearchTool` with a CrewAI agent:
 
 ```python Code
 from crewai import Agent
 from crewai.project import agent
-from crewai_tools import BraveWebSearchTool, BraveNewsSearchTool
+from crewai_tools import BraveSearchTool
 
-web_search = BraveWebSearchTool()
-news_search = BraveNewsSearchTool()
+# Initialize the tool
+brave_search_tool = BraveSearchTool()
 
+# Define an agent with the BraveSearchTool
 @agent
 def researcher(self) -> Agent:
     return Agent(
         config=self.agents_config["researcher"],
-        tools=[web_search, news_search],
+        allow_delegation=False,
+        tools=[brave_search_tool]
     )
 ```
 
-## Advanced Example
-
-Combining multiple parameters for a targeted search:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(
-    requests_per_second=0.5,  # conservative rate limit
-    save_file=True,
-)
-
-results = tool.run(
-    q="artificial intelligence news",
-    country="US",
-    search_lang="en",
-    count=5,
-    freshness="pm",           # past month only
-    extra_snippets=True,
-)
-print(results)
-```
-
-## Migrating from `BraveSearchTool` (Legacy)
-
-If you are currently using `BraveSearchTool`, switching to the new tools is straightforward:
-
-```python Code
-# Before (legacy)
-from crewai_tools import BraveSearchTool
-
-tool = BraveSearchTool(country="US", n_results=5, save_file=True)
-results = tool.run(search_query="AI agents")
-
-# After (recommended)
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(save_file=True)
-results = tool.run(q="AI agents", country="US", count=5)
-```
-
-Key differences:
-- **Import**: Use `BraveWebSearchTool` (or the news/image/video variant) instead of `BraveSearchTool`.
-- **Query parameter**: Use `q` instead of `search_query`. (Both `search_query` and `query` are still accepted for convenience, but `q` is the preferred parameter.)
-- **Result count**: Pass `count` as a query parameter instead of `n_results` at init time.
-- **Country**: Pass `country` as a query parameter instead of at init time.
-- **API key**: Can now be passed directly via `api_key=` in addition to the `BRAVE_API_KEY` environment variable.
-- **Rate limiting**: Configurable via `requests_per_second` with automatic retry on `429` responses.
-
 ## Conclusion
 
-The Brave Search tool suite gives your CrewAI agents flexible, endpoint-specific access to the Brave Search API. Whether you need web pages, breaking news, images, or videos, there is a dedicated tool with validated parameters and built-in resilience. Pick the tool that fits your use case, and refer to the [Brave Search API documentation](https://brave.com/search/api/) for the full details on available parameters and response formats.
+By integrating the `BraveSearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The tool provides a simple interface to the powerful Brave Search API, making it easy to retrieve and process search results programmatically. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. 

docs/ko/changelog.mdx

@@ -4,39 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="2026년 3월 11일">
-  ## v1.10.2a1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## 변경 사항
-
-  ### 기능
-  - Anthropics에 대한 도구 검색 지원 추가, 토큰 저장, 실행 중 적절한 도구를 동적으로 주입하는 기능 추가.
-  - 더 많은 Brave Search 도구 도입.
-  - 야간 릴리스를 위한 액션 생성.
-
-  ### 버그 수정
-  - 동시 다중 프로세스 실행 중 LockException 수정.
-  - 단일 사용자 메시지에서 병렬 도구 결과 그룹화 문제 해결.
-  - MCP 도구 해상도 문제 해결 및 모든 공유 가변 연결 제거.
-  - human_feedback 함수에서 LLM 매개변수 처리 업데이트.
-  - LockedListProxy 및 LockedDictProxy에 누락된 list/dict 메서드 추가.
-  - 병렬 도구 호출 스레드에 contextvars 컨텍스트 전파.
-  - CVE 경로 탐색 취약점을 해결하기 위해 gitpython 의존성을 >=3.1.41로 업데이트.
-
-  ### 리팩토링
-  - 메모리 클래스를 직렬화 가능하도록 리팩토링.
-
-  ### 문서
-  - v1.10.1에 대한 변경 로그 및 버전 업데이트.
-
-  ## 기여자
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
 <Update label="2026년 3월 4일">
   ## v1.10.1
 

docs/pt-BR/changelog.mdx

@@ -4,39 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="11 mar 2026">
-  ## v1.10.2a1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## O que mudou
-
-  ### Recursos
-  - Adicionar suporte para busca de ferramentas, salvamento de tokens e injeção dinâmica de ferramentas apropriadas durante a execução para Anthropics.
-  - Introduzir mais ferramentas de Busca Brave.
-  - Criar ação para lançamentos noturnos.
-
-  ### Correções de Bugs
-  - Corrigir LockException durante a execução concorrente de múltiplos processos.
-  - Resolver problemas com a agrupação de resultados de ferramentas paralelas em uma única mensagem de usuário.
-  - Abordar resoluções de ferramentas MCP e eliminar todas as conexões mutáveis compartilhadas.
-  - Atualizar o manuseio de parâmetros LLM na função human_feedback.
-  - Adicionar métodos de lista/dicionário ausentes a LockedListProxy e LockedDictProxy.
-  - Propagar o contexto de contextvars para as threads de chamada de ferramentas paralelas.
-  - Atualizar a dependência gitpython para >=3.1.41 para resolver a vulnerabilidade de travessia de diretórios CVE.
-
-  ### Refatoração
-  - Refatorar classes de memória para serem serializáveis.
-
-  ### Documentação
-  - Atualizar o changelog e a versão para v1.10.1.
-
-  ## Contribuidores
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
 <Update label="04 mar 2026">
   ## v1.10.1
 

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.10.2a1"
+__version__ = "1.10.1"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.10.2a1",
+    "crewai==1.10.1",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -10,18 +10,7 @@ from crewai_tools.aws.s3.writer_tool import S3WriterTool
 from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool
 from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool
 from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool
-from crewai_tools.tools.brave_search_tool.brave_image_tool import BraveImageSearchTool
-from crewai_tools.tools.brave_search_tool.brave_llm_context_tool import (
-    BraveLLMContextTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_local_pois_tool import (
-    BraveLocalPOIsDescriptionTool,
-    BraveLocalPOIsTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_news_tool import BraveNewsSearchTool
 from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
-from crewai_tools.tools.brave_search_tool.brave_video_tool import BraveVideoSearchTool
-from crewai_tools.tools.brave_search_tool.brave_web_tool import BraveWebSearchTool
 from crewai_tools.tools.brightdata_tool.brightdata_dataset import (
     BrightDataDatasetTool,
 )
@@ -211,14 +200,7 @@ __all__ = [
     "ArxivPaperTool",
     "BedrockInvokeAgentTool",
     "BedrockKBRetrieverTool",
-    "BraveImageSearchTool",
-    "BraveLLMContextTool",
-    "BraveLocalPOIsDescriptionTool",
-    "BraveLocalPOIsTool",
-    "BraveNewsSearchTool",
     "BraveSearchTool",
-    "BraveVideoSearchTool",
-    "BraveWebSearchTool",
     "BrightDataDatasetTool",
     "BrightDataSearchTool",
     "BrightDataWebUnlockerTool",
@@ -309,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.10.2a1"
+__version__ = "1.10.1"

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

@@ -1,18 +1,7 @@
 from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool
 from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool
 from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool
-from crewai_tools.tools.brave_search_tool.brave_image_tool import BraveImageSearchTool
-from crewai_tools.tools.brave_search_tool.brave_llm_context_tool import (
-    BraveLLMContextTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_local_pois_tool import (
-    BraveLocalPOIsDescriptionTool,
-    BraveLocalPOIsTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_news_tool import BraveNewsSearchTool
 from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
-from crewai_tools.tools.brave_search_tool.brave_video_tool import BraveVideoSearchTool
-from crewai_tools.tools.brave_search_tool.brave_web_tool import BraveWebSearchTool
 from crewai_tools.tools.brightdata_tool import (
     BrightDataDatasetTool,
     BrightDataSearchTool,
@@ -196,14 +185,7 @@ __all__ = [
     "AIMindTool",
     "ApifyActorsTool",
     "ArxivPaperTool",
-    "BraveImageSearchTool",
-    "BraveLLMContextTool",
-    "BraveLocalPOIsDescriptionTool",
-    "BraveLocalPOIsTool",
-    "BraveNewsSearchTool",
     "BraveSearchTool",
-    "BraveVideoSearchTool",
-    "BraveWebSearchTool",
     "BrightDataDatasetTool",
     "BrightDataSearchTool",
     "BrightDataWebUnlockerTool",

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/base.py

@@ -1,322 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from datetime import datetime
-import json
-import logging
-import os
-import threading
-import time
-from typing import Any, ClassVar
-
-from crewai.tools import BaseTool, EnvVar
-from pydantic import BaseModel, Field
-import requests
-
-
-logger = logging.getLogger(__name__)
-
-# Brave API error codes that indicate non-retryable quota/usage exhaustion.
-_QUOTA_CODES = frozenset({"QUOTA_LIMITED", "USAGE_LIMIT_EXCEEDED"})
-
-
-def _save_results_to_file(content: str) -> None:
-    """Saves the search results to a file."""
-    filename = f"search_results_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt"
-    with open(filename, "w") as file:
-        file.write(content)
-
-
-def _parse_error_body(resp: requests.Response) -> dict[str, Any] | None:
-    """Extract the structured "error" object from a Brave API error response."""
-    try:
-        body = resp.json()
-        error = body.get("error")
-        return error if isinstance(error, dict) else None
-    except (ValueError, KeyError):
-        return None
-
-
-def _raise_for_error(resp: requests.Response) -> None:
-    """Brave Search API error responses contain helpful JSON payloads"""
-    status = resp.status_code
-    try:
-        body = json.dumps(resp.json())
-    except (ValueError, KeyError):
-        body = resp.text[:500]
-
-    raise RuntimeError(f"Brave Search API error (HTTP {status}): {body}")
-
-
-def _is_retryable(resp: requests.Response) -> bool:
-    """Return True for transient failures that are worth retrying.
-
-    * 429 + RATE_LIMITED — the per-second sliding window is full.
-    * 5xx — transient server-side errors.
-
-    Quota exhaustion (QUOTA_LIMITED, USAGE_LIMIT_EXCEEDED) is
-    explicitly excluded: retrying will never succeed until the billing
-    period resets.
-    """
-    if resp.status_code == 429:
-        error = _parse_error_body(resp) or {}
-        return error.get("code") not in _QUOTA_CODES
-    return 500 <= resp.status_code < 600
-
-
-def _retry_delay(resp: requests.Response, attempt: int) -> float:
-    """Compute wait time before the next retry attempt.
-
-    Prefers the server-supplied Retry-After header when available;
-    falls back to exponential backoff (1s, 2s, 4s, ...).
-    """
-    retry_after = resp.headers.get("Retry-After")
-    if retry_after is not None:
-        try:
-            return max(0.0, float(retry_after))
-        except (ValueError, TypeError):
-            pass
-    return float(2**attempt)
-
-
-class BraveSearchToolBase(BaseTool, ABC):
-    """
-    Base class for Brave Search API interactions.
-
-    Individual tool subclasses must provide the following:
-      - search_url
-      - header_schema (pydantic model)
-      - args_schema (pydantic model)
-      - _refine_payload() -> dict[str, Any]
-    """
-
-    search_url: str
-    raw: bool = False
-    args_schema: type[BaseModel]
-    header_schema: type[BaseModel]
-
-    # Tool options (legacy parameters)
-    country: str | None = None
-    save_file: bool = False
-    n_results: int = 10
-
-    env_vars: list[EnvVar] = Field(
-        default_factory=lambda: [
-            EnvVar(
-                name="BRAVE_API_KEY",
-                description="API key for Brave Search",
-                required=True,
-            ),
-        ]
-    )
-
-    def __init__(
-        self,
-        *,
-        api_key: str | None = None,
-        headers: dict[str, Any] | None = None,
-        requests_per_second: float = 1.0,
-        save_file: bool = False,
-        raw: bool = False,
-        timeout: int = 30,
-        **kwargs: Any,
-    ):
-        super().__init__(**kwargs)
-
-        self._api_key = api_key or os.environ.get("BRAVE_API_KEY")
-        if not self._api_key:
-            raise ValueError("BRAVE_API_KEY environment variable is required")
-
-        self.raw = bool(raw)
-        self._timeout = int(timeout)
-        self.save_file = bool(save_file)
-        self._requests_per_second = float(requests_per_second)
-        self._headers = self._build_and_validate_headers(headers or {})
-        # Per-instance rate limiting: each instance has its own clock and lock.
-        # Total process rate is the sum of limits of instances you create.
-        self._last_request_time: float = 0
-        self._rate_limit_lock = threading.Lock()
-
-    @property
-    def api_key(self) -> str:
-        return self._api_key
-
-    @property
-    def headers(self) -> dict[str, Any]:
-        return self._headers
-
-    def set_headers(self, headers: dict[str, Any]) -> BraveSearchToolBase:
-        merged = {**self._headers, **{k.lower(): v for k, v in headers.items()}}
-        self._headers = self._build_and_validate_headers(merged)
-        return self
-
-    def _build_and_validate_headers(self, headers: dict[str, Any]) -> dict[str, Any]:
-        normalized = {k.lower(): v for k, v in headers.items()}
-        normalized.setdefault("x-subscription-token", self._api_key)
-        normalized.setdefault("accept", "application/json")
-
-        try:
-            self.header_schema(**normalized)
-        except Exception as e:
-            raise ValueError(f"Invalid headers: {e}") from e
-
-        return normalized
-
-    def _rate_limit(self) -> None:
-        """Enforce minimum interval between requests for this instance. Thread-safe."""
-        if self._requests_per_second <= 0:
-            return
-
-        min_interval = 1.0 / self._requests_per_second
-        with self._rate_limit_lock:
-            now = time.time()
-            next_allowed = self._last_request_time + min_interval
-            if now < next_allowed:
-                time.sleep(next_allowed - now)
-                now = time.time()
-            self._last_request_time = now
-
-    def _make_request(
-        self, params: dict[str, Any], *, _max_retries: int = 3
-    ) -> dict[str, Any]:
-        """Execute an HTTP GET against the Brave Search API with retry logic."""
-        last_resp: requests.Response | None = None
-
-        # Retry the request up to _max_retries times
-        for attempt in range(_max_retries):
-            self._rate_limit()
-
-            # Make the request
-            try:
-                resp = requests.get(
-                    self.search_url,
-                    headers=self._headers,
-                    params=params,
-                    timeout=self._timeout,
-                )
-            except requests.ConnectionError as exc:
-                raise RuntimeError(
-                    f"Brave Search API connection failed: {exc}"
-                ) from exc
-            except requests.Timeout as exc:
-                raise RuntimeError(
-                    f"Brave Search API request timed out after {self._timeout}s: {exc}"
-                ) from exc
-
-            # Log the rate limit headers and request details
-            logger.debug(
-                "Brave Search API request: %s %s -> %d",
-                "GET",
-                resp.url,
-                resp.status_code,
-            )
-
-            # Response was OK, return the JSON body
-            if resp.ok:
-                try:
-                    return resp.json()
-                except ValueError as exc:
-                    raise RuntimeError(
-                        f"Brave Search API returned invalid JSON (HTTP {resp.status_code}): {exc}"
-                    ) from exc
-
-            # Response was not OK, but is retryable
-            # (e.g., 429 Too Many Requests, 500 Internal Server Error)
-            if _is_retryable(resp) and attempt < _max_retries - 1:
-                delay = _retry_delay(resp, attempt)
-                logger.warning(
-                    "Brave Search API returned %d. Retrying in %.1fs (attempt %d/%d)",
-                    resp.status_code,
-                    delay,
-                    attempt + 1,
-                    _max_retries,
-                )
-                time.sleep(delay)
-                last_resp = resp
-                continue
-
-            # Response was not OK, nor was it retryable
-            # (e.g., 422 Unprocessable Entity, 400 Bad Request (OPTION_NOT_IN_PLAN))
-            _raise_for_error(resp)
-
-        # All retries exhausted
-        _raise_for_error(last_resp or resp)  # type: ignore[possibly-undefined]
-        return {}  # unreachable (here to satisfy the type checker and linter)
-
-    def _run(self, q: str | None = None, **params: Any) -> Any:
-        # Allow positional usage: tool.run("latest Brave browser features")
-        if q is not None:
-            params["q"] = q
-
-        params = self._common_payload_refinement(params)
-
-        # Validate only schema fields
-        schema_keys = self.args_schema.model_fields
-        payload_in = {k: v for k, v in params.items() if k in schema_keys}
-
-        try:
-            validated = self.args_schema(**payload_in)
-        except Exception as e:
-            raise ValueError(f"Invalid parameters: {e}") from e
-
-        # The subclass may have additional refinements to apply to the payload, such as goggles or other parameters
-        payload = self._refine_request_payload(validated.model_dump(exclude_none=True))
-        response = self._make_request(payload)
-
-        if not self.raw:
-            response = self._refine_response(response)
-
-        if self.save_file:
-            _save_results_to_file(json.dumps(response, indent=2))
-
-        return response
-
-    @abstractmethod
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        """Subclass must implement: transform validated params dict into API request params."""
-        raise NotImplementedError
-
-    @abstractmethod
-    def _refine_response(self, response: dict[str, Any]) -> Any:
-        """Subclass must implement: transform response dict into a more useful format."""
-        raise NotImplementedError
-
-    _EMPTY_VALUES: ClassVar[tuple[None, str, str, list[Any]]] = (None, "", "null", [])
-
-    def _common_payload_refinement(self, params: dict[str, Any]) -> dict[str, Any]:
-        """Common payload refinement for all tools."""
-        # crewAI's schema pipeline (ensure_all_properties_required in
-        # pydantic_schema_utils.py) marks every property as required so
-        # that OpenAI strict-mode structured outputs work correctly.
-        # The side-effect is that the LLM fills in *every* parameter —
-        # even truly optional ones — using placeholder values such as
-        # None, "", "null", or [].  Only optional fields are affected,
-        # so we limit the check to those.
-        fields = self.args_schema.model_fields
-        params = {
-            k: v
-            for k, v in params.items()
-            # Permit custom and required fields, and fields with non-empty values
-            if k not in fields or fields[k].is_required() or v not in self._EMPTY_VALUES
-        }
-
-        # Make sure params has "q" for query instead of "query" or "search_query"
-        query = params.get("query") or params.get("search_query")
-        if query is not None and "q" not in params:
-            params["q"] = query
-        params.pop("query", None)
-        params.pop("search_query", None)
-
-        # If "count" was not explicitly provided, use n_results
-        # (only when the schema actually supports a "count" field)
-        if "count" in self.args_schema.model_fields:
-            if "count" not in params and self.n_results is not None:
-                params["count"] = self.n_results
-
-        # If "country" was not explicitly provided, but self.country is set, use it
-        # (only when the schema actually supports a "country" field)
-        if "country" in self.args_schema.model_fields:
-            if "country" not in params and self.country is not None:
-                params["country"] = self.country
-
-        return params

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_image_tool.py

@@ -1,42 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.schemas import (
-    ImageSearchHeaders,
-    ImageSearchParams,
-)
-
-
-class BraveImageSearchTool(BraveSearchToolBase):
-    """A tool that performs image searches using the Brave Search API."""
-
-    name: str = "Brave Image Search"
-    args_schema: type[BaseModel] = ImageSearchParams
-    header_schema: type[BaseModel] = ImageSearchHeaders
-
-    description: str = (
-        "A tool that performs image searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/images/search"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
-        results = response.get("results", [])
-        return [
-            {
-                "title": result.get("title"),
-                "url": result.get("properties", {}).get("url"),
-                "dimensions": f"{w}x{h}"
-                if (w := result.get("properties", {}).get("width"))
-                and (h := result.get("properties", {}).get("height"))
-                else None,
-            }
-            for result in results
-        ]

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_llm_context_tool.py

@@ -1,32 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.response_types import LLMContext
-from crewai_tools.tools.brave_search_tool.schemas import (
-    LLMContextHeaders,
-    LLMContextParams,
-)
-
-
-class BraveLLMContextTool(BraveSearchToolBase):
-    """A tool that retrieves context for LLM usage from the Brave Search API."""
-
-    name: str = "Brave LLM Context"
-    args_schema: type[BaseModel] = LLMContextParams
-    header_schema: type[BaseModel] = LLMContextHeaders
-
-    description: str = (
-        "A tool that retrieves context for LLM usage from the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/llm/context"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: LLMContext.Response) -> LLMContext.Response:
-        """The LLM Context response schema is fairly simple. Return as is."""
-        return response

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_local_pois_tool.py

@@ -1,109 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.response_types import LocalPOIs
-from crewai_tools.tools.brave_search_tool.schemas import (
-    LocalPOIsDescriptionHeaders,
-    LocalPOIsDescriptionParams,
-    LocalPOIsHeaders,
-    LocalPOIsParams,
-)
-
-
-DayOpeningHours = LocalPOIs.DayOpeningHours
-OpeningHours = LocalPOIs.OpeningHours
-LocationResult = LocalPOIs.LocationResult
-LocalPOIsResponse = LocalPOIs.Response
-
-
-def _flatten_slots(slots: list[DayOpeningHours]) -> list[dict[str, str]]:
-    """Convert a list of DayOpeningHours dicts into simplified entries."""
-    return [
-        {
-            "day": slot["full_name"].lower(),
-            "opens": slot["opens"],
-            "closes": slot["closes"],
-        }
-        for slot in slots
-    ]
-
-
-def _simplify_opening_hours(result: LocationResult) -> list[dict[str, str]] | None:
-    """Collapse opening_hours into a flat list of {day, opens, closes} dicts."""
-    hours = result.get("opening_hours")
-    if not hours:
-        return None
-
-    entries: list[dict[str, str]] = []
-
-    current = hours.get("current_day")
-    if current:
-        entries.extend(_flatten_slots(current))
-
-    days = hours.get("days")
-    if days:
-        for day_slots in days:
-            entries.extend(_flatten_slots(day_slots))
-
-    return entries or None
-
-
-class BraveLocalPOIsTool(BraveSearchToolBase):
-    """A tool that retrieves local POIs using the Brave Search API."""
-
-    name: str = "Brave Local POIs"
-    args_schema: type[BaseModel] = LocalPOIsParams
-    header_schema: type[BaseModel] = LocalPOIsHeaders
-    description: str = (
-        "A tool that retrieves local POIs using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-    search_url: str = "https://api.search.brave.com/res/v1/local/pois"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: LocalPOIsResponse) -> list[dict[str, Any]]:
-        results = response.get("results", [])
-        return [
-            {
-                "title": result.get("title"),
-                "url": result.get("url"),
-                "description": result.get("description"),
-                "address": result.get("postal_address", {}).get("displayAddress"),
-                "contact": result.get("contact", {}).get("telephone")
-                or result.get("contact", {}).get("email")
-                or None,
-                "opening_hours": _simplify_opening_hours(result),
-            }
-            for result in results
-        ]
-
-
-class BraveLocalPOIsDescriptionTool(BraveSearchToolBase):
-    """A tool that retrieves AI-generated descriptions for local POIs using the Brave Search API."""
-
-    name: str = "Brave Local POI Descriptions"
-    args_schema: type[BaseModel] = LocalPOIsDescriptionParams
-    header_schema: type[BaseModel] = LocalPOIsDescriptionHeaders
-    description: str = (
-        "A tool that retrieves AI-generated descriptions for local POIs using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-    search_url: str = "https://api.search.brave.com/res/v1/local/descriptions"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: LocalPOIsResponse) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
-        results = response.get("results", [])
-        return [
-            {
-                "id": result.get("id"),
-                "description": result.get("description"),
-            }
-            for result in results
-        ]

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_news_tool.py

@@ -1,39 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.schemas import (
-    NewsSearchHeaders,
-    NewsSearchParams,
-)
-
-
-class BraveNewsSearchTool(BraveSearchToolBase):
-    """A tool that performs news searches using the Brave Search API."""
-
-    name: str = "Brave News Search"
-    args_schema: type[BaseModel] = NewsSearchParams
-    header_schema: type[BaseModel] = NewsSearchHeaders
-
-    description: str = (
-        "A tool that performs news searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/news/search"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
-        results = response.get("results", [])
-        return [
-            {
-                "url": result.get("url"),
-                "title": result.get("title"),
-                "description": result.get("description"),
-            }
-            for result in results
-        ]

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

@@ -10,13 +10,17 @@ from pydantic import BaseModel, Field
 from pydantic.types import StringConstraints
 import requests
 
-from crewai_tools.tools.brave_search_tool.schemas import WebSearchParams
-from crewai_tools.tools.brave_search_tool.base import _save_results_to_file
-
 
 load_dotenv()
 
 
+def _save_results_to_file(content: str) -> None:
+    """Saves the search results to a file."""
+    filename = f"search_results_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt"
+    with open(filename, "w") as file:
+        file.write(content)
+
+
 FreshnessPreset = Literal["pd", "pw", "pm", "py"]
 FreshnessRange = Annotated[
     str, StringConstraints(pattern=r"^\d{4}-\d{2}-\d{2}to\d{4}-\d{2}-\d{2}$")
@@ -25,6 +29,51 @@ Freshness = FreshnessPreset | FreshnessRange
 SafeSearch = Literal["off", "moderate", "strict"]
 
 
+class BraveSearchToolSchema(BaseModel):
+    """Input for BraveSearchTool"""
+
+    query: str = Field(..., description="Search query to perform")
+    country: str | None = Field(
+        default=None,
+        description="Country code for geo-targeting (e.g., 'US', 'BR').",
+    )
+    search_language: str | None = Field(
+        default=None,
+        description="Language code for the search results (e.g., 'en', 'es').",
+    )
+    count: int | None = Field(
+        default=None,
+        description="The maximum number of results to return. Actual number may be less.",
+    )
+    offset: int | None = Field(
+        default=None, description="Skip the first N result sets/pages. Max is 9."
+    )
+    safesearch: SafeSearch | None = Field(
+        default=None,
+        description="Filter out explicit content. Options: off/moderate/strict",
+    )
+    spellcheck: bool | None = Field(
+        default=None,
+        description="Attempt to correct spelling errors in the search query.",
+    )
+    freshness: Freshness | None = Field(
+        default=None,
+        description="Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
+    )
+    text_decorations: bool | None = Field(
+        default=None,
+        description="Include markup to highlight search terms in the results.",
+    )
+    extra_snippets: bool | None = Field(
+        default=None,
+        description="Include up to 5 text snippets for each page if possible.",
+    )
+    operators: bool | None = Field(
+        default=None,
+        description="Whether to apply search operators (e.g., site:example.com).",
+    )
+
+
 # TODO: Extend support to additional endpoints (e.g., /images, /news, etc.)
 class BraveSearchTool(BaseTool):
     """A tool that performs web searches using the Brave Search API."""
@@ -34,7 +83,7 @@ class BraveSearchTool(BaseTool):
         "A tool that performs web searches using the Brave Search API. "
         "Results are returned as structured JSON data."
     )
-    args_schema: type[BaseModel] = WebSearchParams
+    args_schema: type[BaseModel] = BraveSearchToolSchema
     search_url: str = "https://api.search.brave.com/res/v1/web/search"
     n_results: int = 10
     save_file: bool = False
@@ -71,8 +120,8 @@ class BraveSearchTool(BaseTool):
 
         # Construct and send the request
         try:
-            # Fallback to "query" or "search_query" for backwards compatibility
-            query = kwargs.get("q") or kwargs.get("query") or kwargs.get("search_query")
+            # Maintain both "search_query" and "query" for backwards compatibility
+            query = kwargs.get("search_query") or kwargs.get("query")
             if not query:
                 raise ValueError("Query is required")
 
@@ -81,11 +130,8 @@ class BraveSearchTool(BaseTool):
             if country := kwargs.get("country"):
                 payload["country"] = country
 
-            # Fallback to "search_language" for backwards compatibility
-            if search_lang := kwargs.get("search_lang") or kwargs.get(
-                "search_language"
-            ):
-                payload["search_lang"] = search_lang
+            if search_language := kwargs.get("search_language"):
+                payload["search_language"] = search_language
 
             # Fallback to deprecated n_results parameter if no count is provided
             count = kwargs.get("count")

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_video_tool.py

@@ -1,39 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.schemas import (
-    VideoSearchHeaders,
-    VideoSearchParams,
-)
-
-
-class BraveVideoSearchTool(BraveSearchToolBase):
-    """A tool that performs video searches using the Brave Search API."""
-
-    name: str = "Brave Video Search"
-    args_schema: type[BaseModel] = VideoSearchParams
-    header_schema: type[BaseModel] = VideoSearchHeaders
-
-    description: str = (
-        "A tool that performs video searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/videos/search"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
-        results = response.get("results", [])
-        return [
-            {
-                "url": result.get("url"),
-                "title": result.get("title"),
-                "description": result.get("description"),
-            }
-            for result in results
-        ]

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_web_tool.py

@@ -1,45 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.schemas import (
-    WebSearchHeaders,
-    WebSearchParams,
-)
-
-
-class BraveWebSearchTool(BraveSearchToolBase):
-    """A tool that performs web searches using the Brave Search API."""
-
-    name: str = "Brave Web Search"
-    args_schema: type[BaseModel] = WebSearchParams
-    header_schema: type[BaseModel] = WebSearchHeaders
-
-    description: str = (
-        "A tool that performs web searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/web/search"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        results = response.get("web", {}).get("results", [])
-        refined = []
-        for result in results:
-            snippets = result.get("extra_snippets") or []
-            if not snippets:
-                desc = result.get("description")
-                if desc:
-                    snippets = [desc]
-            refined.append(
-                {
-                    "url": result.get("url"),
-                    "title": result.get("title"),
-                    "snippets": snippets,
-                }
-            )
-        return refined

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/response_types.py

@@ -1,67 +0,0 @@
-from __future__ import annotations
-
-from typing import Literal, TypedDict
-
-
-class LocalPOIs:
-    class PostalAddress(TypedDict, total=False):
-        type: Literal["PostalAddress"]
-        country: str
-        postalCode: str
-        streetAddress: str
-        addressRegion: str
-        addressLocality: str
-        displayAddress: str
-
-    class DayOpeningHours(TypedDict):
-        abbr_name: str
-        full_name: str
-        opens: str
-        closes: str
-
-    class OpeningHours(TypedDict, total=False):
-        current_day: list[LocalPOIs.DayOpeningHours]
-        days: list[list[LocalPOIs.DayOpeningHours]]
-
-    class LocationResult(TypedDict, total=False):
-        provider_url: str
-        title: str
-        url: str
-        id: str | None
-        opening_hours: LocalPOIs.OpeningHours | None
-        postal_address: LocalPOIs.PostalAddress | None
-
-    class Response(TypedDict, total=False):
-        type: Literal["local_pois"]
-        results: list[LocalPOIs.LocationResult]
-
-
-class LLMContext:
-    class LLMContextItem(TypedDict, total=False):
-        snippets: list[str]
-        title: str
-        url: str
-
-    class LLMContextMapItem(TypedDict, total=False):
-        name: str
-        snippets: list[str]
-        title: str
-        url: str
-
-    class LLMContextPOIItem(TypedDict, total=False):
-        name: str
-        snippets: list[str]
-        title: str
-        url: str
-
-    class Grounding(TypedDict, total=False):
-        generic: list[LLMContext.LLMContextItem]
-        poi: LLMContext.LLMContextPOIItem
-        map: list[LLMContext.LLMContextMapItem]
-
-    class Sources(TypedDict, total=False):
-        pass
-
-    class Response(TypedDict, total=False):
-        grounding: LLMContext.Grounding
-        sources: LLMContext.Sources

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/schemas.py

@@ -1,525 +0,0 @@
-from typing import Annotated, Literal
-
-from pydantic import BaseModel, Field
-from pydantic.types import StringConstraints
-
-
-# Common types
-Units = Literal["metric", "imperial"]
-SafeSearch = Literal["off", "moderate", "strict"]
-Freshness = (
-    Literal["pd", "pw", "pm", "py"]
-    | Annotated[
-        str, StringConstraints(pattern=r"^\d{4}-\d{2}-\d{2}to\d{4}-\d{2}-\d{2}$")
-    ]
-)
-ResultFilter = list[
-    Literal[
-        "discussions",
-        "faq",
-        "infobox",
-        "news",
-        "query",
-        "summarizer",
-        "videos",
-        "web",
-        "locations",
-    ]
-]
-
-
-class LLMContextParams(BaseModel):
-    """Parameters for Brave LLM Context endpoint."""
-
-    q: str = Field(
-        description="Search query to perform",
-        min_length=1,
-        max_length=400,
-    )
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-        pattern=r"^[A-Z]{2}$",
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return. Actual number may be less.",
-        ge=1,
-        le=50,
-    )
-    maximum_number_of_urls: int | None = Field(
-        default=None,
-        description="The maximum number of URLs to include in the context.",
-        ge=1,
-        le=50,
-    )
-    maximum_number_of_tokens: int | None = Field(
-        default=None,
-        description="The approximate maximum number of tokens to include in the context.",
-        ge=1,
-        le=32768,
-    )
-    maximum_number_of_snippets: int | None = Field(
-        default=None,
-        description="The maximum number of different snippets to include in the context.",
-        ge=1,
-        le=100,
-    )
-    context_threshold_mode: (
-        Literal["disabled", "strict", "lenient", "balanced"] | None
-    ) = Field(
-        default=None,
-        description="The mode to use for the context thresholding.",
-    )
-    maximum_number_of_tokens_per_url: int | None = Field(
-        default=None,
-        description="The maximum number of tokens to include for each URL in the context.",
-        ge=1,
-        le=8192,
-    )
-    maximum_number_of_snippets_per_url: int | None = Field(
-        default=None,
-        description="The maximum number of snippets to include per URL.",
-        ge=1,
-        le=100,
-    )
-    goggles: str | list[str] | None = Field(
-        default=None,
-        description="Goggles act as a custom re-ranking mechanism. Goggle source or URLs.",
-    )
-    enable_local: bool | None = Field(
-        default=None,
-        description="Whether to enable local recall. Not setting this value means auto-detect and uses local recall if any of the localization headers are provided.",
-    )
-
-
-class WebSearchParams(BaseModel):
-    """Parameters for Brave Web Search endpoint."""
-
-    q: str = Field(
-        description="Search query to perform",
-        min_length=1,
-        max_length=400,
-    )
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-        pattern=r"^[A-Z]{2}$",
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    ui_lang: str | None = Field(
-        default=None,
-        description="Language code for the user interface (e.g., 'en-US', 'es-AR').",
-        pattern=r"^[a-z]{2}-[A-Z]{2}$",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return. Actual number may be less.",
-        ge=1,
-        le=20,
-    )
-    offset: int | None = Field(
-        default=None,
-        description="Skip the first N result sets/pages. Max is 9.",
-        ge=0,
-        le=9,
-    )
-    safesearch: Literal["off", "moderate", "strict"] | None = Field(
-        default=None,
-        description="Filter out explicit content. Options: off/moderate/strict",
-    )
-    spellcheck: bool | None = Field(
-        default=None,
-        description="Attempt to correct spelling errors in the search query.",
-    )
-    freshness: Freshness | None = Field(
-        default=None,
-        description="Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
-    )
-    text_decorations: bool | None = Field(
-        default=None,
-        description="Include markup to highlight search terms in the results.",
-    )
-    extra_snippets: bool | None = Field(
-        default=None,
-        description="Include up to 5 text snippets for each page if possible.",
-    )
-    result_filter: ResultFilter | None = Field(
-        default=None,
-        description="Filter the results by type. Options: discussions/faq/infobox/news/query/summarizer/videos/web/locations. Note: The `count` parameter is applied only to the `web` results.",
-    )
-    units: Units | None = Field(
-        default=None,
-        description="The units to use for the results. Options: metric/imperial",
-    )
-    goggles: str | list[str] | None = Field(
-        default=None,
-        description="Goggles act as a custom re-ranking mechanism. Goggle source or URLs.",
-    )
-    summary: bool | None = Field(
-        default=None,
-        description="Whether to generate a summarizer ID for the results.",
-    )
-    enable_rich_callback: bool | None = Field(
-        default=None,
-        description="Whether to enable rich callbacks for the results. Requires Pro level subscription.",
-    )
-    include_fetch_metadata: bool | None = Field(
-        default=None,
-        description="Whether to include fetch metadata (e.g., last fetch time) in the results.",
-    )
-    operators: bool | None = Field(
-        default=None,
-        description="Whether to apply search operators (e.g., site:example.com).",
-    )
-
-
-class LocalPOIsParams(BaseModel):
-    """Parameters for Brave Local POIs endpoint."""
-
-    ids: list[str] = Field(
-        description="List of POI IDs to retrieve. Maximum of 20. IDs are valid for 8 hours.",
-        min_length=1,
-        max_length=20,
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    ui_lang: str | None = Field(
-        default=None,
-        description="Language code for the user interface (e.g., 'en-US', 'es-AR').",
-        pattern=r"^[a-z]{2}-[A-Z]{2}$",
-    )
-    units: Units | None = Field(
-        default=None,
-        description="The units to use for the results. Options: metric/imperial",
-    )
-
-
-class LocalPOIsDescriptionParams(BaseModel):
-    """Parameters for Brave Local POI Descriptions endpoint."""
-
-    ids: list[str] = Field(
-        description="List of POI IDs to retrieve. Maximum of 20. IDs are valid for 8 hours.",
-        min_length=1,
-        max_length=20,
-    )
-
-
-class ImageSearchParams(BaseModel):
-    """Parameters for Brave Image Search endpoint."""
-
-    q: str = Field(
-        description="Search query to perform",
-        min_length=1,
-        max_length=400,
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-        pattern=r"^[A-Z]{2}$",
-    )
-    safesearch: Literal["off", "strict"] | None = Field(
-        default=None,
-        description="Filter out explicit content. Default is strict.",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return.",
-        ge=1,
-        le=200,
-    )
-    spellcheck: bool | None = Field(
-        default=None,
-        description="Attempt to correct spelling errors in the search query.",
-    )
-
-
-class VideoSearchParams(BaseModel):
-    """Parameters for Brave Video Search endpoint."""
-
-    q: str = Field(
-        description="Search query to perform",
-        min_length=1,
-        max_length=400,
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    ui_lang: str | None = Field(
-        default=None,
-        description="Language code for the user interface (e.g., 'en-US', 'es-AR').",
-        pattern=r"^[a-z]{2}-[A-Z]{2}$",
-    )
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-        pattern=r"^[A-Z]{2}$",
-    )
-    safesearch: SafeSearch | None = Field(
-        default=None,
-        description="Filter out explicit content. Options: off/moderate/strict",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return.",
-        ge=1,
-        le=50,
-    )
-    offset: int | None = Field(
-        default=None,
-        description="Skip the first N result sets/pages. Max is 9.",
-        ge=0,
-        le=9,
-    )
-    spellcheck: bool | None = Field(
-        default=None,
-        description="Attempt to correct spelling errors in the search query.",
-    )
-    freshness: Freshness | None = Field(
-        default=None,
-        description="Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
-    )
-    include_fetch_metadata: bool | None = Field(
-        default=None,
-        description="Whether to include fetch metadata (e.g., last fetch time) in the results.",
-    )
-    operators: bool | None = Field(
-        default=None,
-        description="Whether to apply search operators (e.g., site:example.com).",
-    )
-
-
-class NewsSearchParams(BaseModel):
-    """Parameters for Brave News Search endpoint."""
-
-    q: str = Field(
-        description="Search query to perform",
-        min_length=1,
-        max_length=400,
-    )
-    search_lang: str | None = Field(
-        default=None,
-        description="Language code for the search results (e.g., 'en', 'es').",
-        pattern=r"^[a-z]{2}$",
-    )
-    ui_lang: str | None = Field(
-        default=None,
-        description="Language code for the user interface (e.g., 'en-US', 'es-AR').",
-        pattern=r"^[a-z]{2}-[A-Z]{2}$",
-    )
-    country: str | None = Field(
-        default=None,
-        description="Country code for geo-targeting (e.g., 'US', 'BR').",
-        pattern=r"^[A-Z]{2}$",
-    )
-    safesearch: Literal["off", "moderate", "strict"] | None = Field(
-        default=None,
-        description="Filter out explicit content. Options: off/moderate/strict",
-    )
-    count: int | None = Field(
-        default=None,
-        description="The maximum number of results to return.",
-        ge=1,
-        le=50,
-    )
-    offset: int | None = Field(
-        default=None,
-        description="Skip the first N result sets/pages. Max is 9.",
-        ge=0,
-        le=9,
-    )
-    spellcheck: bool | None = Field(
-        default=None,
-        description="Attempt to correct spelling errors in the search query.",
-    )
-    freshness: Freshness | None = Field(
-        default=None,
-        description="Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
-    )
-    extra_snippets: bool | None = Field(
-        default=None,
-        description="Include up to 5 text snippets for each page if possible.",
-    )
-    goggles: str | list[str] | None = Field(
-        default=None,
-        description="Goggles act as a custom re-ranking mechanism. Goggle source or URLs.",
-    )
-    include_fetch_metadata: bool | None = Field(
-        default=None,
-        description="Whether to include fetch metadata in the results.",
-    )
-    operators: bool | None = Field(
-        default=None,
-        description="Whether to apply search operators (e.g., site:example.com).",
-    )
-
-
-class BaseSearchHeaders(BaseModel):
-    """Common headers for Brave Search endpoints."""
-
-    x_subscription_token: str = Field(
-        alias="x-subscription-token",
-        description="API key for Brave Search",
-    )
-    api_version: str | None = Field(
-        alias="api-version",
-        default=None,
-        description="API version to use. Default is latest available.",
-        pattern=r"^\d{4}-\d{2}-\d{2}$",  # YYYY-MM-DD
-    )
-    accept: Literal["application/json"] | Literal["*/*"] | None = Field(
-        default=None,
-        description="Accept header for the request.",
-    )
-    cache_control: Literal["no-cache"] | None = Field(
-        alias="cache-control",
-        default=None,
-        description="Cache control header for the request.",
-    )
-    user_agent: str | None = Field(
-        alias="user-agent",
-        default=None,
-        description="User agent for the request.",
-    )
-
-
-class LLMContextHeaders(BaseSearchHeaders):
-    """Headers for Brave LLM Context endpoint."""
-
-    x_loc_lat: float | None = Field(
-        alias="x-loc-lat",
-        default=None,
-        description="Latitude of the user's location.",
-        ge=-90.0,
-        le=90.0,
-    )
-    x_loc_long: float | None = Field(
-        alias="x-loc-long",
-        default=None,
-        description="Longitude of the user's location.",
-        ge=-180.0,
-        le=180.0,
-    )
-    x_loc_city: str | None = Field(
-        alias="x-loc-city",
-        default=None,
-        description="City of the user's location.",
-    )
-    x_loc_state: str | None = Field(
-        alias="x-loc-state",
-        default=None,
-        description="State of the user's location.",
-    )
-    x_loc_state_name: str | None = Field(
-        alias="x-loc-state-name",
-        default=None,
-        description="Name of the state of the user's location.",
-    )
-    x_loc_country: str | None = Field(
-        alias="x-loc-country",
-        default=None,
-        description="The ISO 3166-1 alpha-2 country code of the user's location.",
-    )
-
-
-class LocalPOIsHeaders(BaseSearchHeaders):
-    """Headers for Brave Local POIs endpoint."""
-
-    x_loc_lat: float | None = Field(
-        alias="x-loc-lat",
-        default=None,
-        description="Latitude of the user's location.",
-        ge=-90.0,
-        le=90.0,
-    )
-    x_loc_long: float | None = Field(
-        alias="x-loc-long",
-        default=None,
-        description="Longitude of the user's location.",
-        ge=-180.0,
-        le=180.0,
-    )
-
-
-class LocalPOIsDescriptionHeaders(BaseSearchHeaders):
-    """Headers for Brave Local POI Descriptions endpoint."""
-
-
-class VideoSearchHeaders(BaseSearchHeaders):
-    """Headers for Brave Video Search endpoint."""
-
-
-class ImageSearchHeaders(BaseSearchHeaders):
-    """Headers for Brave Image Search endpoint."""
-
-
-class NewsSearchHeaders(BaseSearchHeaders):
-    """Headers for Brave News Search endpoint."""
-
-
-class WebSearchHeaders(BaseSearchHeaders):
-    """Headers for Brave Web Search endpoint."""
-
-    x_loc_lat: float | None = Field(
-        alias="x-loc-lat",
-        default=None,
-        description="Latitude of the user's location.",
-        ge=-90.0,
-        le=90.0,
-    )
-    x_loc_long: float | None = Field(
-        alias="x-loc-long",
-        default=None,
-        description="Longitude of the user's location.",
-        ge=-180.0,
-        le=180.0,
-    )
-    x_loc_timezone: str | None = Field(
-        alias="x-loc-timezone",
-        default=None,
-        description="Timezone of the user's location.",
-    )
-    x_loc_city: str | None = Field(
-        alias="x-loc-city",
-        default=None,
-        description="City of the user's location.",
-    )
-    x_loc_state: str | None = Field(
-        alias="x-loc-state",
-        default=None,
-        description="State of the user's location.",
-    )
-    x_loc_state_name: str | None = Field(
-        alias="x-loc-state-name",
-        default=None,
-        description="Name of the state of the user's location.",
-    )
-    x_loc_country: str | None = Field(
-        alias="x-loc-country",
-        default=None,
-        description="The ISO 3166-1 alpha-2 country code of the user's location.",
-    )
-    x_loc_postal_code: str | None = Field(
-        alias="x-loc-postal-code",
-        default=None,
-        description="The postal code of the user's location.",
-    )

lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/README.md

@@ -1,13 +1,27 @@
 # CodeInterpreterTool
 
 ## Description
-This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a sandboxed environment, so it is safe to run any code.
+This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a Docker container for secure isolation.
 
-It is incredible useful since it allows the Agent to generate code, run it in the same environment, get the result and use it to make decisions.
+It is incredibly useful since it allows the Agent to generate code, run it in an isolated environment, get the result and use it to make decisions.
+
+## ⚠️ Security Requirements
+
+**Docker is REQUIRED** for safe code execution. The tool will refuse to execute code without Docker to prevent security vulnerabilities.
+
+### Why Docker is Required
+
+Previous versions included a "restricted sandbox" fallback when Docker was unavailable. This has been **removed** due to critical security vulnerabilities:
+
+- The Python-based sandbox could be escaped via object introspection
+- Attackers could recover the original `__import__` function and access any module
+- This allowed arbitrary command execution on the host system
+
+**Docker provides real process isolation** and is the only secure way to execute untrusted code.
 
 ## Requirements
 
-- Docker
+- **Docker (REQUIRED)** - Install from [docker.com](https://docs.docker.com/get-docker/)
 
 ## Installation
 Install the crewai_tools package
@@ -17,7 +31,9 @@ pip install 'crewai[tools]'
 
 ## Example
 
-Remember that when using this tool, the code must be generated by the Agent itself. The code must be a Python3 code. And it will take some time for the first time to run because it needs to build the Docker image.
+Remember that when using this tool, the code must be generated by the Agent itself. The code must be Python3 code. It will take some time the first time to run because it needs to build the Docker image.
+
+### Basic Usage (Docker Container - Recommended)
 
 ```python
 from crewai_tools import CodeInterpreterTool
@@ -28,7 +44,9 @@ Agent(
 )
 ```
 
-Or if you need to pass your own Dockerfile just do this
+### Custom Dockerfile
+
+If you need to pass your own Dockerfile:
 
 ```python
 from crewai_tools import CodeInterpreterTool
@@ -39,15 +57,39 @@ Agent(
 )
 ```
 
-If it is difficult to connect to docker daemon automatically (especially for macOS users), you can do this to setup docker host manually
+### Manual Docker Host Configuration
+
+If it is difficult to connect to the Docker daemon automatically (especially for macOS users), you can set up the Docker host manually:
 
 ```python 
 from crewai_tools import CodeInterpreterTool
 
 Agent(
     ...
-    tools=[CodeInterpreterTool(user_docker_base_url="<Docker Host Base Url>",
-        user_dockerfile_path="<Dockerfile_path>")],
+    tools=[CodeInterpreterTool(
+        user_docker_base_url="<Docker Host Base Url>",
+        user_dockerfile_path="<Dockerfile_path>"
+    )],
 )
-
 ```
+
+### Unsafe Mode (NOT RECOMMENDED)
+
+If you absolutely cannot use Docker and **fully trust the code source**, you can use unsafe mode:
+
+```python
+from crewai_tools import CodeInterpreterTool
+
+# WARNING: Only use with fully trusted code!
+Agent(
+    ...
+    tools=[CodeInterpreterTool(unsafe_mode=True)],
+)
+```
+
+**⚠️ SECURITY WARNING:** `unsafe_mode=True` executes code directly on the host without any isolation. Only use this if:
+- You completely trust the code being executed
+- You understand the security risks
+- You cannot install Docker in your environment
+
+For production use, **always use Docker** (the default mode).

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

@@ -50,11 +50,16 @@ class CodeInterpreterSchema(BaseModel):
 
 
 class SandboxPython:
-    """A restricted Python execution environment for running code safely.
+    """INSECURE: A restricted Python execution environment with known vulnerabilities.
 
-    This class provides methods to safely execute Python code by restricting access to
-    potentially dangerous modules and built-in functions. It creates a sandboxed
-    environment where harmful operations are blocked.
+    WARNING: This class does NOT provide real security isolation and is vulnerable to
+    sandbox escape attacks via Python object introspection. Attackers can recover the
+    original __import__ function and bypass all restrictions.
+
+    DO NOT USE for untrusted code execution. Use Docker containers instead.
+
+    This class attempts to restrict access to dangerous modules and built-in functions
+    but provides no real security boundary against a motivated attacker.
     """
 
     BLOCKED_MODULES: ClassVar[set[str]] = {
@@ -299,8 +304,8 @@ class CodeInterpreterTool(BaseTool):
     def run_code_safety(self, code: str, libraries_used: list[str]) -> str:
         """Runs code in the safest available environment.
 
-        Attempts to run code in Docker if available, falls back to a restricted
-        sandbox if Docker is not available.
+        Requires Docker to be available for secure code execution. Fails closed
+        if Docker is not available to prevent sandbox escape vulnerabilities.
 
         Args:
             code: The Python code to execute as a string.
@@ -308,10 +313,24 @@ class CodeInterpreterTool(BaseTool):
 
         Returns:
             The output of the executed code as a string.
+
+        Raises:
+            RuntimeError: If Docker is not available, as the restricted sandbox
+                         is vulnerable to escape attacks and should not be used
+                         for untrusted code execution.
         """
         if self._check_docker_available():
             return self.run_code_in_docker(code, libraries_used)
-        return self.run_code_in_restricted_sandbox(code)
+        
+        error_msg = (
+            "Docker is required for safe code execution but is not available. "
+            "The restricted sandbox fallback has been removed due to security vulnerabilities "
+            "that allow sandbox escape via Python object introspection. "
+            "Please install Docker (https://docs.docker.com/get-docker/) or use unsafe_mode=True "
+            "if you trust the code source and understand the security risks."
+        )
+        Printer.print(error_msg, color="bold_red")
+        raise RuntimeError(error_msg)
 
     def run_code_in_docker(self, code: str, libraries_used: list[str]) -> str:
         """Runs Python code in a Docker container for safe isolation.
@@ -342,10 +361,19 @@ class CodeInterpreterTool(BaseTool):
 
     @staticmethod
     def run_code_in_restricted_sandbox(code: str) -> str:
-        """Runs Python code in a restricted sandbox environment.
+        """DEPRECATED AND INSECURE: Runs Python code in a restricted sandbox environment.
 
-        Executes the code with restricted access to potentially dangerous modules and
-        built-in functions for basic safety when Docker is not available.
+        WARNING: This method is vulnerable to sandbox escape attacks via Python object
+        introspection and should NOT be used for untrusted code execution. It has been
+        deprecated and is only kept for backward compatibility with trusted code.
+
+        The "restricted" environment can be bypassed by attackers who can:
+        - Use object graph introspection to recover the original __import__ function
+        - Access any Python module including os, subprocess, sys, etc.
+        - Execute arbitrary commands on the host system
+
+        Use run_code_in_docker() for secure code execution, or run_code_unsafe()
+        if you explicitly acknowledge the security risks.
 
         Args:
             code: The Python code to execute as a string.
@@ -354,7 +382,10 @@ class CodeInterpreterTool(BaseTool):
             The value of the 'result' variable from the executed code,
             or an error message if execution failed.
         """
-        Printer.print("Running code in restricted sandbox", color="yellow")
+        Printer.print(
+            "WARNING: Running code in INSECURE restricted sandbox (vulnerable to escape attacks)",
+            color="bold_red"
+        )
         exec_locals: dict[str, Any] = {}
         try:
             SandboxPython.exec(code=code, locals_=exec_locals)

lib/crewai-tools/tests/tools/brave_search_tool_test.py

@@ -1,777 +1,80 @@
-import os
-from unittest.mock import MagicMock, patch
+import json
+from unittest.mock import patch
 
 import pytest
-import requests as requests_lib
 
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.brave_web_tool import BraveWebSearchTool
-from crewai_tools.tools.brave_search_tool.brave_image_tool import BraveImageSearchTool
-from crewai_tools.tools.brave_search_tool.brave_news_tool import BraveNewsSearchTool
-from crewai_tools.tools.brave_search_tool.brave_video_tool import BraveVideoSearchTool
-from crewai_tools.tools.brave_search_tool.brave_llm_context_tool import (
-    BraveLLMContextTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_local_pois_tool import (
-    BraveLocalPOIsTool,
-    BraveLocalPOIsDescriptionTool,
-)
-from crewai_tools.tools.brave_search_tool.schemas import (
-    WebSearchParams,
-    WebSearchHeaders,
-    ImageSearchParams,
-    ImageSearchHeaders,
-    NewsSearchParams,
-    NewsSearchHeaders,
-    VideoSearchParams,
-    VideoSearchHeaders,
-    LLMContextParams,
-    LLMContextHeaders,
-    LocalPOIsParams,
-    LocalPOIsHeaders,
-    LocalPOIsDescriptionParams,
-    LocalPOIsDescriptionHeaders,
-)
-
-
-def _mock_response(
-    status_code: int = 200,
-    json_data: dict | None = None,
-    headers: dict | None = None,
-    text: str = "",
-) -> MagicMock:
-    """Build a ``requests.Response``-like mock with the attributes used by ``_make_request``."""
-    resp = MagicMock(spec=requests_lib.Response)
-    resp.status_code = status_code
-    resp.ok = 200 <= status_code < 400
-    resp.url = "https://api.search.brave.com/res/v1/web/search?q=test"
-    resp.text = text or (str(json_data) if json_data else "")
-    resp.headers = headers or {}
-    resp.json.return_value = json_data if json_data is not None else {}
-    return resp
-
-
-# Fixtures
-
-
-@pytest.fixture(autouse=True)
-def _brave_env_and_rate_limit():
-    """Set BRAVE_API_KEY for every test. Rate limiting is per-instance (each tool starts with a fresh clock)."""
-    with patch.dict(os.environ, {"BRAVE_API_KEY": "test-api-key"}):
-        yield
+from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
 
 
 @pytest.fixture
-def web_tool():
-    return BraveWebSearchTool()
+def brave_tool():
+    return BraveSearchTool(n_results=2)
 
 
-@pytest.fixture
-def image_tool():
-    return BraveImageSearchTool()
-
-
-@pytest.fixture
-def news_tool():
-    return BraveNewsSearchTool()
-
-
-@pytest.fixture
-def video_tool():
-    return BraveVideoSearchTool()
-
-
-# Initialization
-
-ALL_TOOL_CLASSES = [
-    BraveWebSearchTool,
-    BraveImageSearchTool,
-    BraveNewsSearchTool,
-    BraveVideoSearchTool,
-    BraveLLMContextTool,
-    BraveLocalPOIsTool,
-    BraveLocalPOIsDescriptionTool,
-]
-
-
-@pytest.mark.parametrize("tool_cls", ALL_TOOL_CLASSES)
-def test_instantiation_with_env_var(tool_cls):
-    """Each tool can be created when BRAVE_API_KEY is in the environment."""
-    tool = tool_cls()
-    assert tool.api_key == "test-api-key"
-
-
-@pytest.mark.parametrize("tool_cls", ALL_TOOL_CLASSES)
-def test_instantiation_with_explicit_key(tool_cls):
-    """An explicit api_key takes precedence over the environment."""
-    tool = tool_cls(api_key="explicit-key")
-    assert tool.api_key == "explicit-key"
-
-
-def test_missing_api_key_raises():
-    with patch.dict(os.environ, {}, clear=True):
-        with pytest.raises(ValueError, match="BRAVE_API_KEY"):
-            BraveWebSearchTool()
-
-
-def test_default_attributes():
-    tool = BraveWebSearchTool()
-    assert tool.save_file is False
+def test_brave_tool_initialization():
+    tool = BraveSearchTool()
     assert tool.n_results == 10
-    assert tool._timeout == 30
-    assert tool._requests_per_second == 1.0
-    assert tool.raw is False
+    assert tool.save_file is False
 
 
-def test_custom_constructor_args():
-    tool = BraveWebSearchTool(
-        save_file=True,
-        timeout=60,
-        n_results=5,
-        requests_per_second=0.5,
-        raw=True,
-    )
-    assert tool.save_file is True
-    assert tool._timeout == 60
-    assert tool.n_results == 5
-    assert tool._requests_per_second == 0.5
-    assert tool.raw is True
-
-
-# Headers
-
-
-def test_default_headers():
-    tool = BraveWebSearchTool()
-    assert tool.headers["x-subscription-token"] == "test-api-key"
-    assert tool.headers["accept"] == "application/json"
-
-
-def test_set_headers_merges_and_normalizes():
-    tool = BraveWebSearchTool()
-    tool.set_headers({"Cache-Control": "no-cache"})
-    assert tool.headers["cache-control"] == "no-cache"
-    assert tool.headers["x-subscription-token"] == "test-api-key"
-
-
-def test_set_headers_returns_self_for_chaining():
-    tool = BraveWebSearchTool()
-    assert tool.set_headers({"Cache-Control": "no-cache"}) is tool
-
-
-def test_invalid_header_value_raises():
-    tool = BraveImageSearchTool()
-    with pytest.raises(ValueError, match="Invalid headers"):
-        tool.set_headers({"Accept": "text/xml"})
-
-
-# Endpoint & Schema Wiring
-
-
-@pytest.mark.parametrize(
-    "tool_cls, expected_url, expected_params, expected_headers",
-    [
-        (
-            BraveWebSearchTool,
-            "https://api.search.brave.com/res/v1/web/search",
-            WebSearchParams,
-            WebSearchHeaders,
-        ),
-        (
-            BraveImageSearchTool,
-            "https://api.search.brave.com/res/v1/images/search",
-            ImageSearchParams,
-            ImageSearchHeaders,
-        ),
-        (
-            BraveNewsSearchTool,
-            "https://api.search.brave.com/res/v1/news/search",
-            NewsSearchParams,
-            NewsSearchHeaders,
-        ),
-        (
-            BraveVideoSearchTool,
-            "https://api.search.brave.com/res/v1/videos/search",
-            VideoSearchParams,
-            VideoSearchHeaders,
-        ),
-        (
-            BraveLLMContextTool,
-            "https://api.search.brave.com/res/v1/llm/context",
-            LLMContextParams,
-            LLMContextHeaders,
-        ),
-        (
-            BraveLocalPOIsTool,
-            "https://api.search.brave.com/res/v1/local/pois",
-            LocalPOIsParams,
-            LocalPOIsHeaders,
-        ),
-        (
-            BraveLocalPOIsDescriptionTool,
-            "https://api.search.brave.com/res/v1/local/descriptions",
-            LocalPOIsDescriptionParams,
-            LocalPOIsDescriptionHeaders,
-        ),
-    ],
-)
-def test_tool_wiring(tool_cls, expected_url, expected_params, expected_headers):
-    tool = tool_cls()
-    assert tool.search_url == expected_url
-    assert tool.args_schema is expected_params
-    assert tool.header_schema is expected_headers
-
-
-# Payload Refinement  (e.g., `query` -> `q`, `count` fallback, param pass-through)
-
-
-def test_web_refine_request_payload_passes_all_params(web_tool):
-    params = web_tool._common_payload_refinement(
-        {
-            "query": "test",
-            "country": "US",
-            "search_lang": "en",
-            "count": 5,
-            "offset": 2,
-            "safesearch": "moderate",
-            "freshness": "pw",
-        }
-    )
-    refined_params = web_tool._refine_request_payload(params)
-
-    assert refined_params["q"] == "test"
-    assert "query" not in refined_params
-    assert refined_params["count"] == 5
-    assert refined_params["country"] == "US"
-    assert refined_params["search_lang"] == "en"
-    assert refined_params["offset"] == 2
-    assert refined_params["safesearch"] == "moderate"
-    assert refined_params["freshness"] == "pw"
-
-
-def test_image_refine_request_payload_passes_all_params(image_tool):
-    params = image_tool._common_payload_refinement(
-        {
-            "query": "cat photos",
-            "country": "US",
-            "search_lang": "en",
-            "safesearch": "strict",
-            "count": 50,
-            "spellcheck": True,
-        }
-    )
-    refined_params = image_tool._refine_request_payload(params)
-
-    assert refined_params["q"] == "cat photos"
-    assert "query" not in refined_params
-    assert refined_params["country"] == "US"
-    assert refined_params["safesearch"] == "strict"
-    assert refined_params["count"] == 50
-    assert refined_params["spellcheck"] is True
-
-
-def test_news_refine_request_payload_passes_all_params(news_tool):
-    params = news_tool._common_payload_refinement(
-        {
-            "query": "breaking news",
-            "country": "US",
-            "count": 10,
-            "offset": 1,
-            "freshness": "pd",
-            "extra_snippets": True,
-        }
-    )
-    refined_params = news_tool._refine_request_payload(params)
-
-    assert refined_params["q"] == "breaking news"
-    assert "query" not in refined_params
-    assert refined_params["country"] == "US"
-    assert refined_params["offset"] == 1
-    assert refined_params["freshness"] == "pd"
-    assert refined_params["extra_snippets"] is True
-
-
-def test_video_refine_request_payload_passes_all_params(video_tool):
-    params = video_tool._common_payload_refinement(
-        {
-            "query": "tutorial",
-            "country": "US",
-            "count": 25,
-            "offset": 0,
-            "safesearch": "strict",
-            "freshness": "pm",
-        }
-    )
-    refined_params = video_tool._refine_request_payload(params)
-
-    assert refined_params["q"] == "tutorial"
-    assert "query" not in refined_params
-    assert refined_params["country"] == "US"
-    assert refined_params["offset"] == 0
-    assert refined_params["freshness"] == "pm"
-
-
-def test_legacy_constructor_params_flow_into_query_params():
-    """The legacy n_results and country constructor params are applied as defaults
-    when count/country are not explicitly provided at call time."""
-    tool = BraveWebSearchTool(n_results=3, country="BR")
-    params = tool._common_payload_refinement({"query": "test"})
-
-    assert params["count"] == 3
-    assert params["country"] == "BR"
-
-
-def test_legacy_constructor_params_do_not_override_explicit_query_params():
-    """Explicit query-time count/country take precedence over constructor defaults."""
-    tool = BraveWebSearchTool(n_results=3, country="BR")
-    params = tool._common_payload_refinement(
-        {"query": "test", "count": 10, "country": "US"}
-    )
-
-    assert params["count"] == 10
-    assert params["country"] == "US"
-
-
-def test_refine_request_payload_passes_multiple_goggles_as_multiple_params(web_tool):
-    result = web_tool._refine_request_payload(
-        {
-            "query": "test",
-            "goggles": ["goggle1", "goggle2"],
-        }
-    )
-    assert result["goggles"] == ["goggle1", "goggle2"]
-
-
-# Null-like / empty value stripping
-#
-# crewAI's ensure_all_properties_required (pydantic_schema_utils.py) marks
-# every schema property as required for OpenAI strict-mode compatibility.
-# Because optional Brave API parameters look required to the LLM, it fills
-# them with placeholder junk — None, "", "null", or [].  The test below
-# verifies that _common_payload_refinement strips these from optional fields.
-
-
-def test_common_refinement_strips_null_like_values(web_tool):
-    """_common_payload_refinement drops optional keys with None / '' / 'null' / []."""
-    params = web_tool._common_payload_refinement(
-        {
-            "query": "test",
-            "country": "US",
-            "search_lang": "",
-            "freshness": "null",
-            "count": 5,
-            "goggles": [],
-        }
-    )
-    assert params["q"] == "test"
-    assert params["country"] == "US"
-    assert params["count"] == 5
-    assert "search_lang" not in params
-    assert "freshness" not in params
-    assert "goggles" not in params
-
-
-# End-to-End _run() with Mocked HTTP Response
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_web_search_end_to_end(mock_get, web_tool):
-    web_tool.raw = True
-    data = {"web": {"results": [{"title": "R", "url": "http://r.co"}]}}
-    mock_get.return_value = _mock_response(json_data=data)
-
-    result = web_tool._run(query="test")
-
-    mock_get.assert_called_once()
-    call_args = mock_get.call_args.kwargs
-    assert call_args["params"]["q"] == "test"
-    assert call_args["headers"]["x-subscription-token"] == "test-api-key"
-    assert result == data
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_image_search_end_to_end(mock_get, image_tool):
-    image_tool.raw = True
-    data = {"results": [{"url": "http://img.co/a.jpg"}]}
-    mock_get.return_value = _mock_response(json_data=data)
-
-    assert image_tool._run(query="cats") == data
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_news_search_end_to_end(mock_get, news_tool):
-    news_tool.raw = True
-    data = {"results": [{"title": "News", "url": "http://n.co"}]}
-    mock_get.return_value = _mock_response(json_data=data)
-
-    assert news_tool._run(query="headlines") == data
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_video_search_end_to_end(mock_get, video_tool):
-    video_tool.raw = True
-    data = {"results": [{"title": "Vid", "url": "http://v.co"}]}
-    mock_get.return_value = _mock_response(json_data=data)
-
-    assert video_tool._run(query="python tutorial") == data
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_raw_false_calls_refine_response(mock_get, web_tool):
-    """With raw=False (the default), _refine_response transforms the API response."""
-    api_response = {
+@patch("requests.get")
+def test_brave_tool_search(mock_get, brave_tool):
+    mock_response = {
         "web": {
             "results": [
                 {
-                    "title": "CrewAI",
-                    "url": "https://crewai.com",
-                    "description": "AI agent framework",
+                    "title": "Test Title",
+                    "url": "http://test.com",
+                    "description": "Test Description",
                 }
             ]
         }
     }
-    mock_get.return_value = _mock_response(json_data=api_response)
-
-    assert web_tool.raw is False
-    result = web_tool._run(query="crewai")
-
-    # The web tool's _refine_response extracts and reshapes results.
-    # The key assertion: we should NOT get back the raw API envelope.
-    assert result != api_response
-
-
-# Backward Compatibility & Legacy Parameter Support
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_positional_query_argument(mock_get, web_tool):
-    """tool.run('my query') works as a positional argument."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    web_tool._run("positional test")
-
-    assert mock_get.call_args.kwargs["params"]["q"] == "positional test"
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_search_query_backward_compat(mock_get, web_tool):
-    """The legacy 'search_query' param is mapped to 'query'."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    web_tool._run(search_query="legacy test")
-
-    assert mock_get.call_args.kwargs["params"]["q"] == "legacy test"
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base._save_results_to_file")
-def test_save_file_called_when_enabled(mock_save, mock_get):
-    mock_get.return_value = _mock_response(json_data={"results": []})
-
-    tool = BraveWebSearchTool(save_file=True)
-    tool._run(query="test")
-
-    mock_save.assert_called_once()
-
-
-# Error Handling
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_connection_error_raises_runtime_error(mock_get, web_tool):
-    mock_get.side_effect = requests_lib.exceptions.ConnectionError("refused")
-    with pytest.raises(RuntimeError, match="Brave Search API connection failed"):
-        web_tool._run(query="test")
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_timeout_raises_runtime_error(mock_get, web_tool):
-    mock_get.side_effect = requests_lib.exceptions.Timeout("timed out")
-    with pytest.raises(RuntimeError, match="timed out"):
-        web_tool._run(query="test")
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_invalid_params_raises_value_error(mock_get, web_tool):
-    """count=999 exceeds WebSearchParams.count le=20."""
-    with pytest.raises(ValueError, match="Invalid parameters"):
-        web_tool._run(query="test", count=999)
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_4xx_error_raises_with_api_detail(mock_get, web_tool):
-    """A 422 with a structured error body includes code and detail in the message."""
-    mock_get.return_value = _mock_response(
-        status_code=422,
-        json_data={
-            "error": {
-                "id": "abc-123",
-                "status": 422,
-                "code": "OPTION_NOT_IN_PLAN",
-                "detail": "extra_snippets requires a Pro plan",
-            }
-        },
-    )
-    with pytest.raises(RuntimeError, match="OPTION_NOT_IN_PLAN") as exc_info:
-        web_tool._run(query="test")
-    assert "extra_snippets requires a Pro plan" in str(exc_info.value)
-    assert "HTTP 422" in str(exc_info.value)
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_auth_error_raises_immediately(mock_get, web_tool):
-    """A 401 with SUBSCRIPTION_TOKEN_INVALID is not retried."""
-    mock_get.return_value = _mock_response(
-        status_code=401,
-        json_data={
-            "error": {
-                "id": "xyz",
-                "status": 401,
-                "code": "SUBSCRIPTION_TOKEN_INVALID",
-                "detail": "The subscription token is invalid",
-            }
-        },
-    )
-    with pytest.raises(RuntimeError, match="SUBSCRIPTION_TOKEN_INVALID"):
-        web_tool._run(query="test")
-    # Should NOT have retried — only one call.
-    assert mock_get.call_count == 1
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_quota_limited_429_raises_immediately(mock_get, web_tool):
-    """A 429 with QUOTA_LIMITED is NOT retried — quota exhaustion is terminal."""
-    mock_get.return_value = _mock_response(
-        status_code=429,
-        json_data={
-            "error": {
-                "id": "ql-1",
-                "status": 429,
-                "code": "QUOTA_LIMITED",
-                "detail": "Monthly quota exceeded",
-            }
-        },
-    )
-    with pytest.raises(RuntimeError, match="QUOTA_LIMITED") as exc_info:
-        web_tool._run(query="test")
-    assert "Monthly quota exceeded" in str(exc_info.value)
-    # Terminal — only one HTTP call, no retries.
-    assert mock_get.call_count == 1
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_usage_limit_exceeded_429_raises_immediately(mock_get, web_tool):
-    """USAGE_LIMIT_EXCEEDED is also non-retryable, just like QUOTA_LIMITED."""
-    mock_get.return_value = _mock_response(
-        status_code=429,
-        json_data={
-            "error": {
-                "id": "ule-1",
-                "status": 429,
-                "code": "USAGE_LIMIT_EXCEEDED",
-            }
-        },
-        text="usage limit exceeded",
-    )
-    with pytest.raises(RuntimeError, match="USAGE_LIMIT_EXCEEDED"):
-        web_tool._run(query="test")
-    assert mock_get.call_count == 1
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_error_body_is_fully_included_in_message(mock_get, web_tool):
-    """The full JSON error body is included in the RuntimeError message."""
-    mock_get.return_value = _mock_response(
-        status_code=429,
-        json_data={
-            "error": {
-                "id": "x",
-                "status": 429,
-                "code": "QUOTA_LIMITED",
-                "detail": "Exceeded",
-                "meta": {"plan": "free", "limit": 1000},
-            }
-        },
-    )
-    with pytest.raises(RuntimeError) as exc_info:
-        web_tool._run(query="test")
-    msg = str(exc_info.value)
-    assert "HTTP 429" in msg
-    assert "QUOTA_LIMITED" in msg
-    assert "free" in msg
-    assert "1000" in msg
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_error_without_json_body_falls_back_to_text(mock_get, web_tool):
-    """When the error response isn't valid JSON, resp.text is used as the detail."""
-    resp = _mock_response(status_code=500, text="Internal Server Error")
-    resp.json.side_effect = ValueError("No JSON")
-    mock_get.return_value = resp
-
-    with pytest.raises(RuntimeError, match="Internal Server Error"):
-        web_tool._run(query="test")
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-def test_invalid_json_on_success_raises_runtime_error(mock_get, web_tool):
-    """A 200 OK with a non-JSON body raises RuntimeError."""
-    resp = _mock_response(status_code=200)
-    resp.json.side_effect = ValueError("Expecting value")
-    mock_get.return_value = resp
-
-    with pytest.raises(RuntimeError, match="invalid JSON"):
-        web_tool._run(query="test")
-
-
-# Rate Limiting
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_rate_limit_sleeps_when_too_fast(mock_time, mock_get, web_tool):
-    """Back-to-back calls within the interval trigger a sleep."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    # Simulate: last request was at t=100, "now" is t=100.2 (only 0.2s elapsed).
-    # With default 1 req/s the min interval is 1.0s, so it should sleep ~0.8s.
-    mock_time.time.return_value = 100.2
-    web_tool._last_request_time = 100.0
-
-    web_tool._run(query="test")
-
-    mock_time.sleep.assert_called_once()
-    sleep_duration = mock_time.sleep.call_args[0][0]
-    assert 0.7 < sleep_duration < 0.9  # approximately 0.8s
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_rate_limit_skips_sleep_when_enough_time_passed(mock_time, mock_get, web_tool):
-    """No sleep when the elapsed time already exceeds the interval."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    # Last request was at t=100, "now" is t=102 (2s elapsed > 1s interval).
-    mock_time.time.return_value = 102.0
-    web_tool._last_request_time = 100.0
-
-    web_tool._run(query="test")
-
-    mock_time.sleep.assert_not_called()
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_rate_limit_disabled_when_zero(mock_time, mock_get, web_tool):
-    """requests_per_second=0 disables rate limiting entirely."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    web_tool._last_request_time = 100.0
-    mock_time.time.return_value = 100.0  # same instant
-
-    web_tool._run(query="test")
-
-    mock_time.sleep.assert_not_called()
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_rate_limit_per_instance_independent(mock_time, mock_get, web_tool, image_tool):
-    """Each instance has its own rate-limit clock; a request on one does not delay the other."""
-    mock_get.return_value = _mock_response(json_data={})
-
-    # Web tool fires at t=100 (its clock goes 0 -> 100).
-    mock_time.time.return_value = 100.0
-    web_tool._run(query="test")
-
-    # Image tool fires at t=100.3. Its clock is still 0 (separate instance), so
-    # next_allowed = 1.0 and 100.3 > 1.0 — no sleep. Total process rate can be sum of instance limits.
-    mock_time.time.return_value = 100.3
-    image_tool._run(query="cats")
-
-    mock_time.sleep.assert_not_called()
-
-
-# Retry Behavior
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_429_rate_limited_retries_then_succeeds(mock_time, mock_get, web_tool):
-    """A transient RATE_LIMITED 429 is retried; success on the second attempt."""
-    mock_time.time.return_value = 200.0
-
-    resp_429 = _mock_response(
-        status_code=429,
-        json_data={"error": {"id": "r", "status": 429, "code": "RATE_LIMITED"}},
-        headers={"Retry-After": "2"},
-    )
-    resp_200 = _mock_response(status_code=200, json_data={"web": {"results": []}})
-    mock_get.side_effect = [resp_429, resp_200]
-
-    web_tool.raw = True
-    result = web_tool._run(query="test")
-
-    assert result == {"web": {"results": []}}
-    assert mock_get.call_count == 2
-    # Slept for the Retry-After value.
-    retry_sleeps = [c for c in mock_time.sleep.call_args_list if c[0][0] == 2.0]
-    assert len(retry_sleeps) == 1
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_5xx_is_retried(mock_time, mock_get, web_tool):
-    """A 502 server error is retried; success on the second attempt."""
-    mock_time.time.return_value = 200.0
-
-    resp_502 = _mock_response(status_code=502, text="Bad Gateway")
-    resp_502.json.side_effect = ValueError("no json")
-    resp_200 = _mock_response(status_code=200, json_data={"web": {"results": []}})
-    mock_get.side_effect = [resp_502, resp_200]
-
-    web_tool.raw = True
-    result = web_tool._run(query="test")
-
-    assert result == {"web": {"results": []}}
-    assert mock_get.call_count == 2
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_429_rate_limited_exhausts_retries(mock_time, mock_get, web_tool):
-    """Persistent RATE_LIMITED 429s exhaust retries and raise RuntimeError."""
-    mock_time.time.return_value = 200.0
-
-    resp_429 = _mock_response(
-        status_code=429,
-        json_data={"error": {"id": "r", "status": 429, "code": "RATE_LIMITED"}},
-    )
-    mock_get.return_value = resp_429
-
-    with pytest.raises(RuntimeError, match="RATE_LIMITED"):
-        web_tool._run(query="test")
-    # 3 attempts (default _max_retries).
-    assert mock_get.call_count == 3
-
-
-@patch("crewai_tools.tools.brave_search_tool.base.requests.get")
-@patch("crewai_tools.tools.brave_search_tool.base.time")
-def test_retry_uses_exponential_backoff_when_no_retry_after(
-    mock_time, mock_get, web_tool
-):
-    """Without Retry-After, backoff is 2^attempt (1s, 2s, ...)."""
-    mock_time.time.return_value = 200.0
-
-    resp_503 = _mock_response(status_code=503, text="Service Unavailable")
-    resp_503.json.side_effect = ValueError("no json")
-    resp_200 = _mock_response(status_code=200, json_data={"ok": True})
-    mock_get.side_effect = [resp_503, resp_503, resp_200]
-
-    web_tool.raw = True
-    web_tool._run(query="test")
-
-    # Two retries: attempt 0 → sleep(1.0), attempt 1 → sleep(2.0).
-    retry_sleeps = [c[0][0] for c in mock_time.sleep.call_args_list]
-    assert 1.0 in retry_sleeps
-    assert 2.0 in retry_sleeps
+    mock_get.return_value.json.return_value = mock_response
+
+    result = brave_tool.run(query="test")
+    data = json.loads(result)
+    assert isinstance(data, list)
+    assert len(data) >= 1
+    assert data[0]["title"] == "Test Title"
+    assert data[0]["url"] == "http://test.com"
+
+
+@patch("requests.get")
+def test_brave_tool(mock_get):
+    mock_response = {
+        "web": {
+            "results": [
+                {
+                    "title": "Brave Browser",
+                    "url": "https://brave.com",
+                    "description": "Brave Browser description",
+                }
+            ]
+        }
+    }
+    mock_get.return_value.json.return_value = mock_response
+
+    tool = BraveSearchTool(n_results=2)
+    result = tool.run(query="Brave Browser")
+    assert result is not None
+
+    # Parse JSON so we can examine the structure
+    data = json.loads(result)
+    assert isinstance(data, list)
+    assert len(data) >= 1
+
+    # First item should have expected fields: title, url, and description
+    first = data[0]
+    assert "title" in first
+    assert first["title"] == "Brave Browser"
+    assert "url" in first
+    assert first["url"] == "https://brave.com"
+    assert "description" in first
+    assert first["description"] == "Brave Browser description"
+
+
+if __name__ == "__main__":
+    test_brave_tool()
+    test_brave_tool_initialization()
+    # test_brave_tool_search(brave_tool)

lib/crewai-tools/tests/tools/test_code_interpreter_tool.py

@@ -76,24 +76,24 @@ print("This is line 2")"""
     )
 
 
-def test_restricted_sandbox_basic_code_execution(printer_mock, docker_unavailable_mock):
-    """Test basic code execution."""
+def test_docker_unavailable_raises_error(printer_mock, docker_unavailable_mock):
+    """Test that execution fails when Docker is unavailable in safe mode."""
     tool = CodeInterpreterTool()
     code = """
 result = 2 + 2
 print(result)
 """
-    result = tool.run(code=code, libraries_used=[])
-    printer_mock.assert_called_with(
-        "Running code in restricted sandbox", color="yellow"
-    )
-    assert result == 4
+    with pytest.raises(RuntimeError) as exc_info:
+        tool.run(code=code, libraries_used=[])
+    
+    assert "Docker is required for safe code execution" in str(exc_info.value)
+    assert "sandbox escape" in str(exc_info.value)
 
 
 def test_restricted_sandbox_running_with_blocked_modules(
     printer_mock, docker_unavailable_mock
 ):
-    """Test that restricted modules cannot be imported."""
+    """Test that restricted modules cannot be imported when using the deprecated sandbox directly."""
     tool = CodeInterpreterTool()
     restricted_modules = SandboxPython.BLOCKED_MODULES
 
@@ -102,18 +102,17 @@ def test_restricted_sandbox_running_with_blocked_modules(
 import {module}
 result = "Import succeeded"
 """
-        result = tool.run(code=code, libraries_used=[])
-        printer_mock.assert_called_with(
-            "Running code in restricted sandbox", color="yellow"
-        )
-
+        # Note: run_code_in_restricted_sandbox is deprecated and insecure
+        # This test verifies the old behavior but should not be used in production
+        result = tool.run_code_in_restricted_sandbox(code)
+        
         assert f"An error occurred: Importing '{module}' is not allowed" in result
 
 
 def test_restricted_sandbox_running_with_blocked_builtins(
     printer_mock, docker_unavailable_mock
 ):
-    """Test that restricted builtins are not available."""
+    """Test that restricted builtins are not available when using the deprecated sandbox directly."""
     tool = CodeInterpreterTool()
     restricted_builtins = SandboxPython.UNSAFE_BUILTINS
 
@@ -122,25 +121,23 @@ def test_restricted_sandbox_running_with_blocked_builtins(
 {builtin}("test")
 result = "Builtin available"
 """
-        result = tool.run(code=code, libraries_used=[])
-        printer_mock.assert_called_with(
-            "Running code in restricted sandbox", color="yellow"
-        )
+        # Note: run_code_in_restricted_sandbox is deprecated and insecure
+        # This test verifies the old behavior but should not be used in production
+        result = tool.run_code_in_restricted_sandbox(code)
         assert f"An error occurred: name '{builtin}' is not defined" in result
 
 
 def test_restricted_sandbox_running_with_no_result_variable(
     printer_mock, docker_unavailable_mock
 ):
-    """Test behavior when no result variable is set."""
+    """Test behavior when no result variable is set in deprecated sandbox."""
     tool = CodeInterpreterTool()
     code = """
 x = 10
 """
-    result = tool.run(code=code, libraries_used=[])
-    printer_mock.assert_called_with(
-        "Running code in restricted sandbox", color="yellow"
-    )
+    # Note: run_code_in_restricted_sandbox is deprecated and insecure
+    # This test verifies the old behavior but should not be used in production
+    result = tool.run_code_in_restricted_sandbox(code)
     assert result == "No result variable found."
 
 
@@ -172,3 +169,40 @@ result = eval("5/1")
         "WARNING: Running code in unsafe mode", color="bold_magenta"
     )
     assert 5.0 == result
+
+
+def test_sandbox_escape_vulnerability_demonstration(printer_mock):
+    """Demonstrate that the restricted sandbox is vulnerable to escape attacks.
+    
+    This test shows that an attacker can use Python object introspection to bypass
+    the restricted sandbox and access blocked modules like 'os'. This is why the
+    sandbox should never be used for untrusted code execution.
+    
+    NOTE: This test uses the deprecated run_code_in_restricted_sandbox directly
+    to demonstrate the vulnerability. In production, Docker is now required.
+    """
+    tool = CodeInterpreterTool()
+    
+    # Classic Python sandbox escape via object introspection
+    escape_code = """
+# Recover the real __import__ function via object introspection
+for cls in ().__class__.__bases__[0].__subclasses__():
+    if cls.__name__ == 'catch_warnings':
+        # Get the real builtins module
+        real_builtins = cls()._module.__builtins__
+        real_import = real_builtins['__import__']
+        # Now we can import os and execute commands
+        os = real_import('os')
+        # Demonstrate we have escaped the sandbox
+        result = "SANDBOX_ESCAPED" if hasattr(os, 'system') else "FAILED"
+        break
+"""
+    
+    # The deprecated sandbox is vulnerable to this attack
+    result = tool.run_code_in_restricted_sandbox(escape_code)
+    
+    # This demonstrates the vulnerability - the attacker can escape
+    assert result == "SANDBOX_ESCAPED", (
+        "The restricted sandbox was bypassed via object introspection. "
+        "This is why Docker is now required for safe code execution."
+    )

lib/crewai/pyproject.toml

@@ -53,7 +53,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.10.2a1",
+    "crewai-tools==1.10.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -4,7 +4,6 @@ import urllib.request
 import warnings
 
 from crewai.agent.core import Agent
-from crewai.agent.planning_config import PlanningConfig
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
@@ -41,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.10.2a1"
+__version__ = "1.10.1"
 _telemetry_submitted = False
 
 
@@ -101,7 +100,6 @@ __all__ = [
     "Knowledge",
     "LLMGuardrail",
     "Memory",
-    "PlanningConfig",
     "Process",
     "Task",
     "TaskOutput",

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

@@ -22,7 +22,6 @@ from pydantic import (
 )
 from typing_extensions import Self
 
-from crewai.agent.planning_config import PlanningConfig
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -192,23 +191,13 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    planning_config: PlanningConfig | None = Field(
-        default=None,
-        description="Configuration for agent planning before task execution.",
-    )
-    planning: bool = Field(
+    reasoning: bool = Field(
         default=False,
         description="Whether the agent should reflect and create a plan before executing a task.",
     )
-    reasoning: bool = Field(
-        default=False,
-        description="[DEPRECATED: Use planning_config instead] Whether the agent should reflect and create a plan before executing a task.",
-        deprecated=True,
-    )
     max_reasoning_attempts: int | None = Field(
         default=None,
-        description="[DEPRECATED: Use planning_config.max_attempts instead] Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
-        deprecated=True,
+        description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
     )
     embedder: EmbedderConfig | None = Field(
         default=None,
@@ -275,26 +264,8 @@ class Agent(BaseAgent):
         if self.allow_code_execution:
             self._validate_docker_installation()
 
-        # Handle backward compatibility: convert reasoning=True to planning_config
-        if self.reasoning and self.planning_config is None:
-            import warnings
-
-            warnings.warn(
-                "The 'reasoning' parameter is deprecated. Use 'planning_config=PlanningConfig()' instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            self.planning_config = PlanningConfig(
-                max_attempts=self.max_reasoning_attempts,
-            )
-
         return self
 
-    @property
-    def planning_enabled(self) -> bool:
-        """Check if planning is enabled for this agent."""
-        return self.planning_config is not None or self.planning
-
     def _setup_agent_executor(self) -> None:
         if not self.cache_handler:
             self.cache_handler = CacheHandler()
@@ -363,11 +334,7 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        # Only call handle_reasoning for legacy CrewAgentExecutor
-        # For AgentExecutor, planning is handled in AgentExecutor.generate_plan()
-        if self.executor_class is not AgentExecutor:
-            handle_reasoning(self, task)
-
+        handle_reasoning(self, task)
         self._inject_date_to_task(task)
 
         if self.tools_handler:
@@ -605,10 +572,7 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        if self.executor_class is not AgentExecutor:
-            handle_reasoning(
-                self, task
-            )  # we need this till CrewAgentExecutor migrates to AgentExecutor
+        handle_reasoning(self, task)
         self._inject_date_to_task(task)
 
         if self.tools_handler:
@@ -1454,19 +1418,17 @@ class Agent(BaseAgent):
         except Exception as e:
             self._logger.log("error", f"Failed to save kickoff result to memory: {e}")
 
-    def _build_output_from_result(
+    def _execute_and_build_output(
         self,
-        result: dict[str, Any],
         executor: AgentExecutor,
+        inputs: dict[str, str],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
-        """Build a LiteAgentOutput from an executor result dict.
-
-        Shared logic used by both sync and async execution paths.
+        """Execute the agent and build the output object.
 
         Args:
-            result: The result dictionary from executor.invoke / invoke_async.
             executor: The executor instance.
+            inputs: Input dictionary for execution.
             response_format: Optional response format.
 
         Returns:
@@ -1474,6 +1436,8 @@ class Agent(BaseAgent):
         """
         import json
 
+        # Execute the agent (this is called from sync path, so invoke returns dict)
+        result = cast(dict[str, Any], executor.invoke(inputs))
         output = result.get("output", "")
 
         # Handle response format conversion
@@ -1521,39 +1485,91 @@ class Agent(BaseAgent):
             else str(raw_output)
         )
 
-        todo_results = LiteAgentOutput.from_todo_items(executor.state.todos.items)
-
         return LiteAgentOutput(
             raw=raw_str,
             pydantic=formatted_result,
             agent_role=self.role,
             usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            messages=list(executor.state.messages),
-            plan=executor.state.plan,
-            todos=todo_results,
-            replan_count=executor.state.replan_count,
-            last_replan_reason=executor.state.last_replan_reason,
+            messages=executor.messages,
         )
 
-    def _execute_and_build_output(
-        self,
-        executor: AgentExecutor,
-        inputs: dict[str, str],
-        response_format: type[Any] | None = None,
-    ) -> LiteAgentOutput:
-        """Execute the agent synchronously and build the output object."""
-        result = cast(dict[str, Any], executor.invoke(inputs))
-        return self._build_output_from_result(result, executor, response_format)
-
     async def _execute_and_build_output_async(
         self,
         executor: AgentExecutor,
         inputs: dict[str, str],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
-        """Execute the agent asynchronously and build the output object."""
+        """Execute the agent asynchronously and build the output object.
+
+        This is the async version of _execute_and_build_output that uses
+        invoke_async() for native async execution within event loops.
+
+        Args:
+            executor: The executor instance.
+            inputs: Input dictionary for execution.
+            response_format: Optional response format.
+
+        Returns:
+            LiteAgentOutput with raw output, formatted result, and metrics.
+        """
+        import json
+
+        # Execute the agent asynchronously
         result = await executor.invoke_async(inputs)
-        return self._build_output_from_result(result, executor, response_format)
+        output = result.get("output", "")
+
+        # Handle response format conversion
+        formatted_result: BaseModel | None = None
+        raw_output: str
+
+        if isinstance(output, BaseModel):
+            formatted_result = output
+            raw_output = output.model_dump_json()
+        elif response_format:
+            raw_output = str(output) if not isinstance(output, str) else output
+            try:
+                model_schema = generate_model_description(response_format)
+                schema = json.dumps(model_schema, indent=2)
+                instructions = self.i18n.slice("formatted_task_instructions").format(
+                    output_format=schema
+                )
+
+                converter = Converter(
+                    llm=self.llm,
+                    text=raw_output,
+                    model=response_format,
+                    instructions=instructions,
+                )
+
+                conversion_result = converter.to_pydantic()
+                if isinstance(conversion_result, BaseModel):
+                    formatted_result = conversion_result
+            except ConverterError:
+                pass  # Keep raw output if conversion fails
+        else:
+            raw_output = str(output) if not isinstance(output, str) else output
+
+        # Get token usage metrics
+        if isinstance(self.llm, BaseLLM):
+            usage_metrics = self.llm.get_token_usage_summary()
+        else:
+            usage_metrics = self._token_process.get_summary()
+
+        raw_str = (
+            raw_output
+            if isinstance(raw_output, str)
+            else raw_output.model_dump_json()
+            if isinstance(raw_output, BaseModel)
+            else str(raw_output)
+        )
+
+        return LiteAgentOutput(
+            raw=raw_str,
+            pydantic=formatted_result,
+            agent_role=self.role,
+            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=executor.messages,
+        )
 
     def _process_kickoff_guardrail(
         self,

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

@@ -1,115 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Literal
-
-from pydantic import BaseModel, Field
-
-
-class PlanningConfig(BaseModel):
-    """Configuration for agent planning/reasoning before task execution.
-
-    This allows users to customize the planning behavior including prompts,
-    iteration limits, the LLM used for planning, and the reasoning effort
-    level that controls post-step observation and replanning behavior.
-
-    Note: To disable planning, don't pass a planning_config or set planning=False
-    on the Agent. The presence of a PlanningConfig enables planning.
-
-    Attributes:
-        reasoning_effort: Controls observation and replanning after each step.
-            - "low": Observe each step (validates success), but skip the
-              decide/replan/refine pipeline. Steps are marked complete and
-              execution continues linearly. Fastest option.
-            - "medium": Observe each step. On failure, trigger replanning.
-              On success, skip refinement and continue. Balanced option.
-            - "high": Full observation pipeline — observe every step, then
-              route through decide_next_action which can trigger early goal
-              achievement, full replanning, or lightweight refinement.
-              Most adaptive but adds latency per step.
-        max_attempts: Maximum number of planning refinement attempts.
-            If None, will continue until the agent indicates readiness.
-        max_steps: Maximum number of steps in the generated plan.
-        system_prompt: Custom system prompt for planning. Uses default if None.
-        plan_prompt: Custom prompt for creating the initial plan.
-        refine_prompt: Custom prompt for refining the plan.
-        llm: LLM to use for planning. Uses agent's LLM if None.
-
-    Example:
-        ```python
-        from crewai import Agent
-        from crewai.agent.planning_config import PlanningConfig
-
-        # Simple usage — fast, linear execution (default)
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            planning_config=PlanningConfig(),
-        )
-
-        # Balanced — replan only when steps fail
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            planning_config=PlanningConfig(
-                reasoning_effort="medium",
-            ),
-        )
-
-        # Full adaptive planning with refinement and replanning
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            planning_config=PlanningConfig(
-                reasoning_effort="high",
-                max_attempts=3,
-                max_steps=10,
-                plan_prompt="Create a focused plan for: {description}",
-                llm="gpt-4o-mini",  # Use cheaper model for planning
-            ),
-        )
-        ```
-    """
-
-    reasoning_effort: Literal["low", "medium", "high"] = Field(
-        default="medium",
-        description=(
-            "Controls post-step observation and replanning behavior. "
-            "'low' observes steps but skips replanning/refinement (fastest). "
-            "'medium' observes and replans only on step failure (balanced). "
-            "'high' runs full observation pipeline with replanning, refinement, "
-            "and early goal detection (most adaptive, highest latency)."
-        ),
-    )
-    max_attempts: int | None = Field(
-        default=None,
-        description=(
-            "Maximum number of planning refinement attempts. "
-            "If None, will continue until the agent indicates readiness."
-        ),
-    )
-    max_steps: int = Field(
-        default=20,
-        description="Maximum number of steps in the generated plan.",
-        ge=1,
-    )
-    system_prompt: str | None = Field(
-        default=None,
-        description="Custom system prompt for planning. Uses default if None.",
-    )
-    plan_prompt: str | None = Field(
-        default=None,
-        description="Custom prompt for creating the initial plan.",
-    )
-    refine_prompt: str | None = Field(
-        default=None,
-        description="Custom prompt for refining the plan.",
-    )
-    llm: str | Any | None = Field(
-        default=None,
-        description="LLM to use for planning. Uses agent's LLM if None.",
-    )
-
-    model_config = {"arbitrary_types_allowed": True}

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

@@ -28,20 +28,13 @@ if TYPE_CHECKING:
 
 
 def handle_reasoning(agent: Agent, task: Task) -> None:
-    """Handle the reasoning/planning process for an agent before task execution.
-
-    This function checks if planning is enabled for the agent and, if so,
-    creates a plan that gets appended to the task description.
-
-    Note: This function is used by CrewAgentExecutor (legacy path).
-    For AgentExecutor, planning is handled in AgentExecutor.generate_plan().
+    """Handle the reasoning process for an agent before task execution.
 
     Args:
         agent: The agent performing the task.
         task: The task to execute.
     """
-    # Check if planning is enabled using the planning_enabled property
-    if not getattr(agent, "planning_enabled", False):
+    if not agent.reasoning:
         return
 
     try:
@@ -50,13 +43,13 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
             AgentReasoningOutput,
         )
 
-        planning_handler = AgentReasoning(agent=agent, task=task)
-        planning_output: AgentReasoningOutput = (
-            planning_handler.handle_agent_reasoning()
+        reasoning_handler = AgentReasoning(task=task, agent=agent)
+        reasoning_output: AgentReasoningOutput = (
+            reasoning_handler.handle_agent_reasoning()
         )
-        task.description += f"\n\nPlanning:\n{planning_output.plan.plan}"
+        task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
     except Exception as e:
-        agent._logger.log("error", f"Error during planning: {e!s}")
+        agent._logger.log("error", f"Error during reasoning process: {e!s}")
 
 
 def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:

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

@@ -38,7 +38,7 @@ from crewai.utilities.string_utils import interpolate_only
 
 
 _SLUG_RE: Final[re.Pattern[str]] = re.compile(
-    r"^(?:crewai-amp:)?[a-zA-Z0-9][a-zA-Z0-9_-]*(?:#[\w-]+)?$"
+    r"^(?:crewai-amp:)?[a-zA-Z0-9][a-zA-Z0-9_-]*(?:#\w+)?$"
 )
 
 

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

@@ -8,8 +8,8 @@ from __future__ import annotations
 
 import asyncio
 from collections.abc import Callable
-from concurrent.futures import ThreadPoolExecutor, as_completed
 import contextvars
+from concurrent.futures import ThreadPoolExecutor, as_completed
 import inspect
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast

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

@@ -1,356 +0,0 @@
-"""PlannerObserver: Observation phase after each step execution.
-
-Implements the "Observe" phase. After every step execution, the Planner
-analyzes what happened, what new information was learned, and whether the
-remaining plan is still valid.
-
-This is NOT an error detector — it runs on every step, including successes,
-to incorporate runtime observations into the remaining plan.
-
-Refinements are structured (StepRefinement objects) and applied directly
-from the observation result — no second LLM call required.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING, Any
-
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.observation_events import (
-    StepObservationCompletedEvent,
-    StepObservationFailedEvent,
-    StepObservationStartedEvent,
-)
-from crewai.utilities.i18n import I18N, get_i18n
-from crewai.utilities.llm_utils import create_llm
-from crewai.utilities.planning_types import StepObservation, TodoItem
-from crewai.utilities.types import LLMMessage
-
-
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.task import Task
-
-logger = logging.getLogger(__name__)
-
-
-class PlannerObserver:
-    """Observes step execution results and decides on plan continuation.
-
-    After EVERY step execution, this class:
-    1. Analyzes what the step accomplished
-    2. Identifies new information learned
-    3. Decides if the remaining plan is still valid
-    4. Suggests lightweight refinements or triggers full replanning
-
-    LLM resolution (magical fallback):
-    - If ``agent.planning_config.llm`` is explicitly set → use that
-    - Otherwise → fall back to ``agent.llm`` (same LLM for everything)
-
-    Args:
-        agent: The agent instance (for LLM resolution and config).
-        task: Optional task context (for description and expected output).
-    """
-
-    def __init__(
-        self,
-        agent: Agent,
-        task: Task | None = None,
-        kickoff_input: str = "",
-    ) -> None:
-        self.agent = agent
-        self.task = task
-        self.kickoff_input = kickoff_input
-        self.llm = self._resolve_llm()
-        self._i18n: I18N = get_i18n()
-
-    def _resolve_llm(self) -> Any:
-        """Resolve which LLM to use for observation/planning.
-
-        Mirrors AgentReasoning._resolve_llm(): uses planning_config.llm
-        if explicitly set, otherwise falls back to agent.llm.
-
-        Returns:
-            The resolved LLM instance.
-        """
-        from crewai.llm import LLM
-
-        config = getattr(self.agent, "planning_config", None)
-        if config is not None and config.llm is not None:
-            if isinstance(config.llm, LLM):
-                return config.llm
-            return create_llm(config.llm)
-        return self.agent.llm
-
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
-    def observe(
-        self,
-        completed_step: TodoItem,
-        result: str,
-        all_completed: list[TodoItem],
-        remaining_todos: list[TodoItem],
-    ) -> StepObservation:
-        """Observe a step's result and decide on plan continuation.
-
-        This runs after EVERY step execution — not just failures.
-
-        Args:
-            completed_step: The todo item that was just executed.
-            result: The final result string from the step.
-            all_completed: All previously completed todos (for context).
-            remaining_todos: The pending todos still in the plan.
-
-        Returns:
-            StepObservation with the Planner's analysis. Any suggested
-            refinements are structured StepRefinement objects ready for
-            direct application — no second LLM call needed.
-        """
-        agent_role = self.agent.role
-
-        crewai_event_bus.emit(
-            self.agent,
-            event=StepObservationStartedEvent(
-                agent_role=agent_role,
-                step_number=completed_step.step_number,
-                step_description=completed_step.description,
-                from_task=self.task,
-                from_agent=self.agent,
-            ),
-        )
-
-        messages = self._build_observation_messages(
-            completed_step, result, all_completed, remaining_todos
-        )
-
-        try:
-            response = self.llm.call(
-                messages,
-                response_model=StepObservation,
-                from_task=self.task,
-                from_agent=self.agent,
-            )
-
-            observation = self._parse_observation_response(response)
-
-            refinement_summaries = (
-                [
-                    f"Step {r.step_number}: {r.new_description}"
-                    for r in observation.suggested_refinements
-                ]
-                if observation.suggested_refinements
-                else None
-            )
-
-            crewai_event_bus.emit(
-                self.agent,
-                event=StepObservationCompletedEvent(
-                    agent_role=agent_role,
-                    step_number=completed_step.step_number,
-                    step_description=completed_step.description,
-                    step_completed_successfully=observation.step_completed_successfully,
-                    key_information_learned=observation.key_information_learned,
-                    remaining_plan_still_valid=observation.remaining_plan_still_valid,
-                    needs_full_replan=observation.needs_full_replan,
-                    replan_reason=observation.replan_reason,
-                    goal_already_achieved=observation.goal_already_achieved,
-                    suggested_refinements=refinement_summaries,
-                    from_task=self.task,
-                    from_agent=self.agent,
-                ),
-            )
-
-            return observation
-
-        except Exception as e:
-            logger.warning(
-                f"Observation LLM call failed: {e}. Defaulting to conservative replan."
-            )
-
-            crewai_event_bus.emit(
-                self.agent,
-                event=StepObservationFailedEvent(
-                    agent_role=agent_role,
-                    step_number=completed_step.step_number,
-                    step_description=completed_step.description,
-                    error=str(e),
-                    from_task=self.task,
-                    from_agent=self.agent,
-                ),
-            )
-
-            # Don't force a full replan — the step may have succeeded even if the
-            # observer LLM failed to parse the result. Defaulting to "continue" is
-            # far less disruptive than wiping the entire plan on every observer error.
-            return StepObservation(
-                step_completed_successfully=True,
-                key_information_learned="",
-                remaining_plan_still_valid=True,
-                needs_full_replan=False,
-            )
-
-    def _extract_task_section(self, text: str) -> str:
-        """Extract the ## Task body from a structured enriched instruction.
-
-        Falls back to the full text (capped at 2000 chars) for plain inputs.
-        """
-        for marker in ("\n## Task\n", "\n## Task:", "## Task\n"):
-            idx = text.find(marker)
-            if idx >= 0:
-                start = idx + len(marker)
-                for end_marker in ("\n---\n", "\n## "):
-                    end = text.find(end_marker, start)
-                    if end > 0:
-                        return text[start:end].strip()
-                return text[start : start + 2000].strip()
-        return text[:2000] if len(text) > 2000 else text
-
-    def apply_refinements(
-        self,
-        observation: StepObservation,
-        remaining_todos: list[TodoItem],
-    ) -> list[TodoItem]:
-        """Apply structured refinements from the observation directly to todo descriptions.
-
-        No LLM call needed — refinements are already structured StepRefinement
-        objects produced by the observation call. This is a pure in-memory update.
-
-        Args:
-            observation: The observation containing structured refinements.
-            remaining_todos: The pending todos to update in-place.
-
-        Returns:
-            The same todo list with updated descriptions where refinements applied.
-        """
-        if not observation.suggested_refinements:
-            return remaining_todos
-
-        todo_by_step: dict[int, TodoItem] = {t.step_number: t for t in remaining_todos}
-        for refinement in observation.suggested_refinements:
-            if refinement.step_number in todo_by_step and refinement.new_description:
-                todo_by_step[refinement.step_number].description = refinement.new_description
-
-        return remaining_todos
-
-    # ------------------------------------------------------------------
-    # Internal: Message building
-    # ------------------------------------------------------------------
-
-    def _build_observation_messages(
-        self,
-        completed_step: TodoItem,
-        result: str,
-        all_completed: list[TodoItem],
-        remaining_todos: list[TodoItem],
-    ) -> list[LLMMessage]:
-        """Build messages for the observation LLM call."""
-        task_desc = ""
-        task_goal = ""
-        if self.task:
-            task_desc = self.task.description or ""
-            task_goal = self.task.expected_output or ""
-        elif self.kickoff_input:
-            # Standalone kickoff path — no Task object, but we have the raw input.
-            # Extract just the ## Task section so the observer sees the actual goal,
-            # not the full enriched instruction with env/tools/verification noise.
-            task_desc = self._extract_task_section(self.kickoff_input)
-            task_goal = "Complete the task successfully"
-
-        system_prompt = self._i18n.retrieve("planning", "observation_system_prompt")
-
-        # Build context of what's been done
-        completed_summary = ""
-        if all_completed:
-            completed_lines = []
-            for todo in all_completed:
-                result_preview = (todo.result or "")[:200]
-                completed_lines.append(
-                    f"  Step {todo.step_number}: {todo.description}\n"
-                    f"    Result: {result_preview}"
-                )
-            completed_summary = "\n## Previously completed steps:\n" + "\n".join(
-                completed_lines
-            )
-
-        # Build remaining plan
-        remaining_summary = ""
-        if remaining_todos:
-            remaining_lines = [
-                f"  Step {todo.step_number}: {todo.description}"
-                for todo in remaining_todos
-            ]
-            remaining_summary = "\n## Remaining plan steps:\n" + "\n".join(
-                remaining_lines
-            )
-
-        user_prompt = self._i18n.retrieve("planning", "observation_user_prompt").format(
-            task_description=task_desc,
-            task_goal=task_goal,
-            completed_summary=completed_summary,
-            step_number=completed_step.step_number,
-            step_description=completed_step.description,
-            step_result=result,
-            remaining_summary=remaining_summary,
-        )
-
-        return [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ]
-
-    @staticmethod
-    def _parse_observation_response(response: Any) -> StepObservation:
-        """Parse the LLM response into a StepObservation.
-
-        The LLM may return:
-        - A StepObservation instance directly (streaming + litellm path)
-        - A JSON string (non-streaming path serialises model_dump_json())
-        - A dict (some provider paths)
-        - Something else (unexpected)
-
-        We handle all cases to avoid silently falling back to a
-        hardcoded success default.
-        """
-
-        if isinstance(response, StepObservation):
-            return response
-
-        # JSON string path — most common miss before this fix
-        if isinstance(response, str):
-            text = response.strip()
-            try:
-                return StepObservation.model_validate_json(text)
-            except Exception:  # noqa: S110
-                pass
-            # Some LLMs wrap the JSON in markdown fences
-            if text.startswith("```"):
-                lines = text.split("\n")
-                # Strip first and last lines (``` markers)
-                inner = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
-                try:
-                    return StepObservation.model_validate_json(inner.strip())
-                except Exception:  # noqa: S110
-                    pass
-
-        # Dict path
-        if isinstance(response, dict):
-            try:
-                return StepObservation.model_validate(response)
-            except Exception:  # noqa: S110
-                pass
-
-        # Last resort — log what we got so it's diagnosable
-        logger.warning(
-            "Could not parse observation response (type=%s). "
-            "Falling back to default success observation. Preview: %.200s",
-            type(response).__name__,
-            str(response),
-        )
-        return StepObservation(
-            step_completed_successfully=True,
-            key_information_learned=str(response) if response else "",
-            remaining_plan_still_valid=True,
-        )

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

@@ -1,616 +0,0 @@
-"""StepExecutor: Isolated executor for a single plan step.
-
-Implements the direct-action execution pattern from Plan-and-Act
-(arxiv 2503.09572): the Executor receives one step description,
-makes a single LLM call, executes any tool call returned, and
-returns the result immediately.
-
-There is no inner loop. Recovery from failure (retry, replan) is
-the responsibility of PlannerObserver and AgentExecutor — keeping
-this class single-purpose and fast.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Callable
-from datetime import datetime
-import json
-import time
-from typing import TYPE_CHECKING, Any, cast
-
-from pydantic import BaseModel
-
-from crewai.agents.parser import AgentAction, AgentFinish
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.tool_usage_events import (
-    ToolUsageErrorEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.agent_utils import (
-    build_tool_calls_assistant_message,
-    check_native_tool_support,
-    enforce_rpm_limit,
-    execute_single_native_tool_call,
-    format_message_for_llm,
-    is_tool_call_list,
-    process_llm_response,
-    setup_native_tools,
-)
-from crewai.utilities.i18n import I18N, get_i18n
-from crewai.utilities.planning_types import TodoItem
-from crewai.utilities.printer import Printer
-from crewai.utilities.step_execution_context import StepExecutionContext, StepResult
-from crewai.utilities.string_utils import sanitize_tool_name
-from crewai.utilities.tool_utils import execute_tool_and_check_finality
-from crewai.utilities.types import LLMMessage
-
-
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.agents.tools_handler import ToolsHandler
-    from crewai.crew import Crew
-    from crewai.llms.base_llm import BaseLLM
-    from crewai.task import Task
-    from crewai.tools.base_tool import BaseTool
-    from crewai.tools.structured_tool import CrewStructuredTool
-
-
-class StepExecutor:
-    """Executes a SINGLE todo item using direct-action execution.
-
-    The StepExecutor owns its own message list per invocation. It never reads
-    or writes the AgentExecutor's state. Results flow back via StepResult.
-
-    Execution pattern (per Plan-and-Act, arxiv 2503.09572):
-        1. Build messages from todo + context
-        2. Call LLM once (with or without native tools)
-        3. If tool call → execute it → return tool result
-        4. If text answer → return it directly
-        No inner loop — recovery is PlannerObserver's responsibility.
-
-    Args:
-        llm: The language model to use for execution.
-        tools: Structured tools available to the executor.
-        agent: The agent instance (for role/goal/verbose/config).
-        original_tools: Original BaseTool instances (needed for native tool schema).
-        tools_handler: Optional tools handler for caching and delegation tracking.
-        task: Optional task context.
-        crew: Optional crew context.
-        function_calling_llm: Optional separate LLM for function calling.
-        request_within_rpm_limit: Optional RPM limit function.
-        callbacks: Optional list of callbacks.
-        i18n: Optional i18n instance.
-    """
-
-    def __init__(
-        self,
-        llm: BaseLLM,
-        tools: list[CrewStructuredTool],
-        agent: Agent,
-        original_tools: list[BaseTool] | None = None,
-        tools_handler: ToolsHandler | None = None,
-        task: Task | None = None,
-        crew: Crew | None = None,
-        function_calling_llm: BaseLLM | Any | None = None,
-        request_within_rpm_limit: Callable[[], bool] | None = None,
-        callbacks: list[Any] | None = None,
-        i18n: I18N | None = None,
-    ) -> None:
-        self.llm = llm
-        self.tools = tools
-        self.agent = agent
-        self.original_tools = original_tools or []
-        self.tools_handler = tools_handler
-        self.task = task
-        self.crew = crew
-        self.function_calling_llm = function_calling_llm
-        self.request_within_rpm_limit = request_within_rpm_limit
-        self.callbacks = callbacks or []
-        self._i18n: I18N = i18n or get_i18n()
-        self._printer: Printer = Printer()
-
-        # Native tool support — set up once
-        self._use_native_tools = check_native_tool_support(
-            self.llm, self.original_tools
-        )
-        self._openai_tools: list[dict[str, Any]] = []
-        self._available_functions: dict[str, Callable[..., Any]] = {}
-        if self._use_native_tools and self.original_tools:
-            (
-                self._openai_tools,
-                self._available_functions,
-                _,
-            ) = setup_native_tools(self.original_tools)
-
-    # ------------------------------------------------------------------
-    # Public API
-    # ------------------------------------------------------------------
-
-    def execute(self, todo: TodoItem, context: StepExecutionContext) -> StepResult:
-        """Execute a single todo item using a multi-turn action loop.
-
-        Enforces the RPM limit, builds a fresh message list, then iterates
-        LLM call → tool execution → observation until the LLM signals it is
-        done (text answer) or max_step_iterations is reached.  Never touches
-        external AgentExecutor state.
-
-        Args:
-            todo: The todo item to execute.
-            context: Immutable context with task info and dependency results.
-
-        Returns:
-            StepResult with the outcome.
-        """
-        start_time = time.monotonic()
-        tool_calls_made: list[str] = []
-
-        try:
-            enforce_rpm_limit(self.request_within_rpm_limit)
-            messages = self._build_isolated_messages(todo, context)
-
-            if self._use_native_tools:
-                result_text = self._execute_native(messages, tool_calls_made)
-            else:
-                result_text = self._execute_text_parsed(messages, tool_calls_made)
-            self._validate_expected_tool_usage(todo, tool_calls_made)
-
-            elapsed = time.monotonic() - start_time
-            return StepResult(
-                success=True,
-                result=result_text,
-                tool_calls_made=tool_calls_made,
-                execution_time=elapsed,
-            )
-        except Exception as e:
-            elapsed = time.monotonic() - start_time
-            return StepResult(
-                success=False,
-                result="",
-                error=str(e),
-                tool_calls_made=tool_calls_made,
-                execution_time=elapsed,
-            )
-
-    # ------------------------------------------------------------------
-    # Internal: Message building
-    # ------------------------------------------------------------------
-
-    def _build_isolated_messages(
-        self, todo: TodoItem, context: StepExecutionContext
-    ) -> list[LLMMessage]:
-        """Build a fresh message list for this step's execution.
-
-        System prompt tells the LLM it is an Executor focused on one step.
-        User prompt provides the step description, dependencies, and tools.
-        """
-        system_prompt = self._build_system_prompt()
-        user_prompt = self._build_user_prompt(todo, context)
-
-        return [
-            format_message_for_llm(system_prompt, role="system"),
-            format_message_for_llm(user_prompt, role="user"),
-        ]
-
-    def _build_system_prompt(self) -> str:
-        """Build the Executor's system prompt."""
-        role = self.agent.role if self.agent else "Assistant"
-        goal = self.agent.goal if self.agent else "Complete tasks efficiently"
-        backstory = getattr(self.agent, "backstory", "") or ""
-
-        tools_section = ""
-        if self.tools and not self._use_native_tools:
-            tool_names = ", ".join(sanitize_tool_name(t.name) for t in self.tools)
-            tools_section = self._i18n.retrieve(
-                "planning", "step_executor_tools_section"
-            ).format(tool_names=tool_names)
-
-        return self._i18n.retrieve("planning", "step_executor_system_prompt").format(
-            role=role,
-            backstory=backstory,
-            goal=goal,
-            tools_section=tools_section,
-        )
-
-    def _extract_task_section(self, task_description: str) -> str:
-        """Extract the most relevant portion of the task description.
-
-        For structured descriptions (e.g. harbor_agent-style with ## Task
-        and ## Instructions sections), extracts just the task body so the
-        executor sees the requirements without duplicating tool/verification
-        instructions that are already in the system prompt.
-
-        For plain descriptions, returns the full text (up to 2000 chars).
-        """
-        # Try to extract between "## Task" and the next "---" separator
-        # or next "##" heading — this isolates the task spec from env/tool noise.
-        for marker in ("\n## Task\n", "\n## Task:", "## Task\n"):
-            idx = task_description.find(marker)
-            if idx >= 0:
-                start = idx + len(marker)
-                # End at the first horizontal rule or next top-level ## section
-                for end_marker in ("\n---\n", "\n## "):
-                    end = task_description.find(end_marker, start)
-                    if end > 0:
-                        return task_description[start:end].strip()
-                # No end marker — take up to 2000 chars
-                return task_description[start : start + 2000].strip()
-
-        # No structured format — use the full description, reasonably truncated
-        if len(task_description) > 2000:
-            return task_description[:2000] + "\n... [truncated]"
-        return task_description
-
-    def _build_user_prompt(self, todo: TodoItem, context: StepExecutionContext) -> str:
-        """Build the user prompt for this specific step."""
-        parts: list[str] = []
-
-        # Include overall task context so the executor knows the full goal and
-        # required output format/location — critical for knowing WHAT to produce.
-        # We extract only the task body (not tool instructions or verification
-        # sections) to avoid duplicating directives already in the system prompt.
-        if context.task_description:
-            task_section = self._extract_task_section(context.task_description)
-            if task_section:
-                parts.append(
-                    self._i18n.retrieve(
-                        "planning", "step_executor_task_context"
-                    ).format(
-                        task_context=task_section,
-                    )
-                )
-
-        parts.append(
-            self._i18n.retrieve("planning", "step_executor_user_prompt").format(
-                step_description=todo.description,
-            )
-        )
-
-        if todo.tool_to_use:
-            parts.append(
-                self._i18n.retrieve("planning", "step_executor_suggested_tool").format(
-                    tool_to_use=todo.tool_to_use,
-                )
-            )
-
-        # Include dependency results (final results only, no traces)
-        if context.dependency_results:
-            parts.append(
-                self._i18n.retrieve("planning", "step_executor_context_header")
-            )
-            for step_num, result in sorted(context.dependency_results.items()):
-                parts.append(
-                    self._i18n.retrieve(
-                        "planning", "step_executor_context_entry"
-                    ).format(step_number=step_num, result=result)
-                )
-
-        parts.append(self._i18n.retrieve("planning", "step_executor_complete_step"))
-
-        return "\n".join(parts)
-
-    # ------------------------------------------------------------------
-    # Internal: Multi-turn execution loop
-    # ------------------------------------------------------------------
-
-    def _execute_text_parsed(
-        self,
-        messages: list[LLMMessage],
-        tool_calls_made: list[str],
-        max_step_iterations: int = 15,
-    ) -> str:
-        """Execute step using text-parsed tool calling with a multi-turn loop.
-
-        Iterates LLM call → tool execution → observation until the LLM
-        produces a Final Answer or max_step_iterations is reached.
-        This allows the agent to: run a command, see the output, adjust its
-        approach, and run another command — all within a single plan step.
-        """
-        use_stop_words = self.llm.supports_stop_words() if self.llm else False
-        last_tool_result = ""
-
-        for _ in range(max_step_iterations):
-            answer = self.llm.call(
-                messages,
-                callbacks=self.callbacks,
-                from_task=self.task,
-                from_agent=self.agent,
-            )
-
-            if not answer:
-                raise ValueError("Empty response from LLM")
-
-            answer_str = str(answer)
-            formatted = process_llm_response(answer_str, use_stop_words)
-
-            if isinstance(formatted, AgentFinish):
-                return str(formatted.output)
-
-            if isinstance(formatted, AgentAction):
-                tool_calls_made.append(formatted.tool)
-                tool_result = self._execute_text_tool_with_events(formatted)
-                last_tool_result = tool_result
-                # Append the assistant's reasoning + action, then the observation.
-                # _build_observation_message handles vision sentinels so the LLM
-                # receives an image content block instead of raw base64 text.
-                messages.append({"role": "assistant", "content": answer_str})
-                messages.append(self._build_observation_message(tool_result))
-                continue
-
-            # Raw text response with no Final Answer marker — treat as done
-            return answer_str
-
-        # Max iterations reached — return the last tool result we accumulated
-        return last_tool_result
-
-    def _execute_text_tool_with_events(self, formatted: AgentAction) -> str:
-        """Execute text-parsed tool calls with tool usage events."""
-        args_dict = self._parse_tool_args(formatted.tool_input)
-        agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageStartedEvent(
-                tool_name=formatted.tool,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-            ),
-        )
-
-        try:
-            fingerprint_context = {}
-            if (
-                self.agent
-                and hasattr(self.agent, "security_config")
-                and hasattr(self.agent.security_config, "fingerprint")
-            ):
-                fingerprint_context = {
-                    "agent_fingerprint": str(self.agent.security_config.fingerprint)
-                }
-
-            tool_result = execute_tool_and_check_finality(
-                agent_action=formatted,
-                fingerprint_context=fingerprint_context,
-                tools=self.tools,
-                i18n=self._i18n,
-                agent_key=self.agent.key if self.agent else None,
-                agent_role=self.agent.role if self.agent else None,
-                tools_handler=self.tools_handler,
-                task=self.task,
-                agent=self.agent,
-                function_calling_llm=self.function_calling_llm,
-                crew=self.crew,
-            )
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageErrorEvent(
-                    tool_name=formatted.tool,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                    error=e,
-                ),
-            )
-            raise
-
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageFinishedEvent(
-                output=str(tool_result.result),
-                tool_name=formatted.tool,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
-        return str(tool_result.result)
-
-    def _parse_tool_args(self, tool_input: Any) -> dict[str, Any]:
-        """Parse tool args from the parser output into a dict payload for events."""
-        if isinstance(tool_input, dict):
-            return tool_input
-        if isinstance(tool_input, str):
-            stripped_input = tool_input.strip()
-            if not stripped_input:
-                return {}
-            try:
-                parsed = json.loads(stripped_input)
-                if isinstance(parsed, dict):
-                    return parsed
-                return {"input": parsed}
-            except json.JSONDecodeError:
-                return {"input": stripped_input}
-        return {"input": str(tool_input)}
-
-    # ------------------------------------------------------------------
-    # Internal: Vision support
-    # ------------------------------------------------------------------
-
-    @staticmethod
-    def _parse_vision_sentinel(raw: str) -> tuple[str, str] | None:
-        """Parse a VISION_IMAGE sentinel into (media_type, base64_data), or None."""
-        prefix = "VISION_IMAGE:"
-        if not raw.startswith(prefix):
-            return None
-        rest = raw[len(prefix) :]
-        sep = rest.find(":")
-        if sep <= 0:
-            return None
-        return rest[:sep], rest[sep + 1 :]
-
-    @staticmethod
-    def _build_observation_message(tool_result: str) -> LLMMessage:
-        """Build an observation message, converting vision sentinels to image blocks.
-
-        When a tool returns a VISION_IMAGE sentinel (e.g. from read_image),
-        we build a multimodal content block so the LLM can actually *see*
-        the image rather than receiving a wall of base64 text.
-
-        Uses the standard image_url / data-URI format so each LLM provider's
-        SDK (OpenAI, LiteLLM, etc.) handles the provider-specific conversion.
-
-        Format: ``VISION_IMAGE:<media_type>:<base64_data>``
-        """
-        parsed = StepExecutor._parse_vision_sentinel(tool_result)
-        if parsed:
-            media_type, b64_data = parsed
-            return {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": "Observation: Here is the image:"},
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:{media_type};base64,{b64_data}",
-                        },
-                    },
-                ],
-            }
-        return {"role": "user", "content": f"Observation: {tool_result}"}
-
-    def _validate_expected_tool_usage(
-        self,
-        todo: TodoItem,
-        tool_calls_made: list[str],
-    ) -> None:
-        """Fail step execution when a required tool is configured but not called."""
-        expected_tool = getattr(todo, "tool_to_use", None)
-        if not expected_tool:
-            return
-        expected_tool_name = sanitize_tool_name(expected_tool)
-        available_tool_names = {
-            sanitize_tool_name(tool.name)
-            for tool in self.tools
-            if getattr(tool, "name", "")
-        } | set(self._available_functions.keys())
-        if expected_tool_name not in available_tool_names:
-            return
-        called_names = {sanitize_tool_name(name) for name in tool_calls_made}
-        if expected_tool_name not in called_names:
-            raise ValueError(
-                f"Expected tool '{expected_tool_name}' was not called "
-                f"for step {todo.step_number}."
-            )
-
-    def _execute_native(
-        self,
-        messages: list[LLMMessage],
-        tool_calls_made: list[str],
-        max_step_iterations: int = 15,
-    ) -> str:
-        """Execute step using native function calling with a multi-turn loop.
-
-        Iterates LLM call → tool execution → appended results until the LLM
-        returns a text answer (no more tool calls) or max_step_iterations is
-        reached.  This lets the agent run a shell command, observe the output,
-        correct mistakes, and issue follow-up commands — all within one step.
-        """
-        accumulated_results: list[str] = []
-
-        for _ in range(max_step_iterations):
-            answer = self.llm.call(
-                messages,
-                tools=self._openai_tools,
-                callbacks=self.callbacks,
-                from_task=self.task,
-                from_agent=self.agent,
-            )
-
-            if not answer:
-                raise ValueError("Empty response from LLM")
-
-            if isinstance(answer, BaseModel):
-                return answer.model_dump_json()
-
-            if isinstance(answer, list) and answer and is_tool_call_list(answer):
-                # _execute_native_tool_calls appends assistant + tool messages
-                # to `messages` as a side-effect, so the next LLM call will
-                # see the full conversation history including tool outputs.
-                result = self._execute_native_tool_calls(
-                    answer, messages, tool_calls_made
-                )
-                accumulated_results.append(result)
-                continue
-
-            # Text answer → LLM decided the step is done
-            return str(answer)
-
-        # Max iterations reached — return everything we accumulated
-        return "\n".join(filter(None, accumulated_results))
-
-    def _execute_native_tool_calls(
-        self,
-        tool_calls: list[Any],
-        messages: list[LLMMessage],
-        tool_calls_made: list[str],
-    ) -> str:
-        """Execute a batch of native tool calls and return their results.
-
-        Returns the result of the first tool marked result_as_answer if any,
-        otherwise returns all tool results concatenated.
-        """
-        assistant_message, _reports = build_tool_calls_assistant_message(tool_calls)
-        if assistant_message:
-            messages.append(assistant_message)
-
-        tool_results: list[str] = []
-        for tool_call in tool_calls:
-            call_result = execute_single_native_tool_call(
-                tool_call,
-                available_functions=self._available_functions,
-                original_tools=self.original_tools,
-                structured_tools=self.tools,
-                tools_handler=self.tools_handler,
-                agent=self.agent,
-                task=self.task,
-                crew=self.crew,
-                event_source=self,
-                printer=self._printer,
-                verbose=bool(self.agent and self.agent.verbose),
-            )
-
-            if call_result.func_name:
-                tool_calls_made.append(call_result.func_name)
-
-            if call_result.result_as_answer:
-                return str(call_result.result)
-
-            if call_result.tool_message:
-                raw_content = call_result.tool_message.get("content", "")
-                if isinstance(raw_content, str):
-                    parsed = self._parse_vision_sentinel(raw_content)
-                    if parsed:
-                        media_type, b64_data = parsed
-                        # Replace the sentinel with a standard image_url content block.
-                        # Each provider's _format_messages handles conversion to
-                        # its native format (e.g. Anthropic image blocks).
-                        modified: LLMMessage = cast(
-                            LLMMessage, dict(call_result.tool_message)
-                        )
-                        modified["content"] = [
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:{media_type};base64,{b64_data}",
-                                },
-                            }
-                        ]
-                        messages.append(modified)
-                        tool_results.append("[image]")
-                    else:
-                        messages.append(call_result.tool_message)
-                        if raw_content:
-                            tool_results.append(raw_content)
-                else:
-                    messages.append(call_result.tool_message)
-                    if raw_content:
-                        tool_results.append(str(raw_content))
-
-        return "\n".join(tool_results) if tool_results else ""

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.10.2a1"
+    "crewai[tools]==1.10.1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.10.2a1"
+    "crewai[tools]==1.10.1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.10.2a1"
+    "crewai[tools]==1.10.1"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/events/event_listener.py

@@ -75,14 +75,6 @@ from crewai.events.types.mcp_events import (
     MCPToolExecutionFailedEvent,
     MCPToolExecutionStartedEvent,
 )
-from crewai.events.types.observation_events import (
-    GoalAchievedEarlyEvent,
-    PlanRefinementEvent,
-    PlanReplanTriggeredEvent,
-    StepObservationCompletedEvent,
-    StepObservationFailedEvent,
-    StepObservationStartedEvent,
-)
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
@@ -543,64 +535,6 @@ class EventListener(BaseEventListener):
                 event.error,
             )
 
-        # ----------- OBSERVATION EVENTS (Plan-and-Execute) -----------
-
-        @crewai_event_bus.on(StepObservationStartedEvent)
-        def on_step_observation_started(
-            _: Any, event: StepObservationStartedEvent
-        ) -> None:
-            self.formatter.handle_observation_started(
-                event.agent_role,
-                event.step_number,
-                event.step_description,
-            )
-
-        @crewai_event_bus.on(StepObservationCompletedEvent)
-        def on_step_observation_completed(
-            _: Any, event: StepObservationCompletedEvent
-        ) -> None:
-            self.formatter.handle_observation_completed(
-                event.agent_role,
-                event.step_number,
-                event.step_completed_successfully,
-                event.remaining_plan_still_valid,
-                event.key_information_learned,
-                event.needs_full_replan,
-                event.goal_already_achieved,
-            )
-
-        @crewai_event_bus.on(StepObservationFailedEvent)
-        def on_step_observation_failed(
-            _: Any, event: StepObservationFailedEvent
-        ) -> None:
-            self.formatter.handle_observation_failed(
-                event.step_number,
-                event.error,
-            )
-
-        @crewai_event_bus.on(PlanRefinementEvent)
-        def on_plan_refinement(_: Any, event: PlanRefinementEvent) -> None:
-            self.formatter.handle_plan_refinement(
-                event.step_number,
-                event.refined_step_count,
-                event.refinements,
-            )
-
-        @crewai_event_bus.on(PlanReplanTriggeredEvent)
-        def on_plan_replan_triggered(_: Any, event: PlanReplanTriggeredEvent) -> None:
-            self.formatter.handle_plan_replan(
-                event.replan_reason,
-                event.replan_count,
-                event.completed_steps_preserved,
-            )
-
-        @crewai_event_bus.on(GoalAchievedEarlyEvent)
-        def on_goal_achieved_early(_: Any, event: GoalAchievedEarlyEvent) -> None:
-            self.formatter.handle_goal_achieved_early(
-                event.steps_completed,
-                event.steps_remaining,
-            )
-
         # ----------- AGENT LOGGING EVENTS -----------
 
         @crewai_event_bus.on(AgentLogsStartedEvent)

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

@@ -93,14 +93,6 @@ from crewai.events.types.memory_events import (
     MemorySaveFailedEvent,
     MemorySaveStartedEvent,
 )
-from crewai.events.types.observation_events import (
-    GoalAchievedEarlyEvent,
-    PlanRefinementEvent,
-    PlanReplanTriggeredEvent,
-    StepObservationCompletedEvent,
-    StepObservationFailedEvent,
-    StepObservationStartedEvent,
-)
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
@@ -445,39 +437,6 @@ class TraceCollectionListener(BaseEventListener):
         ) -> None:
             self._handle_action_event("agent_reasoning_failed", source, event)
 
-        # Observation events (Plan-and-Execute)
-        @event_bus.on(StepObservationStartedEvent)
-        def on_step_observation_started(
-            source: Any, event: StepObservationStartedEvent
-        ) -> None:
-            self._handle_action_event("step_observation_started", source, event)
-
-        @event_bus.on(StepObservationCompletedEvent)
-        def on_step_observation_completed(
-            source: Any, event: StepObservationCompletedEvent
-        ) -> None:
-            self._handle_action_event("step_observation_completed", source, event)
-
-        @event_bus.on(StepObservationFailedEvent)
-        def on_step_observation_failed(
-            source: Any, event: StepObservationFailedEvent
-        ) -> None:
-            self._handle_action_event("step_observation_failed", source, event)
-
-        @event_bus.on(PlanRefinementEvent)
-        def on_plan_refinement(source: Any, event: PlanRefinementEvent) -> None:
-            self._handle_action_event("plan_refinement", source, event)
-
-        @event_bus.on(PlanReplanTriggeredEvent)
-        def on_plan_replan_triggered(
-            source: Any, event: PlanReplanTriggeredEvent
-        ) -> None:
-            self._handle_action_event("plan_replan_triggered", source, event)
-
-        @event_bus.on(GoalAchievedEarlyEvent)
-        def on_goal_achieved_early(source: Any, event: GoalAchievedEarlyEvent) -> None:
-            self._handle_action_event("goal_achieved_early", source, event)
-
         @event_bus.on(KnowledgeRetrievalStartedEvent)
         def on_knowledge_retrieval_started(
             source: Any, event: KnowledgeRetrievalStartedEvent

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

@@ -1,99 +0,0 @@
-"""Observation events for the Plan-and-Execute architecture.
-
-Emitted during the Observation phase (PLAN-AND-ACT Section 3.3) when the
-PlannerObserver analyzes step execution results and decides on plan
-continuation, refinement, or replanning.
-"""
-
-from typing import Any
-
-from crewai.events.base_events import BaseEvent
-
-
-class ObservationEvent(BaseEvent):
-    """Base event for observation phase events."""
-
-    type: str
-    agent_role: str
-    step_number: int
-    step_description: str = ""
-    from_task: Any | None = None
-    from_agent: Any | None = None
-
-    def __init__(self, **data: Any) -> None:
-        super().__init__(**data)
-        self._set_task_params(data)
-        self._set_agent_params(data)
-
-
-class StepObservationStartedEvent(ObservationEvent):
-    """Emitted when the Planner begins observing a step's result.
-
-    Fires after every step execution, before the observation LLM call.
-    """
-
-    type: str = "step_observation_started"
-
-
-class StepObservationCompletedEvent(ObservationEvent):
-    """Emitted when the Planner finishes observing a step's result.
-
-    Contains the full observation analysis: what was learned, whether
-    the plan is still valid, and what action to take next.
-    """
-
-    type: str = "step_observation_completed"
-    step_completed_successfully: bool = True
-    key_information_learned: str = ""
-    remaining_plan_still_valid: bool = True
-    needs_full_replan: bool = False
-    replan_reason: str | None = None
-    goal_already_achieved: bool = False
-    suggested_refinements: list[str] | None = None
-
-
-class StepObservationFailedEvent(ObservationEvent):
-    """Emitted when the observation LLM call itself fails.
-
-    The system defaults to continuing the plan when this happens,
-    but the event allows monitoring/alerting on observation failures.
-    """
-
-    type: str = "step_observation_failed"
-    error: str = ""
-
-
-class PlanRefinementEvent(ObservationEvent):
-    """Emitted when the Planner refines upcoming step descriptions.
-
-    This is the lightweight refinement path — no full replan, just
-    sharpening pending todo descriptions based on new information.
-    """
-
-    type: str = "plan_refinement"
-    refined_step_count: int = 0
-    refinements: list[str] | None = None
-
-
-class PlanReplanTriggeredEvent(ObservationEvent):
-    """Emitted when the Planner triggers a full replan.
-
-    The remaining plan was deemed fundamentally wrong and will be
-    regenerated from scratch, preserving completed step results.
-    """
-
-    type: str = "plan_replan_triggered"
-    replan_reason: str = ""
-    replan_count: int = 0
-    completed_steps_preserved: int = 0
-
-
-class GoalAchievedEarlyEvent(ObservationEvent):
-    """Emitted when the Planner detects the goal was achieved early.
-
-    Remaining steps will be skipped and execution will finalize.
-    """
-
-    type: str = "goal_achieved_early"
-    steps_remaining: int = 0
-    steps_completed: int = 0

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

@@ -9,7 +9,7 @@ class ReasoningEvent(BaseEvent):
     type: str
     attempt: int = 1
     agent_role: str
-    task_id: str | None = None
+    task_id: str
     task_name: str | None = None
     from_task: Any | None = None
     agent_id: str | None = None

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -936,152 +936,6 @@ To enable tracing, do any one of these:
         )
         self.print_panel(error_content, "❌ Reasoning Error", "red")
 
-    # ----------- OBSERVATION EVENTS (Plan-and-Execute) -----------
-
-    def handle_observation_started(
-        self,
-        agent_role: str,
-        step_number: int,
-        step_description: str,
-    ) -> None:
-        """Handle step observation started event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        content.append("Observation Started\n", style="cyan bold")
-        content.append("Agent: ", style="white")
-        content.append(f"{agent_role}\n", style="cyan")
-        content.append("Step: ", style="white")
-        content.append(f"{step_number}\n", style="cyan")
-        if step_description:
-            desc_preview = step_description[:80] + (
-                "..." if len(step_description) > 80 else ""
-            )
-            content.append("Description: ", style="white")
-            content.append(f"{desc_preview}\n", style="cyan")
-
-        self.print_panel(content, "🔍 Observing Step Result", "cyan")
-
-    def handle_observation_completed(
-        self,
-        agent_role: str,
-        step_number: int,
-        step_completed: bool,
-        plan_valid: bool,
-        key_info: str,
-        needs_replan: bool,
-        goal_achieved: bool,
-    ) -> None:
-        """Handle step observation completed event."""
-        if not self.verbose:
-            return
-
-        if goal_achieved:
-            style = "green"
-            status = "Goal Achieved Early"
-        elif needs_replan:
-            style = "yellow"
-            status = "Replan Needed"
-        elif plan_valid:
-            style = "green"
-            status = "Plan Valid — Continue"
-        else:
-            style = "red"
-            status = "Step Failed"
-
-        content = Text()
-        content.append("Observation Complete\n", style=f"{style} bold")
-        content.append("Step: ", style="white")
-        content.append(f"{step_number}\n", style=style)
-        content.append("Status: ", style="white")
-        content.append(f"{status}\n", style=style)
-        if key_info:
-            info_preview = key_info[:120] + ("..." if len(key_info) > 120 else "")
-            content.append("Learned: ", style="white")
-            content.append(f"{info_preview}\n", style=style)
-
-        self.print_panel(content, "🔍 Observation Result", style)
-
-    def handle_observation_failed(
-        self,
-        step_number: int,
-        error: str,
-    ) -> None:
-        """Handle step observation failure event."""
-        if not self.verbose:
-            return
-
-        error_content = self.create_status_content(
-            "Observation Failed",
-            "Error",
-            "red",
-            Step=str(step_number),
-            Error=error,
-        )
-        self.print_panel(error_content, "❌ Observation Error", "red")
-
-    def handle_plan_refinement(
-        self,
-        step_number: int,
-        refined_count: int,
-        refinements: list[str] | None,
-    ) -> None:
-        """Handle plan refinement event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        content.append("Plan Refined\n", style="cyan bold")
-        content.append("After Step: ", style="white")
-        content.append(f"{step_number}\n", style="cyan")
-        content.append("Steps Updated: ", style="white")
-        content.append(f"{refined_count}\n", style="cyan")
-        if refinements:
-            for r in refinements[:3]:
-                content.append(f"  • {r[:80]}\n", style="white")
-
-        self.print_panel(content, "✏️ Plan Refinement", "cyan")
-
-    def handle_plan_replan(
-        self,
-        reason: str,
-        replan_count: int,
-        preserved_count: int,
-    ) -> None:
-        """Handle plan replan triggered event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        content.append("Full Replan Triggered\n", style="yellow bold")
-        content.append("Reason: ", style="white")
-        content.append(f"{reason}\n", style="yellow")
-        content.append("Replan #: ", style="white")
-        content.append(f"{replan_count}\n", style="yellow")
-        content.append("Preserved Steps: ", style="white")
-        content.append(f"{preserved_count}\n", style="yellow")
-
-        self.print_panel(content, "🔄 Dynamic Replan", "yellow")
-
-    def handle_goal_achieved_early(
-        self,
-        steps_completed: int,
-        steps_remaining: int,
-    ) -> None:
-        """Handle goal achieved early event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        content.append("Goal Achieved Early!\n", style="green bold")
-        content.append("Completed: ", style="white")
-        content.append(f"{steps_completed} steps\n", style="green")
-        content.append("Skipped: ", style="white")
-        content.append(f"{steps_remaining} remaining steps\n", style="green")
-
-        self.print_panel(content, "🎯 Early Goal Achievement", "green")
-
     # ----------- AGENT LOGGING EVENTS -----------
 
     def handle_agent_logs_started(

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

@@ -408,7 +408,7 @@ def human_feedback(
                 emit=list(emit) if emit else None,
                 default_outcome=default_outcome,
                 metadata=metadata or {},
-                llm=llm if isinstance(llm, str) else getattr(llm, "model", None),
+                llm=llm if isinstance(llm, str) else None,
             )
 
             # Determine effective provider:

lib/crewai/src/crewai/flow/persistence/sqlite.py

@@ -72,8 +72,7 @@ class SQLiteFlowPersistence(FlowPersistence):
 
     def init_db(self) -> None:
         """Create the necessary tables if they don't exist."""
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
-            conn.execute("PRAGMA journal_mode=WAL")
+        with sqlite3.connect(self.db_path) as conn:
             # Main state table
             conn.execute(
                 """
@@ -137,7 +136,7 @@ class SQLiteFlowPersistence(FlowPersistence):
                 f"state_data must be either a Pydantic BaseModel or dict, got {type(state_data)}"
             )
 
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
+        with sqlite3.connect(self.db_path) as conn:
             conn.execute(
                 """
             INSERT INTO flow_states (
@@ -164,7 +163,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         Returns:
             The most recent state as a dictionary, or None if no state exists
         """
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
+        with sqlite3.connect(self.db_path) as conn:
             cursor = conn.execute(
                 """
             SELECT state_json
@@ -214,7 +213,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         self.save_state(flow_uuid, context.method_name, state_data)
 
         # Save pending feedback context
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
+        with sqlite3.connect(self.db_path) as conn:
             # Use INSERT OR REPLACE to handle re-triggering feedback on same flow
             conn.execute(
                 """
@@ -249,7 +248,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         # Import here to avoid circular imports
         from crewai.flow.async_feedback.types import PendingFeedbackContext
 
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
+        with sqlite3.connect(self.db_path) as conn:
             cursor = conn.execute(
                 """
             SELECT state_json, context_json
@@ -273,7 +272,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         Args:
             flow_uuid: Unique identifier for the flow instance
         """
-        with sqlite3.connect(self.db_path, timeout=30) as conn:
+        with sqlite3.connect(self.db_path) as conn:
             conn.execute(
                 """
             DELETE FROM pending_feedback

lib/crewai/src/crewai/lite_agent_output.py

@@ -6,27 +6,9 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.planning_types import TodoItem
 from crewai.utilities.types import LLMMessage
 
 
-class TodoExecutionResult(BaseModel):
-    """Summary of a single todo execution."""
-
-    step_number: int = Field(description="Step number in the plan")
-    description: str = Field(description="What the todo was supposed to do")
-    tool_used: str | None = Field(
-        default=None, description="Tool that was used for this step"
-    )
-    status: str = Field(description="Final status: completed, failed, pending")
-    result: str | None = Field(
-        default=None, description="Result or error message from execution"
-    )
-    depends_on: list[int] = Field(
-        default_factory=list, description="Step numbers this depended on"
-    )
-
-
 class LiteAgentOutput(BaseModel):
     """Class that represents the result of a LiteAgent execution."""
 
@@ -42,75 +24,12 @@ class LiteAgentOutput(BaseModel):
     )
     messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
 
-    plan: str | None = Field(
-        default=None, description="The execution plan that was generated, if any"
-    )
-    todos: list[TodoExecutionResult] = Field(
-        default_factory=list,
-        description="List of todos that were executed with their results",
-    )
-    replan_count: int = Field(
-        default=0, description="Number of times the plan was regenerated"
-    )
-    last_replan_reason: str | None = Field(
-        default=None, description="Reason for the last replan, if any"
-    )
-
-    @classmethod
-    def from_todo_items(cls, todo_items: list[TodoItem]) -> list[TodoExecutionResult]:
-        """Convert TodoItem objects to TodoExecutionResult summaries.
-
-        Args:
-            todo_items: List of TodoItem objects from execution.
-
-        Returns:
-            List of TodoExecutionResult summaries.
-        """
-        return [
-            TodoExecutionResult(
-                step_number=item.step_number,
-                description=item.description,
-                tool_used=item.tool_to_use,
-                status=item.status,
-                result=item.result,
-                depends_on=item.depends_on,
-            )
-            for item in todo_items
-        ]
-
     def to_dict(self) -> dict[str, Any]:
         """Convert pydantic_output to a dictionary."""
         if self.pydantic:
             return self.pydantic.model_dump()
         return {}
 
-    @property
-    def completed_todos(self) -> list[TodoExecutionResult]:
-        """Get only the completed todos."""
-        return [t for t in self.todos if t.status == "completed"]
-
-    @property
-    def failed_todos(self) -> list[TodoExecutionResult]:
-        """Get only the failed todos."""
-        return [t for t in self.todos if t.status == "failed"]
-
-    @property
-    def had_plan(self) -> bool:
-        """Check if the agent executed with a plan."""
-        return self.plan is not None or len(self.todos) > 0
-
     def __str__(self) -> str:
         """Return the raw output as a string."""
         return self.raw
-
-    def __repr__(self) -> str:
-        """Return a detailed representation including todo summary."""
-        parts = [f"LiteAgentOutput(role={self.agent_role!r}"]
-        if self.todos:
-            completed = len(self.completed_todos)
-            total = len(self.todos)
-            parts.append(f", todos={completed}/{total} completed")
-        if self.replan_count > 0:
-            parts.append(f", replans={self.replan_count}")
-        parts.append(")")
-        return "".join(parts)

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

@@ -22,12 +22,7 @@ if TYPE_CHECKING:
 
 try:
     from anthropic import Anthropic, AsyncAnthropic, transform_schema
-    from anthropic.types import (
-        Message,
-        TextBlock,
-        ThinkingBlock,
-        ToolUseBlock,
-    )
+    from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
     from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock
     import httpx
 except ImportError:
@@ -36,11 +31,6 @@ except ImportError:
     ) from None
 
 
-TOOL_SEARCH_TOOL_TYPES: Final[tuple[str, ...]] = (
-    "tool_search_tool_regex_20251119",
-    "tool_search_tool_bm25_20251119",
-)
-
 ANTHROPIC_FILES_API_BETA: Final = "files-api-2025-04-14"
 ANTHROPIC_STRUCTURED_OUTPUTS_BETA: Final = "structured-outputs-2025-11-13"
 
@@ -127,22 +117,6 @@ class AnthropicThinkingConfig(BaseModel):
     budget_tokens: int | None = None
 
 
-class AnthropicToolSearchConfig(BaseModel):
-    """Configuration for Anthropic's server-side tool search.
-
-    When enabled, tools marked with defer_loading=True are not loaded into
-    context immediately. Instead, Claude uses the tool search tool to
-    dynamically discover and load relevant tools on-demand.
-
-    Attributes:
-        type: The tool search variant to use.
-            - "regex": Claude constructs regex patterns to search tool names/descriptions.
-            - "bm25": Claude uses natural language queries to search tools.
-    """
-
-    type: Literal["regex", "bm25"] = "bm25"
-
-
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -166,7 +140,6 @@ class AnthropicCompletion(BaseLLM):
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         thinking: AnthropicThinkingConfig | None = None,
         response_format: type[BaseModel] | None = None,
-        tool_search: AnthropicToolSearchConfig | bool | None = None,
         **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
@@ -186,10 +159,6 @@ class AnthropicCompletion(BaseLLM):
             interceptor: HTTP interceptor for modifying requests/responses at transport level.
             response_format: Pydantic model for structured output. When provided, responses
                 will be validated against this model schema.
-            tool_search: Enable Anthropic's server-side tool search. When True, uses "bm25"
-                variant by default. Pass an AnthropicToolSearchConfig to choose "regex" or
-                "bm25". When enabled, tools are automatically marked with defer_loading=True
-                and a tool search tool is injected into the tools list.
             **kwargs: Additional parameters
         """
         super().__init__(
@@ -221,13 +190,6 @@ class AnthropicCompletion(BaseLLM):
         self.thinking = thinking
         self.previous_thinking_blocks: list[ThinkingBlock] = []
         self.response_format = response_format
-        # Tool search config
-        if tool_search is True:
-            self.tool_search = AnthropicToolSearchConfig()
-        elif isinstance(tool_search, AnthropicToolSearchConfig):
-            self.tool_search = tool_search
-        else:
-            self.tool_search = None
         # Model-specific settings
         self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = True
@@ -470,23 +432,10 @@ class AnthropicCompletion(BaseLLM):
         # Handle tools for Claude 3+
         if tools and self.supports_tools:
             converted_tools = self._convert_tools_for_interference(tools)
-
-            # When tool_search is enabled and there are 2+ regular tools,
-            # inject the search tool and mark regular tools with defer_loading.
-            # With only 1 tool there's nothing to search — skip tool search
-            # entirely so the normal forced tool_choice optimisation still works.
-            regular_tools = [
-                t
-                for t in converted_tools
-                if t.get("type", "") not in TOOL_SEARCH_TOOL_TYPES
-            ]
-            if self.tool_search is not None and len(regular_tools) >= 2:
-                converted_tools = self._apply_tool_search(converted_tools)
-
             params["tools"] = converted_tools
 
-            if available_functions and len(regular_tools) == 1:
-                tool_name = regular_tools[0].get("name")
+            if available_functions and len(converted_tools) == 1:
+                tool_name = converted_tools[0].get("name")
                 if tool_name and tool_name in available_functions:
                     params["tool_choice"] = {"type": "tool", "name": tool_name}
 
@@ -505,12 +454,6 @@ class AnthropicCompletion(BaseLLM):
         anthropic_tools = []
 
         for tool in tools:
-            # Pass through tool search tool definitions unchanged
-            tool_type = tool.get("type", "")
-            if tool_type in TOOL_SEARCH_TOOL_TYPES:
-                anthropic_tools.append(tool)
-                continue
-
             if "input_schema" in tool and "name" in tool and "description" in tool:
                 anthropic_tools.append(tool)
                 continue
@@ -523,15 +466,15 @@ class AnthropicCompletion(BaseLLM):
                 logging.error(f"Error converting tool to Anthropic format: {e}")
                 raise e
 
-            anthropic_tool: dict[str, Any] = {
+            anthropic_tool = {
                 "name": name,
                 "description": description,
             }
 
             if parameters and isinstance(parameters, dict):
-                anthropic_tool["input_schema"] = parameters
+                anthropic_tool["input_schema"] = parameters  # type: ignore[assignment]
             else:
-                anthropic_tool["input_schema"] = {
+                anthropic_tool["input_schema"] = {  # type: ignore[assignment]
                     "type": "object",
                     "properties": {},
                     "required": [],
@@ -541,55 +484,6 @@ class AnthropicCompletion(BaseLLM):
 
         return anthropic_tools
 
-    def _apply_tool_search(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
-        """Inject tool search tool and mark regular tools with defer_loading.
-
-        When tool_search is enabled, this method:
-        1. Adds the appropriate tool search tool definition (regex or bm25)
-        2. Marks all regular tools with defer_loading=True so they are only
-           loaded when Claude discovers them via search
-
-        Args:
-            tools: Converted tool definitions in Anthropic format.
-
-        Returns:
-            Updated tools list with tool search tool prepended and
-            regular tools marked as deferred.
-        """
-        if self.tool_search is None:
-            return tools
-
-        # Check if a tool search tool is already present (user passed one manually)
-        has_search_tool = any(
-            t.get("type", "") in TOOL_SEARCH_TOOL_TYPES for t in tools
-        )
-
-        result: list[dict[str, Any]] = []
-
-        if not has_search_tool:
-            # Map config type to API type identifier
-            type_map = {
-                "regex": "tool_search_tool_regex_20251119",
-                "bm25": "tool_search_tool_bm25_20251119",
-            }
-            tool_type = type_map[self.tool_search.type]
-            # Tool search tool names follow the convention: tool_search_tool_{variant}
-            tool_name = f"tool_search_tool_{self.tool_search.type}"
-            result.append({"type": tool_type, "name": tool_name})
-
-        for tool in tools:
-            # Don't modify tool search tools
-            if tool.get("type", "") in TOOL_SEARCH_TOOL_TYPES:
-                result.append(tool)
-                continue
-
-            # Mark regular tools as deferred if not already set
-            if "defer_loading" not in tool:
-                tool = {**tool, "defer_loading": True}
-            result.append(tool)
-
-        return result
-
     def _extract_thinking_block(
         self, content_block: Any
     ) -> ThinkingBlock | dict[str, Any] | None:
@@ -618,50 +512,6 @@ class AnthropicCompletion(BaseLLM):
             return redacted_block
         return None
 
-    @staticmethod
-    def _convert_image_blocks(content: Any) -> Any:
-        """Convert OpenAI-style image_url blocks to Anthropic image blocks.
-
-        Upstream code (e.g. StepExecutor) uses the standard ``image_url``
-        format with a ``data:`` URI.  Anthropic rejects that — it requires
-        ``{"type": "image", "source": {"type": "base64", ...}}``.
-
-        Non-list content and blocks that are not ``image_url`` are passed
-        through unchanged.
-        """
-        if not isinstance(content, list):
-            return content
-
-        converted: list[dict[str, Any]] = []
-        for block in content:
-            if not isinstance(block, dict) or block.get("type") != "image_url":
-                converted.append(block)
-                continue
-
-            image_info = block.get("image_url", {})
-            url = image_info.get("url", "") if isinstance(image_info, dict) else ""
-            if url.startswith("data:") and ";base64," in url:
-                # Parse  data:<media_type>;base64,<data>
-                header, b64_data = url.split(";base64,", 1)
-                media_type = (
-                    header.split("data:", 1)[1] if "data:" in header else "image/png"
-                )
-                converted.append(
-                    {
-                        "type": "image",
-                        "source": {
-                            "type": "base64",
-                            "media_type": media_type,
-                            "data": b64_data,
-                        },
-                    }
-                )
-            else:
-                # Non-data URI — pass through as-is (Anthropic supports url source)
-                converted.append(block)
-
-        return converted
-
     def _format_messages_for_anthropic(
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[LLMMessage], str | None]:
@@ -700,11 +550,10 @@ class AnthropicCompletion(BaseLLM):
                 tool_call_id = message.get("tool_call_id", "")
                 if not tool_call_id:
                     raise ValueError("Tool message missing required tool_call_id")
-                tool_content = self._convert_image_blocks(content) if content else ""
                 tool_result = {
                     "type": "tool_result",
                     "tool_use_id": tool_call_id,
-                    "content": tool_content,
+                    "content": content if content else "",
                 }
                 pending_tool_results.append(tool_result)
             elif role == "assistant":
@@ -763,12 +612,7 @@ class AnthropicCompletion(BaseLLM):
 
                 role_str = role if role is not None else "user"
                 if isinstance(content, list):
-                    formatted_messages.append(
-                        {
-                            "role": role_str,
-                            "content": self._convert_image_blocks(content),
-                        }
-                    )
+                    formatted_messages.append({"role": role_str, "content": content})
                 else:
                     content_str = content if content is not None else ""
                     formatted_messages.append(

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

@@ -1781,7 +1781,6 @@ class BedrockCompletion(BaseLLM):
 
         converse_messages: list[LLMMessage] = []
         system_message: str | None = None
-        pending_tool_results: list[dict[str, Any]] = []
 
         for message in formatted_messages:
             role = message.get("role")
@@ -1795,62 +1794,56 @@ class BedrockCompletion(BaseLLM):
                     system_message += f"\n\n{content}"
                 else:
                     system_message = cast(str, content)
+            elif role == "assistant" and tool_calls:
+                # Convert OpenAI-style tool_calls to Bedrock toolUse format
+                bedrock_content = []
+                for tc in tool_calls:
+                    func = tc.get("function", {})
+                    tool_use_block = {
+                        "toolUse": {
+                            "toolUseId": tc.get("id", f"call_{id(tc)}"),
+                            "name": func.get("name", ""),
+                            "input": func.get("arguments", {})
+                            if isinstance(func.get("arguments"), dict)
+                            else json.loads(func.get("arguments", "{}") or "{}"),
+                        }
+                    }
+                    bedrock_content.append(tool_use_block)
+                converse_messages.append(
+                    {"role": "assistant", "content": bedrock_content}
+                )
             elif role == "tool":
                 if not tool_call_id:
                     raise ValueError("Tool message missing required tool_call_id")
-                pending_tool_results.append(
+                converse_messages.append(
                     {
-                        "toolResult": {
-                            "toolUseId": tool_call_id,
-                            "content": [{"text": str(content) if content else ""}],
-                        }
+                        "role": "user",
+                        "content": [
+                            {
+                                "toolResult": {
+                                    "toolUseId": tool_call_id,
+                                    "content": [
+                                        {"text": str(content) if content else ""}
+                                    ],
+                                }
+                            }
+                        ],
                     }
                 )
             else:
-                if pending_tool_results:
-                    converse_messages.append(
-                        {"role": "user", "content": pending_tool_results}
-                    )
-                    pending_tool_results = []
-
-                if role == "assistant" and tool_calls:
-                    # Convert OpenAI-style tool_calls to Bedrock toolUse format
-                    bedrock_content = []
-                    for tc in tool_calls:
-                        func = tc.get("function", {})
-                        tool_use_block = {
-                            "toolUse": {
-                                "toolUseId": tc.get("id", f"call_{id(tc)}"),
-                                "name": func.get("name", ""),
-                                "input": func.get("arguments", {})
-                                if isinstance(func.get("arguments"), dict)
-                                else json.loads(func.get("arguments", "{}") or "{}"),
-                            }
-                        }
-                        bedrock_content.append(tool_use_block)
-                    converse_messages.append(
-                        {"role": "assistant", "content": bedrock_content}
-                    )
+                # Convert to Converse API format with proper content structure
+                if isinstance(content, list):
+                    # Already formatted as multimodal content blocks
+                    converse_messages.append({"role": role, "content": content})
                 else:
-                    # Convert to Converse API format with proper content structure
-                    if isinstance(content, list):
-                        # Already formatted as multimodal content blocks
-                        converse_messages.append({"role": role, "content": content})
-                    else:
-                        # String content - wrap in text block
-                        text_content = content if content else ""
-                        converse_messages.append(
-                            {"role": role, "content": [{"text": text_content}]}
-                        )
-
-        if pending_tool_results:
-            converse_messages.append({"role": "user", "content": pending_tool_results})
+                    # String content - wrap in text block
+                    text_content = content if content else ""
+                    converse_messages.append(
+                        {"role": role, "content": [{"text": text_content}]}
+                    )
 
         # CRITICAL: Handle model-specific conversation requirements
-        # Cohere and some other models require conversation to end with user message.
-        # Anthropic models on Bedrock also reject assistant messages in the final
-        # position when tools are present ("pre-filling the assistant response is
-        # not supported").
+        # Cohere and some other models require conversation to end with user message
         if converse_messages:
             last_message = converse_messages[-1]
             if last_message["role"] == "assistant":
@@ -1877,20 +1870,6 @@ class BedrockCompletion(BaseLLM):
                             "content": [{"text": "Continue your response."}],
                         }
                     )
-                # Anthropic (Claude) models reject assistant-last messages when
-                # tools are in the request. Append a user message so the
-                # Converse API accepts the payload.
-                elif "anthropic" in self.model.lower() or "claude" in self.model.lower():
-                    converse_messages.append(
-                        {
-                            "role": "user",
-                            "content": [
-                                {
-                                    "text": "Please continue and provide your final answer."
-                                }
-                            ],
-                        }
-                    )
 
         # Ensure first message is from user (required by Converse API)
         if not converse_messages:

lib/crewai/src/crewai/mcp/tool_resolver.py

@@ -22,7 +22,6 @@ from crewai.mcp.config import (
     MCPServerSSE,
     MCPServerStdio,
 )
-from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.mcp.transports.http import HTTPTransport
 from crewai.mcp.transports.sse import SSETransport
 from crewai.mcp.transports.stdio import StdioTransport
@@ -75,9 +74,10 @@ class MCPToolResolver:
             elif isinstance(mcp_config, str):
                 amp_refs.append(self._parse_amp_ref(mcp_config))
             else:
-                tools, clients = self._resolve_native(mcp_config)
+                tools, client = self._resolve_native(mcp_config)
                 all_tools.extend(tools)
-                self._clients.extend(clients)
+                if client:
+                    self._clients.append(client)
 
         if amp_refs:
             tools, clients = self._resolve_amp(amp_refs)
@@ -131,7 +131,7 @@ class MCPToolResolver:
         all_tools: list[BaseTool] = []
         all_clients: list[Any] = []
 
-        resolved_cache: dict[str, tuple[list[BaseTool], list[Any]]] = {}
+        resolved_cache: dict[str, tuple[list[BaseTool], Any | None]] = {}
 
         for slug in unique_slugs:
             config_dict = amp_configs_map.get(slug)
@@ -149,9 +149,10 @@ class MCPToolResolver:
             mcp_server_config = self._build_mcp_config_from_dict(config_dict)
 
             try:
-                tools, clients = self._resolve_native(mcp_server_config)
-                resolved_cache[slug] = (tools, clients)
-                all_clients.extend(clients)
+                tools, client = self._resolve_native(mcp_server_config)
+                resolved_cache[slug] = (tools, client)
+                if client:
+                    all_clients.append(client)
             except Exception as e:
                 crewai_event_bus.emit(
                     self,
@@ -169,9 +170,8 @@ class MCPToolResolver:
 
             slug_tools, _ = cached
             if specific_tool:
-                sanitized = sanitize_tool_name(specific_tool)
                 all_tools.extend(
-                    t for t in slug_tools if t.name.endswith(f"_{sanitized}")
+                    t for t in slug_tools if t.name.endswith(f"_{specific_tool}")
                 )
             else:
                 all_tools.extend(slug_tools)
@@ -198,6 +198,7 @@ class MCPToolResolver:
 
             plus_api = PlusAPI(api_key=get_platform_integration_token())
             response = plus_api.get_mcp_configs(slugs)
+
             if response.status_code == 200:
                 configs: dict[str, dict[str, Any]] = response.json().get("configs", {})
                 return configs
@@ -217,7 +218,6 @@ class MCPToolResolver:
 
     def _resolve_external(self, mcp_ref: str) -> list[BaseTool]:
         """Resolve an HTTPS MCP server URL into tools."""
-        from crewai.tools.base_tool import BaseTool
         from crewai.tools.mcp_tool_wrapper import MCPToolWrapper
 
         if "#" in mcp_ref:
@@ -227,7 +227,6 @@ class MCPToolResolver:
 
         server_params = {"url": server_url}
         server_name = self._extract_server_name(server_url)
-        sanitized_specific_tool = sanitize_tool_name(specific_tool) if specific_tool else None
 
         try:
             tool_schemas = self._get_mcp_tool_schemas(server_params)
@@ -240,7 +239,7 @@ class MCPToolResolver:
 
             tools = []
             for tool_name, schema in tool_schemas.items():
-                if sanitized_specific_tool and tool_name != sanitized_specific_tool:
+                if specific_tool and tool_name != specific_tool:
                     continue
 
                 try:
@@ -272,16 +271,14 @@ class MCPToolResolver:
             )
             return []
 
-    @staticmethod
-    def _create_transport(
-        mcp_config: MCPServerConfig,
-    ) -> tuple[StdioTransport | HTTPTransport | SSETransport, str]:
-        """Create a fresh transport instance from an MCP server config.
+    def _resolve_native(
+        self, mcp_config: MCPServerConfig
+    ) -> tuple[list[BaseTool], Any | None]:
+        """Resolve an ``MCPServerConfig`` into tools, returning the client for cleanup."""
+        from crewai.tools.base_tool import BaseTool
+        from crewai.tools.mcp_native_tool import MCPNativeTool
 
-        Returns a ``(transport, server_name)`` tuple. Each call produces an
-        independent transport so that parallel tool executions never share
-        state.
-        """
+        transport: StdioTransport | HTTPTransport | SSETransport
         if isinstance(mcp_config, MCPServerStdio):
             transport = StdioTransport(
                 command=mcp_config.command,
@@ -295,54 +292,38 @@ class MCPToolResolver:
                 headers=mcp_config.headers,
                 streamable=mcp_config.streamable,
             )
-            server_name = MCPToolResolver._extract_server_name(mcp_config.url)
+            server_name = self._extract_server_name(mcp_config.url)
         elif isinstance(mcp_config, MCPServerSSE):
             transport = SSETransport(
                 url=mcp_config.url,
                 headers=mcp_config.headers,
             )
-            server_name = MCPToolResolver._extract_server_name(mcp_config.url)
+            server_name = self._extract_server_name(mcp_config.url)
         else:
             raise ValueError(f"Unsupported MCP server config type: {type(mcp_config)}")
-        return transport, server_name
 
-    def _resolve_native(
-        self, mcp_config: MCPServerConfig
-    ) -> tuple[list[BaseTool], list[Any]]:
-        """Resolve an ``MCPServerConfig`` into tools.
-
-        Returns ``(tools, clients)`` where *clients* is always empty for
-        native tools (clients are now created on-demand per invocation).
-        A ``client_factory`` closure is passed to each ``MCPNativeTool`` so
-        every call -- even concurrent calls to the *same* tool -- gets its
-        own ``MCPClient`` + transport with no shared mutable state.
-        """
-        from crewai.tools.base_tool import BaseTool
-        from crewai.tools.mcp_native_tool import MCPNativeTool
-
-        discovery_transport, server_name = self._create_transport(mcp_config)
-        discovery_client = MCPClient(
-            transport=discovery_transport,
+        client = MCPClient(
+            transport=transport,
             cache_tools_list=mcp_config.cache_tools_list,
         )
 
         async def _setup_client_and_list_tools() -> list[dict[str, Any]]:
             try:
-                if not discovery_client.connected:
-                    await discovery_client.connect()
+                if not client.connected:
+                    await client.connect()
 
-                tools_list = await discovery_client.list_tools()
+                tools_list = await client.list_tools()
 
                 try:
-                    await discovery_client.disconnect()
+                    await client.disconnect()
                     await asyncio.sleep(0.1)
                 except Exception as e:
                     self._logger.log("error", f"Error during disconnect: {e}")
 
                 return tools_list
             except Exception as e:
-                if discovery_client.connected:
-                    await discovery_client.disconnect()
+                if client.connected:
+                    await client.disconnect()
                     await asyncio.sleep(0.1)
                 raise RuntimeError(
                     f"Error during setup client and list tools: {e}"
@@ -395,13 +376,6 @@ class MCPToolResolver:
                         filtered_tools.append(tool)
                 tools_list = filtered_tools
 
-            def _client_factory() -> MCPClient:
-                transport, _ = self._create_transport(mcp_config)
-                return MCPClient(
-                    transport=transport,
-                    cache_tools_list=mcp_config.cache_tools_list,
-                )
-
             tools = []
             for tool_def in tools_list:
                 tool_name = tool_def.get("name", "")
@@ -422,7 +396,7 @@ class MCPToolResolver:
 
                 try:
                     native_tool = MCPNativeTool(
-                        client_factory=_client_factory,
+                        mcp_client=client,
                         tool_name=tool_name,
                         tool_schema=tool_schema,
                         server_name=server_name,
@@ -433,10 +407,10 @@ class MCPToolResolver:
                     self._logger.log("error", f"Failed to create native MCP tool: {e}")
                     continue
 
-            return cast(list[BaseTool], tools), []
+            return cast(list[BaseTool], tools), client
         except Exception as e:
-            if discovery_client.connected:
-                asyncio.run(discovery_client.disconnect())
+            if client.connected:
+                asyncio.run(client.disconnect())
 
             raise RuntimeError(f"Failed to get native MCP tools: {e}") from e
 

lib/crewai/src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -38,8 +38,7 @@ class KickoffTaskOutputsSQLiteStorage:
             DatabaseOperationError: If database initialization fails due to SQLite errors.
         """
         try:
-            with sqlite3.connect(self.db_path, timeout=30) as conn:
-                conn.execute("PRAGMA journal_mode=WAL")
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -83,7 +82,7 @@ class KickoffTaskOutputsSQLiteStorage:
         """
         inputs = inputs or {}
         try:
-            with sqlite3.connect(self.db_path, timeout=30) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
                 cursor.execute(
@@ -126,7 +125,7 @@ class KickoffTaskOutputsSQLiteStorage:
             DatabaseOperationError: If updating the task output fails due to SQLite errors.
         """
         try:
-            with sqlite3.connect(self.db_path, timeout=30) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
 
@@ -167,7 +166,7 @@ class KickoffTaskOutputsSQLiteStorage:
             DatabaseOperationError: If loading task outputs fails due to SQLite errors.
         """
         try:
-            with sqlite3.connect(self.db_path, timeout=30) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("""
                 SELECT *
@@ -206,7 +205,7 @@ class KickoffTaskOutputsSQLiteStorage:
             DatabaseOperationError: If deleting task outputs fails due to SQLite errors.
         """
         try:
-            with sqlite3.connect(self.db_path, timeout=30) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")

lib/crewai/src/crewai/memory/storage/lancedb_storage.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-from contextlib import AbstractContextManager
 from datetime import datetime
 import json
 import logging
@@ -15,7 +14,6 @@ from typing import Any, ClassVar
 import lancedb
 
 from crewai.memory.types import MemoryRecord, ScopeInfo
-from crewai.utilities.lock_store import lock as store_lock
 
 
 _logger = logging.getLogger(__name__)
@@ -92,7 +90,6 @@ class LanceDBStorage:
         # Raise it proactively so scans on large tables never hit OS error 24.
         try:
             import resource
-
             soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
             if soft < 4096:
                 resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 4096), hard))
@@ -102,8 +99,7 @@ class LanceDBStorage:
         self._compact_every = compact_every
         self._save_count = 0
 
-        self._lock_name = f"lancedb:{self._path.resolve()}"
-
+        # Get or create a shared write lock for this database path.
         resolved = str(self._path.resolve())
         with LanceDBStorage._path_locks_guard:
             if resolved not in LanceDBStorage._path_locks:
@@ -114,13 +110,10 @@ class LanceDBStorage:
         # If no table exists yet, defer creation until the first save so the
         # dimension can be auto-detected from the embedder's actual output.
         try:
-            self._table: lancedb.table.Table | None = self._db.open_table(
-                self._table_name
-            )
+            self._table: lancedb.table.Table | None = self._db.open_table(self._table_name)
             self._vector_dim: int = self._infer_dim_from_table(self._table)
             # Best-effort: create the scope index if it doesn't exist yet.
-            with self._file_lock():
-                self._ensure_scope_index()
+            self._ensure_scope_index()
             # Compact in the background if the table has accumulated many
             # fragments from previous runs (each save() creates one).
             self._compact_if_needed()
@@ -131,8 +124,7 @@ class LanceDBStorage:
         # Explicit dim provided: create the table immediately if it doesn't exist.
         if self._table is None and vector_dim is not None:
             self._vector_dim = vector_dim
-            with self._file_lock():
-                self._table = self._create_table(vector_dim)
+            self._table = self._create_table(vector_dim)
 
     @property
     def write_lock(self) -> threading.RLock:
@@ -157,14 +149,18 @@ class LanceDBStorage:
                     break
         return DEFAULT_VECTOR_DIM
 
-    def _file_lock(self) -> AbstractContextManager[None]:
-        """Return a cross-process lock for serialising writes."""
-        return store_lock(self._lock_name)
+    def _retry_write(self, op: str, *args: Any, **kwargs: Any) -> Any:
+        """Execute a table operation with retry on LanceDB commit conflicts.
 
-    def _do_write(self, op: str, *args: Any, **kwargs: Any) -> Any:
-        """Execute a single table write with retry on commit conflicts.
+        Args:
+            op: Method name on the table object (e.g. "add", "delete").
+            *args, **kwargs: Passed to the table method.
 
-        Caller must already hold the cross-process file lock.
+        LanceDB uses optimistic concurrency: if two transactions overlap,
+        the second to commit fails with an ``OSError`` containing
+        "Commit conflict". This helper retries with exponential backoff,
+        refreshing the table reference before each retry so the retried
+        call uses the latest committed version (not a stale reference).
         """
         delay = _RETRY_BASE_DELAY
         for attempt in range(_MAX_RETRIES + 1):
@@ -175,24 +171,20 @@ class LanceDBStorage:
                     raise
                 _logger.debug(
                     "LanceDB commit conflict on %s (attempt %d/%d), retrying in %.1fs",
-                    op,
-                    attempt + 1,
-                    _MAX_RETRIES,
-                    delay,
+                    op, attempt + 1, _MAX_RETRIES, delay,
                 )
+                # Refresh table to pick up the latest version before retrying.
+                # The next getattr(self._table, op) will use the fresh table.
                 try:
                     self._table = self._db.open_table(self._table_name)
                 except Exception:  # noqa: S110
-                    pass
+                    pass  # table refresh is best-effort
                 time.sleep(delay)
                 delay *= 2
         return None  # unreachable, but satisfies type checker
 
     def _create_table(self, vector_dim: int) -> lancedb.table.Table:
-        """Create a new table with the given vector dimension.
-
-        Caller must already hold the cross-process file lock.
-        """
+        """Create a new table with the given vector dimension."""
         placeholder = [
             {
                 "id": "__schema_placeholder__",
@@ -208,12 +200,8 @@ class LanceDBStorage:
                 "vector": [0.0] * vector_dim,
             }
         ]
-        try:
-            table = self._db.create_table(self._table_name, placeholder)
-        except ValueError:
-            table = self._db.open_table(self._table_name)
-        else:
-            table.delete("id = '__schema_placeholder__'")
+        table = self._db.create_table(self._table_name, placeholder)
+        table.delete("id = '__schema_placeholder__'")
         return table
 
     def _ensure_scope_index(self) -> None:
@@ -260,9 +248,9 @@ class LanceDBStorage:
         """Run ``table.optimize()`` in a background thread, absorbing errors."""
         try:
             if self._table is not None:
-                with self._file_lock():
-                    self._table.optimize()
-                    self._ensure_scope_index()
+                self._table.optimize()
+                # Refresh the scope index so new fragments are covered.
+                self._ensure_scope_index()
         except Exception:
             _logger.debug("LanceDB background compaction failed", exc_info=True)
 
@@ -292,9 +280,7 @@ class LanceDBStorage:
             "last_accessed": record.last_accessed.isoformat(),
             "source": record.source or "",
             "private": record.private,
-            "vector": record.embedding
-            if record.embedding
-            else [0.0] * self._vector_dim,
+            "vector": record.embedding if record.embedding else [0.0] * self._vector_dim,
         }
 
     def _row_to_record(self, row: dict[str, Any]) -> MemoryRecord:
@@ -310,9 +296,7 @@ class LanceDBStorage:
             id=str(row["id"]),
             content=str(row["content"]),
             scope=str(row["scope"]),
-            categories=json.loads(row["categories_str"])
-            if row.get("categories_str")
-            else [],
+            categories=json.loads(row["categories_str"]) if row.get("categories_str") else [],
             metadata=json.loads(row["metadata_str"]) if row.get("metadata_str") else {},
             importance=float(row.get("importance", 0.5)),
             created_at=_parse_dt(row.get("created_at")),
@@ -332,15 +316,16 @@ class LanceDBStorage:
                 dim = len(r.embedding)
                 break
         is_new_table = self._table is None
-        with self._write_lock, self._file_lock():
+        with self._write_lock:
             self._ensure_table(vector_dim=dim)
             rows = [self._record_to_row(r) for r in records]
             for r in rows:
                 if r["vector"] is None or len(r["vector"]) != self._vector_dim:
                     r["vector"] = [0.0] * self._vector_dim
-            self._do_write("add", rows)
-            if is_new_table:
-                self._ensure_scope_index()
+            self._retry_write("add", rows)
+        # Create the scope index on the first save so it covers the initial dataset.
+        if is_new_table:
+            self._ensure_scope_index()
         # Auto-compact every N saves so fragment files don't pile up.
         self._save_count += 1
         if self._compact_every > 0 and self._save_count % self._compact_every == 0:
@@ -348,14 +333,14 @@ class LanceDBStorage:
 
     def update(self, record: MemoryRecord) -> None:
         """Update a record by ID. Preserves created_at, updates last_accessed."""
-        with self._write_lock, self._file_lock():
+        with self._write_lock:
             self._ensure_table()
             safe_id = str(record.id).replace("'", "''")
-            self._do_write("delete", f"id = '{safe_id}'")
+            self._retry_write("delete", f"id = '{safe_id}'")
             row = self._record_to_row(record)
             if row["vector"] is None or len(row["vector"]) != self._vector_dim:
                 row["vector"] = [0.0] * self._vector_dim
-            self._do_write("add", [row])
+            self._retry_write("add", [row])
 
     def touch_records(self, record_ids: list[str]) -> None:
         """Update last_accessed to now for the given record IDs.
@@ -369,11 +354,11 @@ class LanceDBStorage:
         """
         if not record_ids or self._table is None:
             return
-        with self._write_lock, self._file_lock():
+        with self._write_lock:
             now = datetime.utcnow().isoformat()
             safe_ids = [str(rid).replace("'", "''") for rid in record_ids]
             ids_expr = ", ".join(f"'{rid}'" for rid in safe_ids)
-            self._do_write(
+            self._retry_write(
                 "update",
                 where=f"id IN ({ids_expr})",
                 values={"last_accessed": now},
@@ -405,17 +390,13 @@ class LanceDBStorage:
             prefix = scope_prefix.rstrip("/")
             like_val = prefix + "%"
             query = query.where(f"scope LIKE '{like_val}'")
-        results = query.limit(
-            limit * 3 if (categories or metadata_filter) else limit
-        ).to_list()
+        results = query.limit(limit * 3 if (categories or metadata_filter) else limit).to_list()
         out: list[tuple[MemoryRecord, float]] = []
         for row in results:
             record = self._row_to_record(row)
             if categories and not any(c in record.categories for c in categories):
                 continue
-            if metadata_filter and not all(
-                record.metadata.get(k) == v for k, v in metadata_filter.items()
-            ):
+            if metadata_filter and not all(record.metadata.get(k) == v for k, v in metadata_filter.items()):
                 continue
             distance = row.get("_distance", 0.0)
             score = 1.0 / (1.0 + float(distance)) if distance is not None else 1.0
@@ -435,24 +416,20 @@ class LanceDBStorage:
     ) -> int:
         if self._table is None:
             return 0
-        with self._write_lock, self._file_lock():
+        with self._write_lock:
             if record_ids and not (categories or metadata_filter):
                 before = self._table.count_rows()
                 ids_expr = ", ".join(f"'{rid}'" for rid in record_ids)
-                self._do_write("delete", f"id IN ({ids_expr})")
+                self._retry_write("delete", f"id IN ({ids_expr})")
                 return before - self._table.count_rows()
             if categories or metadata_filter:
                 rows = self._scan_rows(scope_prefix)
                 to_delete: list[str] = []
                 for row in rows:
                     record = self._row_to_record(row)
-                    if categories and not any(
-                        c in record.categories for c in categories
-                    ):
+                    if categories and not any(c in record.categories for c in categories):
                         continue
-                    if metadata_filter and not all(
-                        record.metadata.get(k) == v for k, v in metadata_filter.items()
-                    ):
+                    if metadata_filter and not all(record.metadata.get(k) == v for k, v in metadata_filter.items()):
                         continue
                     if older_than and record.created_at >= older_than:
                         continue
@@ -461,7 +438,7 @@ class LanceDBStorage:
                     return 0
                 before = self._table.count_rows()
                 ids_expr = ", ".join(f"'{rid}'" for rid in to_delete)
-                self._do_write("delete", f"id IN ({ids_expr})")
+                self._retry_write("delete", f"id IN ({ids_expr})")
                 return before - self._table.count_rows()
             conditions = []
             if scope_prefix is not None and scope_prefix.strip("/"):
@@ -473,11 +450,11 @@ class LanceDBStorage:
                 conditions.append(f"created_at < '{older_than.isoformat()}'")
             if not conditions:
                 before = self._table.count_rows()
-                self._do_write("delete", "id != ''")
+                self._retry_write("delete", "id != ''")
                 return before - self._table.count_rows()
             where_expr = " AND ".join(conditions)
             before = self._table.count_rows()
-            self._do_write("delete", where_expr)
+            self._retry_write("delete", where_expr)
             return before - self._table.count_rows()
 
     def _scan_rows(
@@ -551,7 +528,7 @@ class LanceDBStorage:
         for row in rows:
             sc = str(row.get("scope", ""))
             if child_prefix and sc.startswith(child_prefix):
-                rest = sc[len(child_prefix) :]
+                rest = sc[len(child_prefix):]
                 first_component = rest.split("/", 1)[0]
                 if first_component:
                     children.add(child_prefix + first_component)
@@ -562,11 +539,7 @@ class LanceDBStorage:
                 pass
             created = row.get("created_at")
             if created:
-                dt = (
-                    datetime.fromisoformat(str(created).replace("Z", "+00:00"))
-                    if isinstance(created, str)
-                    else created
-                )
+                dt = datetime.fromisoformat(str(created).replace("Z", "+00:00")) if isinstance(created, str) else created
                 if isinstance(dt, datetime):
                     if oldest is None or dt < oldest:
                         oldest = dt
@@ -589,7 +562,7 @@ class LanceDBStorage:
         for row in rows:
             sc = str(row.get("scope", ""))
             if sc.startswith(prefix) and sc != (prefix.rstrip("/") or "/"):
-                rest = sc[len(prefix) :]
+                rest = sc[len(prefix):]
                 first_component = rest.split("/", 1)[0]
                 if first_component:
                     children.add(prefix + first_component)
@@ -617,19 +590,17 @@ class LanceDBStorage:
         return info.record_count
 
     def reset(self, scope_prefix: str | None = None) -> None:
-        with self._write_lock, self._file_lock():
-            if scope_prefix is None or scope_prefix.strip("/") == "":
-                if self._table is not None:
-                    self._db.drop_table(self._table_name)
-                self._table = None
-                return
-            if self._table is None:
-                return
-            prefix = scope_prefix.rstrip("/")
-            if prefix:
-                self._do_write(
-                    "delete", f"scope >= '{prefix}' AND scope < '{prefix}/\uffff'"
-                )
+        if scope_prefix is None or scope_prefix.strip("/") == "":
+            if self._table is not None:
+                self._db.drop_table(self._table_name)
+            self._table = None
+            # Dimension is preserved; table will be recreated on next save.
+            return
+        if self._table is None:
+            return
+        prefix = scope_prefix.rstrip("/")
+        if prefix:
+            self._table.delete(f"scope >= '{prefix}' AND scope < '{prefix}/\uFFFF'")
 
     def optimize(self) -> None:
         """Compact the table synchronously and refresh the scope index.
@@ -643,9 +614,8 @@ class LanceDBStorage:
         """
         if self._table is None:
             return
-        with self._write_lock, self._file_lock():
-            self._table.optimize()
-            self._ensure_scope_index()
+        self._table.optimize()
+        self._ensure_scope_index()
 
     async def asave(self, records: list[MemoryRecord]) -> None:
         self.save(records)

lib/crewai/src/crewai/rag/chromadb/factory.py

@@ -1,12 +1,13 @@
 """Factory functions for creating ChromaDB clients."""
 
+from hashlib import md5
 import os
 
 from chromadb import PersistentClient
+import portalocker
 
 from crewai.rag.chromadb.client import ChromaDBClient
 from crewai.rag.chromadb.config import ChromaDBConfig
-from crewai.utilities.lock_store import lock
 
 
 def create_client(config: ChromaDBConfig) -> ChromaDBClient:
@@ -24,8 +25,10 @@ def create_client(config: ChromaDBConfig) -> ChromaDBClient:
 
     persist_dir = config.settings.persist_directory
     os.makedirs(persist_dir, exist_ok=True)
+    lock_id = md5(persist_dir.encode(), usedforsecurity=False).hexdigest()
+    lockfile = os.path.join(persist_dir, f"chromadb-{lock_id}.lock")
 
-    with lock(f"chromadb:{persist_dir}"):
+    with portalocker.Lock(lockfile):
         client = PersistentClient(
             path=persist_dir,
             settings=config.settings,

lib/crewai/src/crewai/tools/mcp_native_tool.py

@@ -1,30 +1,29 @@
 """Native MCP tool wrapper for CrewAI agents.
 
-This module provides a tool wrapper that creates a fresh MCP client for every
-invocation, ensuring safe parallel execution even when the same tool is called
-concurrently by the executor.
+This module provides a tool wrapper that reuses existing MCP client sessions
+for better performance and connection management.
 """
 
 import asyncio
-from collections.abc import Callable
 from typing import Any
 
 from crewai.tools import BaseTool
 
 
 class MCPNativeTool(BaseTool):
-    """Native MCP tool that creates a fresh client per invocation.
+    """Native MCP tool that reuses client sessions.
 
-    A ``client_factory`` callable produces an independent ``MCPClient`` +
-    transport for every ``_run_async`` call.  This guarantees that parallel
-    invocations -- whether of the *same* tool or *different* tools from the
-    same server -- never share mutable connection state (which would cause
-    anyio cancel-scope errors).
+    This tool wrapper is used when agents connect to MCP servers using
+    structured configurations. It reuses existing client sessions for
+    better performance and proper connection lifecycle management.
+
+    Unlike MCPToolWrapper which connects on-demand, this tool uses
+    a shared MCP client instance that maintains a persistent connection.
     """
 
     def __init__(
         self,
-        client_factory: Callable[[], Any],
+        mcp_client: Any,
         tool_name: str,
         tool_schema: dict[str, Any],
         server_name: str,
@@ -33,16 +32,19 @@ class MCPNativeTool(BaseTool):
         """Initialize native MCP tool.
 
         Args:
-            client_factory: Zero-arg callable that returns a new MCPClient.
+            mcp_client: MCPClient instance with active session.
             tool_name: Name of the tool (may be prefixed).
             tool_schema: Schema information for the tool.
             server_name: Name of the MCP server for prefixing.
             original_tool_name: Original name of the tool on the MCP server.
         """
+        # Create tool name with server prefix to avoid conflicts
         prefixed_name = f"{server_name}_{tool_name}"
 
+        # Handle args_schema properly - BaseTool expects a BaseModel subclass
         args_schema = tool_schema.get("args_schema")
 
+        # Only pass args_schema if it's provided
         kwargs = {
             "name": prefixed_name,
             "description": tool_schema.get(
@@ -55,9 +57,16 @@ class MCPNativeTool(BaseTool):
 
         super().__init__(**kwargs)
 
-        self._client_factory = client_factory
+        # Set instance attributes after super().__init__
+        self._mcp_client = mcp_client
         self._original_tool_name = original_tool_name or tool_name
         self._server_name = server_name
+        # self._logger = logging.getLogger(__name__)
+
+    @property
+    def mcp_client(self) -> Any:
+        """Get the MCP client instance."""
+        return self._mcp_client
 
     @property
     def original_tool_name(self) -> str:
@@ -99,26 +108,51 @@ class MCPNativeTool(BaseTool):
     async def _run_async(self, **kwargs) -> str:
         """Async implementation of tool execution.
 
-        A fresh ``MCPClient`` is created for every invocation so that
-        concurrent calls never share transport or session state.
-
         Args:
             **kwargs: Arguments to pass to the MCP tool.
 
         Returns:
             Result from the MCP tool execution.
         """
-        client = self._client_factory()
-        await client.connect()
+        # Note: Since we use asyncio.run() which creates a new event loop each time,
+        # Always reconnect on-demand because asyncio.run() creates new event loops per call
+        # All MCP transport context managers (stdio, streamablehttp_client, sse_client)
+        # use anyio.create_task_group() which can't span different event loops
+        if self._mcp_client.connected:
+            await self._mcp_client.disconnect()
+
+        await self._mcp_client.connect()
 
         try:
-            result = await client.call_tool(self.original_tool_name, kwargs)
-        finally:
-            await client.disconnect()
+            result = await self._mcp_client.call_tool(self.original_tool_name, kwargs)
 
+        except Exception as e:
+            error_str = str(e).lower()
+            if (
+                "not connected" in error_str
+                or "connection" in error_str
+                or "send" in error_str
+            ):
+                await self._mcp_client.disconnect()
+                await self._mcp_client.connect()
+                # Retry the call
+                result = await self._mcp_client.call_tool(
+                    self.original_tool_name, kwargs
+                )
+            else:
+                raise
+
+        finally:
+            # Always disconnect after tool call to ensure clean context manager lifecycle
+            # This prevents "exit cancel scope in different task" errors
+            # All transport context managers must be exited in the same event loop they were entered
+            await self._mcp_client.disconnect()
+
+        # Extract result content
         if isinstance(result, str):
             return result
 
+        # Handle various result formats
         if hasattr(result, "content") and result.content:
             if isinstance(result.content, list) and len(result.content) > 0:
                 content_item = result.content[0]

lib/crewai/src/crewai/translations/en.json

@@ -74,28 +74,9 @@
     "consolidation_user": "New content to consider storing:\n{new_content}\n\nExisting similar memories:\n{records_summary}\n\nReturn the consolidation plan as structured output."
   },
   "reasoning": {
-    "initial_plan": "You are {role}. Create a focused execution plan using only the essential steps needed.",
-    "refine_plan": "You are {role}. Refine your plan to address the specific gap while keeping it minimal.",
-    "create_plan_prompt": "You are {role}.\n\nTask: {description}\n\nExpected output: {expected_output}\n\nAvailable tools: {tools}\n\nCreate a focused plan with ONLY the essential steps needed. Most tasks require just 2-5 steps. Do NOT pad with unnecessary steps like \"review\", \"verify\", \"document\", or \"finalize\" unless explicitly required.\n\nFor each step, specify the action and which tool to use (if any).\n\nConclude with:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
-    "refine_plan_prompt": "Your plan:\n{current_plan}\n\nYou indicated you're not ready. Address the specific gap while keeping the plan minimal.\n\nConclude with READY or NOT READY."
-  },
-  "planning": {
-    "system_prompt": "You are a strategic planning assistant. Create concrete, executable plans where every step produces a verifiable result.",
-    "create_plan_prompt": "Create an execution plan for the following task:\n\n## Task\n{description}\n\n## Expected Output\n{expected_output}\n\n## Available Tools\n{tools}\n\n## Planning Principles\nFocus on CONCRETE, EXECUTABLE steps. Each step must clearly state WHAT ACTION to take and HOW to verify it succeeded. The number of steps should match the task complexity. Hard limit: {max_steps} steps.\n\n## Rules:\n- Each step must have a clear DONE criterion\n- Do NOT group unrelated actions: if steps can fail independently, keep them separate\n- NO standalone \"thinking\" or \"planning\" steps — act, don't just observe\n- The last step must produce the required output\n\nAfter your plan, state READY or NOT READY.",
-    "refine_plan_prompt": "Your previous plan:\n{current_plan}\n\nYou indicated you weren't ready. Refine your plan to address the specific gap.\n\nKeep the plan minimal - only add steps that directly address the issue.\n\nConclude with READY or NOT READY as before.",
-    "observation_system_prompt": "You are a Planning Agent observing execution progress. After each step completes, you analyze what happened and decide whether the remaining plan is still valid.\n\nReason step-by-step about:\n1. Did this step produce a concrete, verifiable result? (file created, command succeeded, service running, etc.) — or did it only explore without acting?\n2. What new information was learned from this step's result?\n3. Whether the remaining steps still make sense given this new information\n4. What refinements, if any, are needed for upcoming steps\n5. Whether the overall goal has already been achieved\n\nCritical: mark `step_completed_successfully=false` if:\n- The step result is only exploratory (ls, pwd, cat) without producing the required artifact or action\n- A command returned a non-zero exit code and the error was not recovered\n- The step description required creating/building/starting something and the result shows it was not done\n\nBe conservative about triggering full replans — only do so when the remaining plan is fundamentally wrong, not just suboptimal.\n\nIMPORTANT: Set step_completed_successfully=false if:\n- The step's stated goal was NOT achieved (even if other things were done)\n- The first meaningful action returned an error (file not found, command not found, etc.)\n- The result is exploration/discovery output rather than the concrete action the step required\n- The step ran out of attempts without producing the required output\nSet needs_full_replan=true if the current plan's remaining steps reference paths or state that don't exist yet and need to be created first.",
-    "observation_user_prompt": "## Original task\n{task_description}\n\n## Expected output\n{task_goal}\n{completed_summary}\n\n## Just completed step {step_number}\nDescription: {step_description}\nResult: {step_result}\n{remaining_summary}\n\nAnalyze this step's result and provide your observation.",
-    "step_executor_system_prompt": "You are {role}. {backstory}\n\nYour goal: {goal}\n\nYou are executing ONE specific step in a larger plan. Your ONLY job is to fully complete this step — not to plan ahead.\n\nKey rules:\n- **ACT FIRST.** Execute the primary action of this step immediately. Do NOT read or explore files before attempting the main action unless exploration IS the step's goal.\n- If the step says 'run X', run X NOW. If it says 'write file Y', write Y NOW.\n- If the step requires producing an output file (e.g. /app/move.txt, report.jsonl, summary.csv), you MUST write that file using a tool call — do NOT just state the answer in text.\n- You may use tools MULTIPLE TIMES. After each tool use, check the result. If it failed, try a different approach.\n- Only output your Final Answer AFTER the concrete outcome is verified (file written, build succeeded, command exited 0).\n- If a command is not found or a path does not exist, fix it (different PATH, install missing deps, use absolute paths).\n- Do NOT spend more than 3 tool calls on exploration/analysis before attempting the primary action.{tools_section}",
-    "step_executor_tools_section": "\n\nAvailable tools: {tool_names}\n\nYou may call tools multiple times in sequence. Use this format for EACH tool call:\nThought: <what you observed and what you will try next>\nAction: <tool_name>\nAction Input: <input>\n\nAfter observing each result, decide: is the step complete? If yes:\nThought: The step is done because <evidence>\nFinal Answer: <concise summary of what was accomplished and the key result>",
-    "step_executor_user_prompt": "## Current Step\n{step_description}",
-    "step_executor_suggested_tool": "\nSuggested tool: {tool_to_use}",
-    "step_executor_context_header": "\n## Context from previous steps:",
-    "step_executor_context_entry": "Step {step_number} result: {result}",
-    "step_executor_complete_step": "\n**Execute the primary action of this step NOW.** If the step requires writing a file, write it. If it requires running a command, run it. Verify the outcome with a follow-up tool call, then give your Final Answer. Your Final Answer must confirm what was DONE (file created at path X, command succeeded), not just what should be done.",
-    "todo_system_prompt": "You are {role}. Your goal: {goal}\n\nYou are executing a specific step in a multi-step plan. Focus only on completing the current step. Use the suggested tool if one is provided. Be concise and provide clear results that can be used by subsequent steps.",
-    "synthesis_system_prompt": "You are {role}. You have completed a multi-step task. Synthesize the results from all steps into a single, coherent final response that directly addresses the original task. Do NOT list step numbers or say 'Step 1 result'. Produce a clean, polished answer as if you did it all at once.",
-    "synthesis_user_prompt": "## Original Task\n{task_description}\n\n## Results from each step\n{combined_steps}\n\nSynthesize these results into a single, coherent final answer.",
-    "replan_enhancement_prompt": "\n\nIMPORTANT: Previous execution attempt did not fully succeed. Please create a revised plan that accounts for the following context from the previous attempt:\n\n{previous_context}\n\nConsider:\n1. What steps succeeded and can be built upon\n2. What steps failed and why they might have failed\n3. Alternative approaches that might work better\n4. Whether dependencies need to be restructured",
-    "step_executor_task_context": "## Task Context\nThe following is the full task you are helping complete. Keep this in mind — especially any required output files, exact filenames, and expected formats.\n\n{task_context}\n\n---\n"
+    "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
+    "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
+    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
+    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
   }
-}
+}

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -3,8 +3,6 @@ from __future__ import annotations
 import asyncio
 from collections.abc import Callable, Sequence
 import concurrent.futures
-from dataclasses import dataclass, field
-from datetime import datetime
 import inspect
 import json
 import re
@@ -41,7 +39,6 @@ from crewai.utilities.types import LLMMessage
 if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.agents.crew_agent_executor import CrewAgentExecutor
-    from crewai.agents.tools_handler import ToolsHandler
     from crewai.experimental.agent_executor import AgentExecutor
     from crewai.lite_agent import LiteAgent
     from crewai.llm import LLM
@@ -338,66 +335,6 @@ def enforce_rpm_limit(
         request_within_rpm_limit()
 
 
-def _prepare_llm_call(
-    executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
-    messages: list[LLMMessage],
-    printer: Printer,
-    verbose: bool = True,
-) -> list[LLMMessage]:
-    """Shared pre-call logic: run before hooks and resolve messages.
-
-    Args:
-        executor_context: Optional executor context for hook invocation.
-        messages: The messages to send to the LLM.
-        printer: Printer instance for output.
-        verbose: Whether to print output.
-
-    Returns:
-        The resolved messages list (may come from executor_context).
-
-    Raises:
-        ValueError: If a before hook blocks the call.
-    """
-    if executor_context is not None:
-        if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
-            raise ValueError("LLM call blocked by before_llm_call hook")
-        messages = executor_context.messages
-    return messages
-
-
-def _validate_and_finalize_llm_response(
-    answer: Any,
-    executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
-    printer: Printer,
-    verbose: bool = True,
-) -> str | BaseModel | Any:
-    """Shared post-call logic: validate response and run after hooks.
-
-    Args:
-        answer: The raw LLM response.
-        executor_context: Optional executor context for hook invocation.
-        printer: Printer instance for output.
-        verbose: Whether to print output.
-
-    Returns:
-        The potentially modified response.
-
-    Raises:
-        ValueError: If the response is None or empty.
-    """
-    if not answer:
-        if verbose:
-            printer.print(
-                content="Received None or empty response from LLM call.",
-                color="red",
-            )
-        raise ValueError("Invalid response from LLM call - None or empty.")
-
-    return _setup_after_llm_call_hooks(
-        executor_context, answer, printer, verbose=verbose
-    )
-
-
 def get_llm_response(
     llm: LLM | BaseLLM,
     messages: list[LLMMessage],
@@ -434,7 +371,11 @@ def get_llm_response(
         Exception: If an error occurs.
         ValueError: If the response is None or empty.
     """
-    messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
+
+    if executor_context is not None:
+        if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
+            raise ValueError("LLM call blocked by before_llm_call hook")
+        messages = executor_context.messages
 
     try:
         answer = llm.call(
@@ -448,9 +389,16 @@ def get_llm_response(
         )
     except Exception as e:
         raise e
+    if not answer:
+        if verbose:
+            printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+        raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return _validate_and_finalize_llm_response(
-        answer, executor_context, printer, verbose=verbose
+    return _setup_after_llm_call_hooks(
+        executor_context, answer, printer, verbose=verbose
     )
 
 
@@ -480,7 +428,6 @@ async def aget_llm_response(
         from_agent: Optional agent context for the LLM call.
         response_model: Optional Pydantic model for structured outputs.
         executor_context: Optional executor context for hook invocation.
-        verbose: Whether to print output.
 
     Returns:
         The response from the LLM as a string, Pydantic model (when response_model is provided),
@@ -490,7 +437,10 @@ async def aget_llm_response(
         Exception: If an error occurs.
         ValueError: If the response is None or empty.
     """
-    messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
+    if executor_context is not None:
+        if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
+            raise ValueError("LLM call blocked by before_llm_call hook")
+        messages = executor_context.messages
 
     try:
         answer = await llm.acall(
@@ -504,9 +454,16 @@ async def aget_llm_response(
         )
     except Exception as e:
         raise e
+    if not answer:
+        if verbose:
+            printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+        raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return _validate_and_finalize_llm_response(
-        answer, executor_context, printer, verbose=verbose
+    return _setup_after_llm_call_hooks(
+        executor_context, answer, printer, verbose=verbose
     )
 
 
@@ -1200,386 +1157,6 @@ def extract_tool_call_info(
     return None
 
 
-def is_tool_call_list(response: list[Any]) -> bool:
-    """Check if a response from the LLM is a list of tool calls.
-
-    Supports OpenAI, Anthropic, Bedrock, and Gemini formats.
-
-    Args:
-        response: The response to check.
-
-    Returns:
-        True if the response appears to be a list of tool calls.
-    """
-    if not response:
-        return False
-    first_item = response[0]
-    # OpenAI-style
-    if hasattr(first_item, "function") or (
-        isinstance(first_item, dict) and "function" in first_item
-    ):
-        return True
-    # Anthropic-style (ToolUseBlock)
-    if hasattr(first_item, "type") and getattr(first_item, "type", None) == "tool_use":
-        return True
-    if hasattr(first_item, "name") and hasattr(first_item, "input"):
-        return True
-    # Bedrock-style
-    if isinstance(first_item, dict) and "name" in first_item and "input" in first_item:
-        return True
-    # Gemini-style
-    if hasattr(first_item, "function_call") and first_item.function_call:
-        return True
-    return False
-
-
-def check_native_tool_support(llm: Any, original_tools: list[BaseTool] | None) -> bool:
-    """Check if the LLM supports native function calling and tools are available.
-
-    Args:
-        llm: The LLM instance.
-        original_tools: Original BaseTool instances.
-
-    Returns:
-        True if native function calling is supported and tools exist.
-    """
-    return (
-        hasattr(llm, "supports_function_calling")
-        and callable(getattr(llm, "supports_function_calling", None))
-        and llm.supports_function_calling()
-        and bool(original_tools)
-    )
-
-
-def setup_native_tools(
-    original_tools: list[BaseTool],
-) -> tuple[
-    list[dict[str, Any]],
-    dict[str, Callable[..., Any]],
-    dict[str, BaseTool | CrewStructuredTool],
-]:
-    """Convert tools to OpenAI schema format for native function calling.
-
-    Args:
-        original_tools: Original BaseTool instances.
-
-    Returns:
-        Tuple of (openai_tools_schema, available_functions_dict, tool_name_mapping).
-    """
-    return convert_tools_to_openai_schema(original_tools)
-
-
-def build_tool_calls_assistant_message(
-    tool_calls: list[Any],
-) -> tuple[LLMMessage | None, list[dict[str, Any]]]:
-    """Build an assistant message containing tool call reports.
-
-    Extracts info from each tool call, builds the standard assistant message
-    format, and preserves raw Gemini parts when applicable.
-
-    Args:
-        tool_calls: Raw tool call objects from the LLM response.
-
-    Returns:
-        Tuple of (assistant_message, tool_calls_to_report).
-        assistant_message is None if no valid tool calls found.
-    """
-    tool_calls_to_report: list[dict[str, Any]] = []
-    for tool_call in tool_calls:
-        info = extract_tool_call_info(tool_call)
-        if not info:
-            continue
-        call_id, func_name, func_args = info
-        tool_calls_to_report.append(
-            {
-                "id": call_id,
-                "type": "function",
-                "function": {
-                    "name": func_name,
-                    "arguments": func_args
-                    if isinstance(func_args, str)
-                    else json.dumps(func_args),
-                },
-            }
-        )
-
-    if not tool_calls_to_report:
-        return None, []
-
-    assistant_message: LLMMessage = {
-        "role": "assistant",
-        "content": None,
-        "tool_calls": tool_calls_to_report,
-    }
-    # Preserve raw parts for Gemini compatibility
-    if all(type(tc).__qualname__ == "Part" for tc in tool_calls):
-        assistant_message["raw_tool_call_parts"] = list(tool_calls)
-
-    return assistant_message, tool_calls_to_report
-
-
-@dataclass
-class NativeToolCallResult:
-    """Result from executing a single native tool call."""
-
-    call_id: str
-    func_name: str
-    result: str
-    from_cache: bool = False
-    result_as_answer: bool = False
-    tool_message: LLMMessage = field(default_factory=dict)  # type: ignore[assignment]
-
-
-def execute_single_native_tool_call(
-    tool_call: Any,
-    *,
-    available_functions: dict[str, Callable[..., Any]],
-    original_tools: list[BaseTool],
-    structured_tools: list[CrewStructuredTool] | None,
-    tools_handler: ToolsHandler | None,
-    agent: Agent | None,
-    task: Task | None,
-    crew: Any | None,
-    event_source: Any,
-    printer: Printer | None = None,
-    verbose: bool = False,
-) -> NativeToolCallResult:
-    """Execute a single native tool call with full lifecycle management.
-
-    Handles: arg parsing, tool lookup, max-usage check, cache read/write,
-    before/after hooks, event emission, and result_as_answer detection.
-
-    Args:
-        tool_call: Raw tool call object from the LLM.
-        available_functions: Map of sanitized tool name -> callable.
-        original_tools: Original BaseTool list (for cache_function, result_as_answer).
-        structured_tools: Structured tools list (for hook context).
-        tools_handler: Optional handler with cache.
-        agent: The agent instance.
-        task: The current task.
-        crew: The crew instance.
-        event_source: The object to use as event emitter source.
-        printer: Optional printer for verbose logging.
-        verbose: Whether to print verbose output.
-
-    Returns:
-        NativeToolCallResult with all execution details.
-    """
-    from crewai.events.event_bus import crewai_event_bus
-    from crewai.events.types.tool_usage_events import (
-        ToolUsageErrorEvent,
-        ToolUsageFinishedEvent,
-        ToolUsageStartedEvent,
-    )
-    from crewai.hooks.tool_hooks import (
-        ToolCallHookContext,
-        get_after_tool_call_hooks,
-        get_before_tool_call_hooks,
-    )
-
-    info = extract_tool_call_info(tool_call)
-    if not info:
-        return NativeToolCallResult(
-            call_id="", func_name="", result="Unrecognized tool call format"
-        )
-
-    call_id, func_name, func_args = info
-
-    # Parse arguments
-    if isinstance(func_args, str):
-        try:
-            args_dict = json.loads(func_args)
-        except json.JSONDecodeError:
-            args_dict = {}
-    else:
-        args_dict = func_args
-
-    agent_key = getattr(agent, "key", "unknown") if agent else "unknown"
-
-    # Find original tool for cache_function and result_as_answer
-    original_tool: BaseTool | None = None
-    for tool in original_tools:
-        if sanitize_tool_name(tool.name) == func_name:
-            original_tool = tool
-            break
-
-    # Check max usage count
-    max_usage_reached = False
-    if (
-        original_tool
-        and original_tool.max_usage_count is not None
-        and original_tool.current_usage_count >= original_tool.max_usage_count
-    ):
-        max_usage_reached = True
-
-    # Check cache
-    from_cache = False
-    input_str = json.dumps(args_dict) if args_dict else ""
-    result = "Tool not found"
-
-    if tools_handler and tools_handler.cache:
-        cached_result = tools_handler.cache.read(tool=func_name, input=input_str)
-        if cached_result is not None:
-            result = (
-                str(cached_result)
-                if not isinstance(cached_result, str)
-                else cached_result
-            )
-            from_cache = True
-
-    # Emit tool started event
-    started_at = datetime.now()
-    crewai_event_bus.emit(
-        event_source,
-        event=ToolUsageStartedEvent(
-            tool_name=func_name,
-            tool_args=args_dict,
-            from_agent=agent,
-            from_task=task,
-            agent_key=agent_key,
-        ),
-    )
-
-    track_delegation_if_needed(func_name, args_dict, task)
-
-    # Find structured tool for hooks
-    structured_tool: CrewStructuredTool | None = None
-    for structured in structured_tools or []:
-        if sanitize_tool_name(structured.name) == func_name:
-            structured_tool = structured
-            break
-
-    # Before hooks
-    hook_blocked = False
-    before_hook_context = ToolCallHookContext(
-        tool_name=func_name,
-        tool_input=args_dict,
-        tool=structured_tool,  # type: ignore[arg-type]
-        agent=agent,
-        task=task,
-        crew=crew,
-    )
-    try:
-        for hook in get_before_tool_call_hooks():
-            if hook(before_hook_context) is False:
-                hook_blocked = True
-                break
-    except Exception:  # noqa: S110
-        pass
-
-    error_event_emitted = False
-    if hook_blocked:
-        result = f"Tool execution blocked by hook. Tool: {func_name}"
-    elif not from_cache and not max_usage_reached:
-        if func_name in available_functions:
-            try:
-                tool_func = available_functions[func_name]
-                raw_result = tool_func(**args_dict)
-
-                # Cache result
-                if tools_handler and tools_handler.cache:
-                    should_cache = True
-                    if original_tool:
-                        should_cache = original_tool.cache_function(
-                            args_dict, raw_result
-                        )
-                    if should_cache:
-                        tools_handler.cache.add(
-                            tool=func_name, input=input_str, output=raw_result
-                        )
-
-                result = (
-                    str(raw_result) if not isinstance(raw_result, str) else raw_result
-                )
-            except Exception as e:
-                result = f"Error executing tool: {e}"
-                if task:
-                    task.increment_tools_errors()
-                crewai_event_bus.emit(
-                    event_source,
-                    event=ToolUsageErrorEvent(
-                        tool_name=func_name,
-                        tool_args=args_dict,
-                        from_agent=agent,
-                        from_task=task,
-                        agent_key=agent_key,
-                        error=e,
-                    ),
-                )
-                error_event_emitted = True
-    elif max_usage_reached and original_tool:
-        result = (
-            f"Tool '{func_name}' has reached its usage limit of "
-            f"{original_tool.max_usage_count} times and cannot be used anymore."
-        )
-
-    # After hooks
-    after_hook_context = ToolCallHookContext(
-        tool_name=func_name,
-        tool_input=args_dict,
-        tool=structured_tool,  # type: ignore[arg-type]
-        agent=agent,
-        task=task,
-        crew=crew,
-        tool_result=result,
-    )
-    try:
-        for after_hook in get_after_tool_call_hooks():
-            hook_result = after_hook(after_hook_context)
-            if hook_result is not None:
-                result = hook_result
-                after_hook_context.tool_result = result
-    except Exception:  # noqa: S110
-        pass
-
-    # Emit tool finished event (only if error event wasn't already emitted)
-    if not error_event_emitted:
-        crewai_event_bus.emit(
-            event_source,
-            event=ToolUsageFinishedEvent(
-                output=result,
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=agent,
-                from_task=task,
-                agent_key=agent_key,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
-
-    # Build tool result message
-    tool_message: LLMMessage = {
-        "role": "tool",
-        "tool_call_id": call_id,
-        "name": func_name,
-        "content": result,
-    }
-
-    if verbose and printer:
-        cache_info = " (from cache)" if from_cache else ""
-        printer.print(
-            content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
-            color="green",
-        )
-
-    # Check result_as_answer
-    is_result_as_answer = bool(
-        original_tool
-        and hasattr(original_tool, "result_as_answer")
-        and original_tool.result_as_answer
-    )
-
-    return NativeToolCallResult(
-        call_id=call_id,
-        func_name=func_name,
-        result=result,
-        from_cache=from_cache,
-        result_as_answer=is_result_as_answer,
-        tool_message=tool_message,
-    )
-
-
 def parse_tool_call_args(
     func_args: dict[str, Any] | str,
     func_name: str,

lib/crewai/src/crewai/utilities/i18n.py

@@ -100,13 +100,7 @@ class I18N(BaseModel):
     def retrieve(
         self,
         kind: Literal[
-            "slices",
-            "errors",
-            "tools",
-            "reasoning",
-            "planning",
-            "hierarchical_manager_agent",
-            "memory",
+            "slices", "errors", "tools", "reasoning", "hierarchical_manager_agent", "memory"
         ],
         key: str,
     ) -> str:

lib/crewai/src/crewai/utilities/lock_store.py

@@ -1,61 +0,0 @@
-"""Centralised lock factory.
-
-If ``REDIS_URL`` is set, locks are distributed via ``portalocker.RedisLock``. Otherwise, falls
-back to the standard ``portalocker.Lock``.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Iterator
-from contextlib import contextmanager
-from functools import lru_cache
-from hashlib import md5
-import os
-import tempfile
-from typing import TYPE_CHECKING, Final
-
-import portalocker
-
-
-if TYPE_CHECKING:
-    import redis
-
-
-_REDIS_URL: str | None = os.environ.get("REDIS_URL")
-
-_DEFAULT_TIMEOUT: Final[int] = 120
-
-
-@lru_cache(maxsize=1)
-def _redis_connection() -> redis.Redis:
-    """Return a cached Redis connection, creating one on first call."""
-    from redis import Redis
-
-    if _REDIS_URL is None:
-        raise ValueError("REDIS_URL environment variable is not set")
-    return Redis.from_url(_REDIS_URL)
-
-
-@contextmanager
-def lock(name: str, *, timeout: float = _DEFAULT_TIMEOUT) -> Iterator[None]:
-    """Acquire a named lock, yielding while it is held.
-
-    Args:
-        name: A human-readable lock name (e.g. ``"chromadb_init"``).
-              Automatically namespaced to avoid collisions.
-        timeout: Maximum seconds to wait for the lock before raising.
-    """
-    channel = f"crewai:{md5(name.encode(), usedforsecurity=False).hexdigest()}"
-
-    if _REDIS_URL:
-        with portalocker.RedisLock(
-            channel=channel,
-            connection=_redis_connection(),
-            timeout=timeout,
-        ):
-            yield
-    else:
-        lock_dir = tempfile.gettempdir()
-        lock_path = os.path.join(lock_dir, f"{channel}.lock")
-        with portalocker.Lock(lock_path, timeout=timeout):
-            yield

lib/crewai/src/crewai/utilities/planning_types.py

@@ -1,278 +0,0 @@
-"""Types for agent planning and todo tracking."""
-
-from __future__ import annotations
-
-from typing import Literal
-from uuid import uuid4
-
-from pydantic import BaseModel, Field, field_validator
-
-
-# Todo status type
-TodoStatus = Literal["pending", "running", "completed", "failed"]
-
-
-class PlanStep(BaseModel):
-    """A single step in the reasoning plan."""
-
-    step_number: int = Field(description="Step number (1-based)")
-    description: str = Field(description="What to do in this step")
-    tool_to_use: str | None = Field(
-        default=None, description="Tool to use for this step, if any"
-    )
-    depends_on: list[int] = Field(
-        default_factory=list, description="Step numbers this step depends on"
-    )
-
-
-class TodoItem(BaseModel):
-    """A single todo item representing a step in the execution plan."""
-
-    id: str = Field(default_factory=lambda: str(uuid4()))
-    step_number: int = Field(description="Order of this step in the plan (1-based)")
-    description: str = Field(description="What needs to be done")
-    tool_to_use: str | None = Field(
-        default=None, description="Tool to use for this step, if any"
-    )
-    status: TodoStatus = Field(default="pending", description="Current status")
-    depends_on: list[int] = Field(
-        default_factory=list, description="Step numbers this depends on"
-    )
-    result: str | None = Field(
-        default=None, description="Result after completion, if any"
-    )
-
-
-class TodoList(BaseModel):
-    """Collection of todos for tracking plan execution."""
-
-    items: list[TodoItem] = Field(default_factory=list)
-
-    @property
-    def current_todo(self) -> TodoItem | None:
-        """Get the currently running todo item."""
-        for item in self.items:
-            if item.status == "running":
-                return item
-        return None
-
-    @property
-    def next_pending(self) -> TodoItem | None:
-        """Get the next pending todo item."""
-        for item in self.items:
-            if item.status == "pending":
-                return item
-        return None
-
-    @property
-    def is_complete(self) -> bool:
-        """Check if all todos are in a terminal state (completed or failed)."""
-        return len(self.items) > 0 and all(
-            item.status in ("completed", "failed") for item in self.items
-        )
-
-    @property
-    def pending_count(self) -> int:
-        """Count of pending todos."""
-        return sum(1 for item in self.items if item.status == "pending")
-
-    @property
-    def completed_count(self) -> int:
-        """Count of completed todos."""
-        return sum(1 for item in self.items if item.status == "completed")
-
-    def get_by_step_number(self, step_number: int) -> TodoItem | None:
-        """Get a todo by its step number."""
-        for item in self.items:
-            if item.step_number == step_number:
-                return item
-        return None
-
-    def mark_running(self, step_number: int) -> None:
-        """Mark a todo as running by step number."""
-        item = self.get_by_step_number(step_number)
-        if item:
-            item.status = "running"
-
-    def mark_completed(self, step_number: int, result: str | None = None) -> None:
-        """Mark a todo as completed by step number."""
-        item = self.get_by_step_number(step_number)
-        if item:
-            item.status = "completed"
-            if result is not None:
-                item.result = result
-
-    def mark_failed(self, step_number: int, result: str | None = None) -> None:
-        """Mark a todo as failed by step number."""
-        item = self.get_by_step_number(step_number)
-        if item:
-            item.status = "failed"
-            if result is not None:
-                item.result = result
-
-    def _dependencies_satisfied(self, item: TodoItem) -> bool:
-        """Check if all dependencies for a todo item are in a terminal state.
-
-        A dependency is satisfied when it has finished executing — either
-        successfully (completed) or not (failed). This prevents downstream
-        todos from being permanently blocked when a dependency fails.
-        The executor/observer is responsible for deciding whether to skip,
-        replan, or continue when a dependency has failed.
-
-        Args:
-            item: The todo item to check dependencies for.
-
-        Returns:
-            True if all dependencies are in a terminal state, False otherwise.
-        """
-        for dep_num in item.depends_on:
-            dep = self.get_by_step_number(dep_num)
-            if dep is None or dep.status not in ("completed", "failed"):
-                return False
-        return True
-
-    def get_ready_todos(self) -> list[TodoItem]:
-        """Get all todos that are ready to execute (pending with satisfied dependencies).
-
-        Returns:
-            List of TodoItem objects that can be executed now.
-        """
-        ready: list[TodoItem] = []
-        for item in self.items:
-            if item.status != "pending":
-                continue
-            if self._dependencies_satisfied(item):
-                ready.append(item)
-        return ready
-
-    @property
-    def can_parallelize(self) -> bool:
-        """Check if multiple todos can run in parallel.
-
-        Returns:
-            True if more than one todo is ready to execute.
-        """
-        return len(self.get_ready_todos()) > 1
-
-    @property
-    def running_count(self) -> int:
-        """Count of currently running todos."""
-        return sum(1 for item in self.items if item.status == "running")
-
-    def get_completed_todos(self) -> list[TodoItem]:
-        """Get all completed todos.
-
-        Returns:
-            List of completed TodoItem objects.
-        """
-        return [item for item in self.items if item.status == "completed"]
-
-    def get_failed_todos(self) -> list[TodoItem]:
-        """Get all failed todos.
-
-        Returns:
-            List of failed TodoItem objects.
-        """
-        return [item for item in self.items if item.status == "failed"]
-
-    def get_pending_todos(self) -> list[TodoItem]:
-        """Get all pending todos.
-
-        Returns:
-            List of pending TodoItem objects.
-        """
-        return [item for item in self.items if item.status == "pending"]
-
-    def replace_pending_todos(self, new_items: list[TodoItem]) -> None:
-        """Replace all pending todos with new items.
-
-        Preserves completed, failed, and running todos, replaces only pending ones.
-        Used during replanning to swap in a new plan for remaining work.
-
-        Args:
-            new_items: The new todo items to replace pending ones.
-        """
-        non_pending = [item for item in self.items if item.status != "pending"]
-        self.items = non_pending + new_items
-
-
-class StepRefinement(BaseModel):
-    """A structured in-place update for a single pending step.
-
-    Returned as part of StepObservation when the Planner learns new
-    information that makes a pending step description more specific.
-    Applied directly — no second LLM call required.
-    """
-
-    step_number: int = Field(description="The step number to update (1-based)")
-    new_description: str = Field(
-        description="The updated, more specific description for this step"
-    )
-
-
-class StepObservation(BaseModel):
-    """Planner's observation after a step execution completes.
-
-    Returned by the PlannerObserver after EVERY step — not just failures.
-    The Planner uses this to decide whether to continue, refine, or replan.
-
-    Based on PLAN-AND-ACT (Section 3.3): the Planner observes what the Executor
-    did and incorporates new information into the remaining plan.
-
-    Attributes:
-        step_completed_successfully: Whether the step achieved its objective.
-        key_information_learned: New information revealed by this step
-            (e.g., "Found 3 products: A, B, C"). Used to refine upcoming steps.
-        remaining_plan_still_valid: Whether pending todos still make sense
-            given the new information. True does NOT mean no refinement needed.
-        suggested_refinements: Structured in-place updates to pending step
-            descriptions. Each entry targets a specific step by number. These
-            are applied directly without a second LLM call.
-            Example: [{"step_number": 3, "new_description": "Select product B (highest rated)"}]
-        needs_full_replan: The remaining plan is fundamentally wrong and must
-            be regenerated from scratch. Mutually exclusive with
-            remaining_plan_still_valid (if this is True, that should be False).
-        replan_reason: Explanation of why a full replan is needed (None if not).
-        goal_already_achieved: The overall task goal has been satisfied early.
-            No more steps needed — skip remaining todos and finalize.
-    """
-
-    step_completed_successfully: bool = Field(
-        description="Whether the step achieved what it was asked to do"
-    )
-    key_information_learned: str = Field(
-        default="",
-        description="What new information this step revealed",
-    )
-    remaining_plan_still_valid: bool = Field(
-        default=True,
-        description="Whether the remaining pending todos still make sense given new information",
-    )
-    suggested_refinements: list[StepRefinement] | None = Field(
-        default=None,
-        description=(
-            "Structured updates to pending step descriptions based on new information. "
-            "Each entry specifies a step_number and new_description. "
-            "Applied directly — no separate replan needed."
-        ),
-    )
-
-    @field_validator("suggested_refinements", mode="before")
-    @classmethod
-    def coerce_single_refinement_to_list(cls, v):
-        """Coerce a single dict refinement into a list to handle LLM returning a single object."""
-        if isinstance(v, dict):
-            return [v]
-        return v
-    needs_full_replan: bool = Field(
-        default=False,
-        description="The remaining plan is fundamentally wrong and must be regenerated",
-    )
-    replan_reason: str | None = Field(
-        default=None,
-        description="Explanation of why a full replan is needed",
-    )
-    goal_already_achieved: bool = Field(
-        default=False,
-        description="The overall task goal has been satisfied early; no more steps needed",
-    )

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -1,13 +1,10 @@
-"""Handles planning/reasoning for agents before task execution."""
-
-from __future__ import annotations
-
 import json
 import logging
-from typing import TYPE_CHECKING, Any, Final, Literal, cast
+from typing import Any, Final, Literal, cast
 
 from pydantic import BaseModel, Field
 
+from crewai.agent import Agent
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
@@ -15,24 +12,14 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningStartedEvent,
 )
 from crewai.llm import LLM
-from crewai.utilities.llm_utils import create_llm
-from crewai.utilities.planning_types import PlanStep
+from crewai.task import Task
 from crewai.utilities.string_utils import sanitize_tool_name
 
 
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.agent.planning_config import PlanningConfig
-    from crewai.task import Task
-
-
 class ReasoningPlan(BaseModel):
     """Model representing a reasoning plan for a task."""
 
     plan: str = Field(description="The detailed reasoning plan for the task.")
-    steps: list[PlanStep] = Field(
-        default_factory=list, description="Structured steps to execute"
-    )
     ready: bool = Field(description="Whether the agent is ready to execute the task.")
 
 
@@ -42,63 +29,24 @@ class AgentReasoningOutput(BaseModel):
     plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
 
 
-# Aliases for backward compatibility
-PlanningPlan = ReasoningPlan
-AgentPlanningOutput = AgentReasoningOutput
-
-
 FUNCTION_SCHEMA: Final[dict[str, Any]] = {
     "type": "function",
     "function": {
         "name": "create_reasoning_plan",
-        "description": "Create or refine a reasoning plan for a task with structured steps",
+        "description": "Create or refine a reasoning plan for a task",
         "parameters": {
             "type": "object",
             "properties": {
                 "plan": {
                     "type": "string",
-                    "description": "A brief summary of the overall plan.",
-                },
-                "steps": {
-                    "type": "array",
-                    "description": "List of discrete steps to execute the plan",
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "step_number": {
-                                "type": "integer",
-                                "description": "Step number (1-based)",
-                            },
-                            "description": {
-                                "type": "string",
-                                "description": "What to do in this step",
-                            },
-                            "tool_to_use": {
-                                "type": ["string", "null"],
-                                "description": "Tool to use for this step, or null if no tool needed",
-                            },
-                            "depends_on": {
-                                "type": "array",
-                                "items": {"type": "integer"},
-                                "description": "Step numbers this step depends on (empty array if none)",
-                            },
-                        },
-                        "required": [
-                            "step_number",
-                            "description",
-                            "tool_to_use",
-                            "depends_on",
-                        ],
-                        "additionalProperties": False,
-                    },
+                    "description": "The detailed reasoning plan for the task.",
                 },
                 "ready": {
                     "type": "boolean",
                     "description": "Whether the agent is ready to execute the task.",
                 },
             },
-            "required": ["plan", "steps", "ready"],
-            "additionalProperties": False,
+            "required": ["plan", "ready"],
         },
     },
 }
@@ -106,101 +54,41 @@ FUNCTION_SCHEMA: Final[dict[str, Any]] = {
 
 class AgentReasoning:
     """
-    Handles the agent planning/reasoning process, enabling an agent to reflect
-    and create a plan before executing a task.
+    Handles the agent reasoning process, enabling an agent to reflect and create a plan
+    before executing a task.
 
     Attributes:
-        task: The task for which the agent is planning (optional).
-        agent: The agent performing the planning.
-        config: The planning configuration.
-        llm: The language model used for planning.
+        task: The task for which the agent is reasoning.
+        agent: The agent performing the reasoning.
+        llm: The language model used for reasoning.
         logger: Logger for logging events and errors.
-        description: Task description or input text for planning.
-        expected_output: Expected output description.
     """
 
-    def __init__(
-        self,
-        agent: Agent,
-        task: Task | None = None,
-        *,
-        description: str | None = None,
-        expected_output: str | None = None,
-    ) -> None:
-        """Initialize the AgentReasoning with an agent and optional task.
+    def __init__(self, task: Task, agent: Agent) -> None:
+        """Initialize the AgentReasoning with a task and an agent.
 
         Args:
-            agent: The agent performing the planning.
-            task: The task for which the agent is planning (optional).
-            description: Task description or input text (used if task is None).
-            expected_output: Expected output (used if task is None).
+            task: The task for which the agent is reasoning.
+            agent: The agent performing the reasoning.
         """
-        self.agent = agent
         self.task = task
-        # Use task attributes if available, otherwise use provided values
-        self._description = description or (
-            task.description if task else "Complete the requested task"
-        )
-        self._expected_output = expected_output or (
-            task.expected_output if task else "Complete the task successfully"
-        )
-        self.config = self._get_planning_config()
-        self.llm = self._resolve_llm()
+        self.agent = agent
+        self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
 
-    @property
-    def description(self) -> str:
-        """Get the task/input description."""
-        return self._description
-
-    @property
-    def expected_output(self) -> str:
-        """Get the expected output."""
-        return self._expected_output
-
-    def _get_planning_config(self) -> PlanningConfig:
-        """Get the planning configuration from the agent.
-
-        Returns:
-            The planning configuration, using defaults if not set.
-        """
-        from crewai.agent.planning_config import PlanningConfig
-
-        if self.agent.planning_config is not None:
-            return self.agent.planning_config
-        # Fallback for backward compatibility
-        return PlanningConfig(
-            max_attempts=getattr(self.agent, "max_reasoning_attempts", None),
-        )
-
-    def _resolve_llm(self) -> LLM:
-        """Resolve which LLM to use for planning.
-
-        Returns:
-            The LLM to use - either from config or the agent's LLM.
-        """
-        if self.config.llm is not None:
-            if isinstance(self.config.llm, LLM):
-                return self.config.llm
-            return create_llm(self.config.llm)
-        return cast(LLM, self.agent.llm)
-
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """Public method for the planning process that creates and refines a plan
-        for the task until the agent is ready to execute it.
+        """Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
 
         Returns:
-            AgentReasoningOutput: The output of the agent planning process.
+            AgentReasoningOutput: The output of the agent reasoning process.
         """
-        task_id = str(self.task.id) if self.task else "kickoff"
-
-        # Emit a planning started event (attempt 1)
+        # Emit a reasoning started event (attempt 1)
         try:
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningStartedEvent(
                     agent_role=self.agent.role,
-                    task_id=task_id,
+                    task_id=str(self.task.id),
                     attempt=1,
                     from_task=self.task,
                 ),
@@ -210,13 +98,13 @@ class AgentReasoning:
             pass
 
         try:
-            output = self._execute_planning()
+            output = self.__handle_agent_reasoning()
 
             crewai_event_bus.emit(
                 self.agent,
                 AgentReasoningCompletedEvent(
                     agent_role=self.agent.role,
-                    task_id=task_id,
+                    task_id=str(self.task.id),
                     plan=output.plan.plan,
                     ready=output.plan.ready,
                     attempt=1,
@@ -227,76 +115,71 @@ class AgentReasoning:
 
             return output
         except Exception as e:
-            # Emit planning failed event
+            # Emit reasoning failed event
             try:
                 crewai_event_bus.emit(
                     self.agent,
                     AgentReasoningFailedEvent(
                         agent_role=self.agent.role,
-                        task_id=task_id,
+                        task_id=str(self.task.id),
                         error=str(e),
                         attempt=1,
                         from_task=self.task,
                         from_agent=self.agent,
                     ),
                 )
-            except Exception as event_error:
-                logging.error(f"Error emitting planning failed event: {event_error}")
+            except Exception as e:
+                logging.error(f"Error emitting reasoning failed event: {e}")
 
             raise
 
-    def _execute_planning(self) -> AgentReasoningOutput:
-        """Execute the planning process.
+    def __handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """Private method that handles the agent reasoning process.
 
         Returns:
-            The output of the agent planning process.
+            The output of the agent reasoning process.
         """
-        plan, steps, ready = self._create_initial_plan()
-        plan, steps, ready = self._refine_plan_if_needed(plan, steps, ready)
+        plan, ready = self.__create_initial_plan()
 
-        reasoning_plan = ReasoningPlan(plan=plan, steps=steps, ready=ready)
+        plan, ready = self.__refine_plan_if_needed(plan, ready)
+
+        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
         return AgentReasoningOutput(plan=reasoning_plan)
 
-    def _create_initial_plan(self) -> tuple[str, list[PlanStep], bool]:
-        """Creates the initial plan for the task.
+    def __create_initial_plan(self) -> tuple[str, bool]:
+        """Creates the initial reasoning plan for the task.
 
         Returns:
-            A tuple of the plan summary, list of steps, and whether the agent is ready.
+            The initial plan and whether the agent is ready to execute the task.
         """
-        planning_prompt = self._create_planning_prompt()
+        reasoning_prompt = self.__create_reasoning_prompt()
 
         if self.llm.supports_function_calling():
-            plan, steps, ready = self._call_with_function(
-                planning_prompt, "create_plan"
-            )
-            return plan, steps, ready
-
-        response = self._call_llm_with_prompt(
-            prompt=planning_prompt,
-            plan_type="create_plan",
+            plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
+            return plan, ready
+        response = _call_llm_with_reasoning_prompt(
+            llm=self.llm,
+            prompt=reasoning_prompt,
+            task=self.task,
+            reasoning_agent=self.agent,
+            backstory=self.__get_agent_backstory(),
+            plan_type="initial_plan",
         )
 
-        plan, ready = self._parse_planning_response(str(response))
-        return plan, [], ready  # No structured steps from text parsing
+        return self.__parse_reasoning_response(str(response))
 
-    def _refine_plan_if_needed(
-        self, plan: str, steps: list[PlanStep], ready: bool
-    ) -> tuple[str, list[PlanStep], bool]:
-        """Refines the plan if the agent is not ready to execute the task.
+    def __refine_plan_if_needed(self, plan: str, ready: bool) -> tuple[str, bool]:
+        """Refines the reasoning plan if the agent is not ready to execute the task.
 
         Args:
-            plan: The current plan.
-            steps: The current list of steps.
+            plan: The current reasoning plan.
             ready: Whether the agent is ready to execute the task.
 
         Returns:
-            The refined plan, steps, and whether the agent is ready to execute.
+            The refined plan and whether the agent is ready to execute the task.
         """
-
         attempt = 1
-        max_attempts = self.config.max_attempts
-        task_id = str(self.task.id) if self.task else "kickoff"
-        current_attempt = attempt + 1
+        max_attempts = self.agent.max_reasoning_attempts
 
         while not ready and (max_attempts is None or attempt < max_attempts):
             # Emit event for each refinement attempt
@@ -305,81 +188,62 @@ class AgentReasoning:
                     self.agent,
                     AgentReasoningStartedEvent(
                         agent_role=self.agent.role,
-                        task_id=task_id,
-                        attempt=current_attempt,
+                        task_id=str(self.task.id),
+                        attempt=attempt + 1,
                         from_task=self.task,
                     ),
                 )
             except Exception:  # noqa: S110
                 pass
 
-            refine_prompt = self._create_refine_prompt(plan)
+            refine_prompt = self.__create_refine_prompt(plan)
 
             if self.llm.supports_function_calling():
-                plan, steps, ready = self._call_with_function(
-                    refine_prompt, "refine_plan"
-                )
+                plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
             else:
-                response = self._call_llm_with_prompt(
+                response = _call_llm_with_reasoning_prompt(
+                    llm=self.llm,
                     prompt=refine_prompt,
+                    task=self.task,
+                    reasoning_agent=self.agent,
+                    backstory=self.__get_agent_backstory(),
                     plan_type="refine_plan",
                 )
-                plan, ready = self._parse_planning_response(str(response))
-                steps = []  # No structured steps from text parsing
-
-            # Emit completed event for this refinement attempt
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningCompletedEvent(
-                        agent_role=self.agent.role,
-                        task_id=task_id,
-                        plan=plan,
-                        ready=ready,
-                        attempt=current_attempt,
-                        from_task=self.task,
-                        from_agent=self.agent,
-                    ),
-                )
-            except Exception:  # noqa: S110
-                pass
+                plan, ready = self.__parse_reasoning_response(str(response))
 
             attempt += 1
 
             if max_attempts is not None and attempt >= max_attempts:
                 self.logger.warning(
-                    f"Agent planning reached maximum attempts ({max_attempts}) "
-                    "without being ready. Proceeding with current plan."
+                    f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
                 )
                 break
 
-        return plan, steps, ready
+        return plan, ready
 
-    def _call_with_function(
-        self, prompt: str, plan_type: Literal["create_plan", "refine_plan"]
-    ) -> tuple[str, list[PlanStep], bool]:
-        """Calls the LLM with function calling to get a plan.
+    def __call_with_function(self, prompt: str, prompt_type: str) -> tuple[str, bool]:
+        """Calls the LLM with function calling to get a reasoning plan.
 
         Args:
             prompt: The prompt to send to the LLM.
-            plan_type: The type of plan being created.
+            prompt_type: The type of prompt (initial_plan or refine_plan).
 
         Returns:
-            A tuple containing the plan summary, list of steps, and whether the agent is ready.
+            A tuple containing the plan and whether the agent is ready.
         """
-        self.logger.debug(f"Using function calling for {plan_type} planning")
+        self.logger.debug(f"Using function calling for {prompt_type} reasoning")
 
         try:
-            system_prompt = self._get_system_prompt()
+            system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
+                role=self.agent.role,
+                goal=self.agent.goal,
+                backstory=self.__get_agent_backstory(),
+            )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(
-                plan: str,
-                steps: list[dict[str, Any]] | None = None,
-                ready: bool = True,
-            ) -> str:
-                """Return the planning result in JSON string form."""
-                return json.dumps({"plan": plan, "steps": steps or [], "ready": ready})
+            def _create_reasoning_plan(plan: str, ready: bool = True) -> str:
+                """Return the reasoning plan result in JSON string form."""
+                return json.dumps({"plan": plan, "ready": ready})
 
             response = self.llm.call(
                 [
@@ -391,33 +255,19 @@ class AgentReasoning:
                 from_task=self.task,
                 from_agent=self.agent,
             )
+
+            self.logger.debug(f"Function calling response: {response[:100]}...")
+
             try:
                 result = json.loads(response)
                 if "plan" in result and "ready" in result:
-                    # Parse steps from the response
-                    steps: list[PlanStep] = []
-                    raw_steps = result.get("steps", [])
-                    try:
-                        for step_data in raw_steps:
-                            step = PlanStep(
-                                step_number=step_data.get("step_number", 0),
-                                description=step_data.get("description", ""),
-                                tool_to_use=step_data.get("tool_to_use"),
-                                depends_on=step_data.get("depends_on", []),
-                            )
-                            steps.append(step)
-                    except Exception as step_error:
-                        self.logger.warning(
-                            f"Failed to parse step: {step_data}, error: {step_error}"
-                        )
-                    return result["plan"], steps, result["ready"]
+                    return result["plan"], result["ready"]
             except (json.JSONDecodeError, KeyError):
                 pass
 
             response_str = str(response)
             return (
                 response_str,
-                [],
                 "READY: I am ready to execute the task." in response_str,
             )
 
@@ -427,7 +277,13 @@ class AgentReasoning:
             )
 
             try:
-                system_prompt = self._get_system_prompt()
+                system_prompt = self.agent.i18n.retrieve(
+                    "reasoning", prompt_type
+                ).format(
+                    role=self.agent.role,
+                    goal=self.agent.goal,
+                    backstory=self.__get_agent_backstory(),
+                )
 
                 fallback_response = self.llm.call(
                     [
@@ -441,165 +297,78 @@ class AgentReasoning:
                 fallback_str = str(fallback_response)
                 return (
                     fallback_str,
-                    [],
                     "READY: I am ready to execute the task." in fallback_str,
                 )
             except Exception as inner_e:
                 self.logger.error(f"Error during fallback text parsing: {inner_e!s}")
                 return (
                     "Failed to generate a plan due to an error.",
-                    [],
                     True,
                 )  # Default to ready to avoid getting stuck
 
-    def _call_llm_with_prompt(
-        self,
-        prompt: str,
-        plan_type: Literal["create_plan", "refine_plan"],
-    ) -> str:
-        """Calls the LLM with the planning prompt.
-
-        Args:
-            prompt: The prompt to send to the LLM.
-            plan_type: The type of plan being created.
-
-        Returns:
-            The LLM response.
+    def __get_agent_backstory(self) -> str:
         """
-        system_prompt = self._get_system_prompt()
-
-        response = self.llm.call(
-            [
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": prompt},
-            ],
-            from_task=self.task,
-            from_agent=self.agent,
-        )
-        return str(response)
-
-    def _get_system_prompt(self) -> str:
-        """Get the system prompt for planning.
+        Safely gets the agent's backstory, providing a default if not available.
 
         Returns:
-            The system prompt, either custom or from i18n.
-        """
-        if self.config.system_prompt is not None:
-            return self.config.system_prompt
-
-        # Try new "planning" section first, fall back to "reasoning" for compatibility
-        try:
-            return self.agent.i18n.retrieve("planning", "system_prompt")
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "initial_plan").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-            )
-
-    def _get_agent_backstory(self) -> str:
-        """Safely gets the agent's backstory, providing a default if not available.
-
-        Returns:
-            The agent's backstory or a default value.
+            str: The agent's backstory or a default value.
         """
         return getattr(self.agent, "backstory", "No backstory provided")
 
-    def _create_planning_prompt(self) -> str:
-        """Creates a prompt for the agent to plan the task.
+    def __create_reasoning_prompt(self) -> str:
+        """
+        Creates a prompt for the agent to reason about the task.
 
         Returns:
-            The planning prompt.
+            str: The reasoning prompt.
         """
-        available_tools = self._format_available_tools()
+        available_tools = self.__format_available_tools()
 
-        # Use custom prompt if provided
-        if self.config.plan_prompt is not None:
-            return self.config.plan_prompt.format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-                max_steps=self.config.max_steps,
-            )
+        return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory(),
+            description=self.task.description,
+            expected_output=self.task.expected_output,
+            tools=available_tools,
+        )
 
-        # Try new "planning" section first
-        try:
-            return self.agent.i18n.retrieve("planning", "create_plan_prompt").format(
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-                max_steps=self.config.max_steps,
-            )
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                description=self.description,
-                expected_output=self.expected_output,
-                tools=available_tools,
-            )
-
-    def _format_available_tools(self) -> str:
-        """Formats the available tools for inclusion in the prompt.
+    def __format_available_tools(self) -> str:
+        """
+        Formats the available tools for inclusion in the prompt.
 
         Returns:
-            Comma-separated list of tool names.
+            str: Comma-separated list of tool names.
         """
         try:
-            # Try task tools first, then agent tools
-            tools = []
-            if self.task:
-                tools = self.task.tools or []
-            if not tools:
-                tools = getattr(self.agent, "tools", []) or []
-            if not tools:
-                return "No tools available"
-            return ", ".join([sanitize_tool_name(tool.name) for tool in tools])
+            return ", ".join(
+                [sanitize_tool_name(tool.name) for tool in (self.task.tools or [])]
+            )
         except (AttributeError, TypeError):
             return "No tools available"
 
-    def _create_refine_prompt(self, current_plan: str) -> str:
-        """Creates a prompt for the agent to refine its plan.
+    def __create_refine_prompt(self, current_plan: str) -> str:
+        """
+        Creates a prompt for the agent to refine its reasoning plan.
 
         Args:
-            current_plan: The current plan.
+            current_plan: The current reasoning plan.
 
         Returns:
-            The refine prompt.
+            str: The refine prompt.
         """
-        # Use custom prompt if provided
-        if self.config.refine_prompt is not None:
-            return self.config.refine_prompt.format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                current_plan=current_plan,
-                max_steps=self.config.max_steps,
-            )
-
-        # Try new "planning" section first
-        try:
-            return self.agent.i18n.retrieve("planning", "refine_plan_prompt").format(
-                current_plan=current_plan,
-            )
-        except (KeyError, AttributeError):
-            # Fallback to reasoning section for backward compatibility
-            return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self._get_agent_backstory(),
-                current_plan=current_plan,
-            )
+        return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
+            role=self.agent.role,
+            goal=self.agent.goal,
+            backstory=self.__get_agent_backstory(),
+            current_plan=current_plan,
+        )
 
     @staticmethod
-    def _parse_planning_response(response: str) -> tuple[str, bool]:
-        """Parses the planning response to extract the plan and readiness.
+    def __parse_reasoning_response(response: str) -> tuple[str, bool]:
+        """
+        Parses the reasoning response to extract the plan and whether
+        the agent is ready to execute the task.
 
         Args:
             response: The LLM response.
@@ -611,13 +380,25 @@ class AgentReasoning:
             return "No plan was generated.", False
 
         plan = response
-        ready = "READY: I am ready to execute the task." in response
+        ready = False
+
+        if "READY: I am ready to execute the task." in response:
+            ready = True
 
         return plan, ready
 
+    def _handle_agent_reasoning(self) -> AgentReasoningOutput:
+        """
+        Deprecated method for backward compatibility.
+        Use handle_agent_reasoning() instead.
 
-# Alias for backward compatibility
-AgentPlanning = AgentReasoning
+        Returns:
+            AgentReasoningOutput: The output of the agent reasoning process.
+        """
+        self.logger.warning(
+            "The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
+        )
+        return self.handle_agent_reasoning()
 
 
 def _call_llm_with_reasoning_prompt(
@@ -628,9 +409,7 @@ def _call_llm_with_reasoning_prompt(
     backstory: str,
     plan_type: Literal["initial_plan", "refine_plan"],
 ) -> str:
-    """Deprecated: Calls the LLM with the reasoning prompt.
-
-    This function is kept for backward compatibility.
+    """Calls the LLM with the reasoning prompt.
 
     Args:
         llm: The language model to use.
@@ -638,7 +417,7 @@ def _call_llm_with_reasoning_prompt(
         task: The task for which the agent is reasoning.
         reasoning_agent: The agent performing the reasoning.
         backstory: The agent's backstory.
-        plan_type: The type of plan being created.
+        plan_type: The type of plan being created ("initial_plan" or "refine_plan").
 
     Returns:
         The LLM response.

lib/crewai/src/crewai/utilities/step_execution_context.py

@@ -1,64 +0,0 @@
-"""Context and result types for isolated step execution in Plan-and-Execute architecture.
-
-These types mediate between the AgentExecutor (orchestrator) and StepExecutor (per-step worker).
-StepExecutionContext carries only final results from dependencies — never LLM message histories.
-StepResult carries only the outcome of a step — never internal execution traces.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-
-@dataclass(frozen=True)
-class StepExecutionContext:
-    """Immutable context passed to a StepExecutor for a single todo.
-
-    Contains only the information the Executor needs to complete one step:
-    the task description, goal, and final results from dependency steps.
-    No LLM message history, no execution traces, no shared mutable state.
-
-    Attributes:
-        task_description: The original task description (from Task or kickoff input).
-        task_goal: The expected output / goal of the overall task.
-        dependency_results: Mapping of step_number → final result string
-            for all completed dependencies of the current step.
-    """
-
-    task_description: str
-    task_goal: str
-    dependency_results: dict[int, str] = field(default_factory=dict)
-
-    def get_dependency_result(self, step_number: int) -> str | None:
-        """Get the final result of a dependency step.
-
-        Args:
-            step_number: The step number to look up.
-
-        Returns:
-            The result string if available, None otherwise.
-        """
-        return self.dependency_results.get(step_number)
-
-
-@dataclass
-class StepResult:
-    """Result returned by a StepExecutor after executing a single todo.
-
-    Contains the final outcome and metadata for debugging/metrics.
-    Tool call details are for audit logging only — they are NOT passed
-    to subsequent steps or the Planner.
-
-    Attributes:
-        success: Whether the step completed successfully.
-        result: The final output string from the step.
-        error: Error message if the step failed (None on success).
-        tool_calls_made: List of tool names invoked (for debugging/logging only).
-        execution_time: Wall-clock time in seconds for the step execution.
-    """
-
-    success: bool
-    result: str
-    error: str | None = None
-    tool_calls_made: list[str] = field(default_factory=list)
-    execution_time: float = 0.0

lib/crewai/tests/agents/test_agent.py

@@ -1456,7 +1456,7 @@ def test_agent_execute_task_with_tool():
     )
 
     result = agent.execute_task(task)
-    assert "test query" in result
+    assert "you should always think about what to do" in result
 
 
 @pytest.mark.vcr()
@@ -1475,9 +1475,9 @@ def test_agent_execute_task_with_custom_llm():
     )
 
     result = agent.execute_task(task)
-    assert "Artificial minds" in result
-    assert "Code and circuits" in result
-    assert "Future undefined" in result
+    assert "In circuits they thrive" in result
+    assert "Artificial minds awake" in result
+    assert "Future's coded drive" in result
 
 
 @pytest.mark.vcr()
@@ -2353,68 +2353,3 @@ def test_agent_without_apps_no_platform_tools():
 
     tools = crew._prepare_tools(agent, task, [])
     assert tools == []
-
-
-def test_agent_mcps_accepts_slug_with_specific_tool():
-    """Agent(mcps=["notion#get_page"]) must pass validation (_SLUG_RE)."""
-    agent = Agent(
-        role="MCP Agent",
-        goal="Test MCP validation",
-        backstory="Test agent",
-        mcps=["notion#get_page"],
-    )
-    assert agent.mcps == ["notion#get_page"]
-
-
-def test_agent_mcps_accepts_slug_with_hyphenated_tool():
-    agent = Agent(
-        role="MCP Agent",
-        goal="Test MCP validation",
-        backstory="Test agent",
-        mcps=["notion#get-page"],
-    )
-    assert agent.mcps == ["notion#get-page"]
-
-
-def test_agent_mcps_accepts_multiple_hash_refs():
-    agent = Agent(
-        role="MCP Agent",
-        goal="Test MCP validation",
-        backstory="Test agent",
-        mcps=["notion#get_page", "notion#search", "github#list_repos"],
-    )
-    assert len(agent.mcps) == 3
-
-
-def test_agent_mcps_accepts_mixed_ref_types():
-    agent = Agent(
-        role="MCP Agent",
-        goal="Test MCP validation",
-        backstory="Test agent",
-        mcps=[
-            "notion#get_page",
-            "notion",
-            "https://mcp.example.com/api",
-        ],
-    )
-    assert len(agent.mcps) == 3
-
-
-def test_agent_mcps_rejects_hash_without_slug():
-    with pytest.raises(ValueError, match="Invalid MCP reference"):
-        Agent(
-            role="MCP Agent",
-            goal="Test MCP validation",
-            backstory="Test agent",
-            mcps=["#get_page"],
-        )
-
-
-def test_agent_mcps_accepts_legacy_prefix_with_tool():
-    agent = Agent(
-        role="MCP Agent",
-        goal="Test MCP validation",
-        backstory="Test agent",
-        mcps=["crewai-amp:notion#get_page"],
-    )
-    assert agent.mcps == ["crewai-amp:notion#get_page"]

lib/crewai/tests/agents/test_agent_reasoning.py

@@ -1,345 +1,240 @@
-"""Tests for planning/reasoning in agents."""
+"""Tests for reasoning in agents."""
 
-import warnings
+import json
 
 import pytest
 
-from crewai import Agent, PlanningConfig, Task
+from crewai import Agent, Task
 from crewai.llm import LLM
 
 
-# =============================================================================
-# Tests for PlanningConfig configuration (no LLM calls needed)
-# =============================================================================
+@pytest.fixture
+def mock_llm_responses():
+    """Fixture for mock LLM responses."""
+    return {
+        "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
+        "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
+        "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
+        "execution": "4",
+    }
 
 
-def test_planning_config_default_values():
-    """Test PlanningConfig default values."""
-    config = PlanningConfig()
-
-    assert config.max_attempts is None
-    assert config.max_steps == 20
-    assert config.system_prompt is None
-    assert config.plan_prompt is None
-    assert config.refine_prompt is None
-    assert config.llm is None
-
-
-def test_planning_config_custom_values():
-    """Test PlanningConfig with custom values."""
-    config = PlanningConfig(
-        max_attempts=5,
-        max_steps=15,
-        system_prompt="Custom system",
-        plan_prompt="Custom plan: {description}",
-        refine_prompt="Custom refine: {current_plan}",
-        llm="gpt-4",
-    )
-
-    assert config.max_attempts == 5
-    assert config.max_steps == 15
-    assert config.system_prompt == "Custom system"
-    assert config.plan_prompt == "Custom plan: {description}"
-    assert config.refine_prompt == "Custom refine: {current_plan}"
-    assert config.llm == "gpt-4"
-
-
-def test_agent_with_planning_config_custom_prompts():
-    """Test agent with PlanningConfig using custom prompts."""
-    llm = LLM("gpt-4o-mini")
-
-    custom_system_prompt = "You are a specialized planner."
-    custom_plan_prompt = "Plan this task: {description}"
-
-    agent = Agent(
-        role="Test Agent",
-        goal="To test custom prompts",
-        backstory="I am a test agent.",
-        llm=llm,
-        planning_config=PlanningConfig(
-            system_prompt=custom_system_prompt,
-            plan_prompt=custom_plan_prompt,
-            max_steps=10,
-        ),
-        verbose=False,
-    )
-
-    # Just test that the agent is created properly
-    assert agent.planning_config is not None
-    assert agent.planning_config.system_prompt == custom_system_prompt
-    assert agent.planning_config.plan_prompt == custom_plan_prompt
-    assert agent.planning_config.max_steps == 10
-
-
-def test_agent_with_planning_config_disabled():
-    """Test agent with PlanningConfig disabled."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Test Agent",
-        goal="To test disabled planning",
-        backstory="I am a test agent.",
-        llm=llm,
-        planning=False,
-        verbose=False,
-    )
-
-    # Planning should be disabled
-    assert agent.planning_enabled is False
-
-
-def test_planning_enabled_property():
-    """Test the planning_enabled property on Agent."""
-    llm = LLM("gpt-4o-mini")
-
-    # With planning_config enabled
-    agent_with_planning = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-        planning=True,
-    )
-    assert agent_with_planning.planning_enabled is True
-
-    # With planning_config disabled
-    agent_disabled = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-        planning=False,
-    )
-    assert agent_disabled.planning_enabled is False
-
-    # Without planning_config
-    agent_no_planning = Agent(
-        role="Test Agent",
-        goal="Test",
-        backstory="Test",
-        llm=llm,
-    )
-    assert agent_no_planning.planning_enabled is False
-
-
-# =============================================================================
-# Tests for backward compatibility with reasoning=True (no LLM calls)
-# =============================================================================
-
-
-def test_agent_with_reasoning_backward_compat():
-    """Test agent with reasoning=True (backward compatibility)."""
-    llm = LLM("gpt-4o-mini")
-
-    # This should emit a deprecation warning
-    with warnings.catch_warnings(record=True):
-        warnings.simplefilter("always")
-        agent = Agent(
-            role="Test Agent",
-            goal="To test the reasoning feature",
-            backstory="I am a test agent created to verify the reasoning feature works correctly.",
-            llm=llm,
-            reasoning=True,
-            verbose=False,
-        )
-
-    # Should have created a PlanningConfig internally
-    assert agent.planning_config is not None
-    assert agent.planning_enabled is True
-
-
-def test_agent_with_reasoning_and_max_attempts_backward_compat():
-    """Test agent with reasoning=True and max_reasoning_attempts (backward compatibility)."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning(mock_llm_responses):
+    """Test agent with reasoning."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
         role="Test Agent",
         goal="To test the reasoning feature",
-        backstory="I am a test agent.",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
         reasoning=True,
-        max_reasoning_attempts=5,
-        verbose=False,
-    )
-
-    # Should have created a PlanningConfig with max_attempts
-    assert agent.planning_config is not None
-    assert agent.planning_config.max_attempts == 5
-
-
-# =============================================================================
-# Tests for Agent.kickoff() with planning (uses AgentExecutor)
-# =============================================================================
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_with_planning():
-    """Test Agent.kickoff() with planning enabled generates a plan."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems step by step",
-        backstory="A helpful math tutor",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1),
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 15 + 27?")
-
-    assert result is not None
-    assert "42" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_without_planning():
-    """Test Agent.kickoff() without planning skips plan generation."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
-        llm=llm,
-        # No planning_config = no planning
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 8 * 7?")
-
-    assert result is not None
-    assert "56" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_with_planning_disabled():
-    """Test Agent.kickoff() with planning explicitly disabled via planning=False."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
-        llm=llm,
-        planning=False,  # Explicitly disable planning
-        verbose=False,
-    )
-
-    result = agent.kickoff("What is 100 / 4?")
-
-    assert result is not None
-    assert "25" in str(result)
-
-
-@pytest.mark.vcr()
-def test_agent_kickoff_multi_step_task_with_planning():
-    """Test Agent.kickoff() with a multi-step task that benefits from planning."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Tutor",
-        goal="Solve multi-step math problems",
-        backstory="An expert tutor who explains step by step",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1, max_steps=5),
-        verbose=False,
-    )
-
-    # Task requires: find primes, sum them, then double
-    result = agent.kickoff(
-        "Find the first 3 prime numbers, add them together, then multiply by 2."
-    )
-
-    assert result is not None
-    # First 3 primes: 2, 3, 5 -> sum = 10 -> doubled = 20
-    assert "20" in str(result)
-
-
-# =============================================================================
-# Tests for Agent.execute_task() with planning (uses CrewAgentExecutor)
-# These test the legacy path via handle_reasoning()
-# =============================================================================
-
-
-@pytest.mark.vcr()
-def test_agent_execute_task_with_planning():
-    """Test Agent.execute_task() with planning via CrewAgentExecutor."""
-    llm = LLM("gpt-4o-mini")
-
-    agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful math tutor",
-        llm=llm,
-        planning_config=PlanningConfig(max_attempts=1),
-        verbose=False,
+        verbose=True,
     )
 
     task = Task(
-        description="What is 9 + 11?",
-        expected_output="A number",
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
         agent=agent,
     )
 
+    agent.llm.call = lambda messages, *args, **kwargs: (
+        mock_llm_responses["ready"]
+        if any("create a detailed plan" in msg.get("content", "") for msg in messages)
+        else mock_llm_responses["execution"]
+    )
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    assert "20" in str(result)
-    # Planning should be appended to task description
-    assert "Planning:" in task.description
+    assert result == mock_llm_responses["execution"]
+    assert "Reasoning Plan:" in task.description
 
 
-@pytest.mark.vcr()
-def test_agent_execute_task_without_planning():
-    """Test Agent.execute_task() without planning."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
+    """Test agent with reasoning that requires refinement."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
-        role="Math Assistant",
-        goal="Help solve math problems",
-        backstory="A helpful assistant",
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        verbose=False,
+        reasoning=True,
+        max_reasoning_attempts=2,
+        verbose=True,
     )
 
     task = Task(
-        description="What is 12 * 3?",
-        expected_output="A number",
+        description="Complex math task: What's the derivative of x²?",
+        expected_output="The answer should be a mathematical expression.",
         agent=agent,
     )
 
+    call_count = [0]
+
+    def mock_llm_call(messages, *args, **kwargs):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            if call_count[0] == 1:
+                return mock_llm_responses["not_ready"]
+            return mock_llm_responses["ready_after_refine"]
+        return "2x"
+
+    agent.llm.call = mock_llm_call
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    assert "36" in str(result)
-    # No planning should be added
-    assert "Planning:" not in task.description
+    assert result == "2x"
+    assert call_count[0] == 2  # Should have made 2 reasoning calls
+    assert "Reasoning Plan:" in task.description
 
 
-@pytest.mark.vcr()
-def test_agent_execute_task_with_planning_refine():
-    """Test Agent.execute_task() with planning that requires refinement."""
-    llm = LLM("gpt-4o-mini")
+def test_agent_with_reasoning_max_attempts_reached():
+    """Test agent with reasoning that reaches max attempts without being ready."""
+    llm = LLM("gpt-3.5-turbo")
 
     agent = Agent(
-        role="Math Tutor",
-        goal="Solve complex math problems step by step",
-        backstory="An expert tutor",
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
         llm=llm,
-        planning_config=PlanningConfig(max_attempts=2),
-        verbose=False,
+        reasoning=True,
+        max_reasoning_attempts=2,
+        verbose=True,
     )
 
     task = Task(
-        description="Calculate the area of a circle with radius 5 (use pi = 3.14)",
-        expected_output="The area as a number",
+        description="Complex math task: Solve the Riemann hypothesis.",
+        expected_output="A proof or disproof of the hypothesis.",
         agent=agent,
     )
 
+    call_count = [0]
+
+    def mock_llm_call(messages, *args, **kwargs):
+        if any(
+            "create a detailed plan" in msg.get("content", "") for msg in messages
+        ) or any("refine your plan" in msg.get("content", "") for msg in messages):
+            call_count[0] += 1
+            return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
+        return "This is an unsolved problem in mathematics."
+
+    agent.llm.call = mock_llm_call
+
     result = agent.execute_task(task)
 
-    assert result is not None
-    # Area = pi * r^2 = 3.14 * 25 = 78.5
-    assert "78" in str(result) or "79" in str(result)
-    assert "Planning:" in task.description
+    assert result == "This is an unsolved problem in mathematics."
+    assert (
+        call_count[0] == 2
+    )  # Should have made exactly 2 reasoning calls (max_attempts)
+    assert "Reasoning Plan:" in task.description
+
+
+def test_agent_reasoning_error_handling():
+    """Test error handling during the reasoning process."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+    )
+
+    task = Task(
+        description="Task that will cause an error",
+        expected_output="Output that will never be generated",
+        agent=agent,
+    )
+
+    call_count = [0]
+
+    def mock_llm_call_error(*args, **kwargs):
+        call_count[0] += 1
+        if call_count[0] <= 2:  # First calls are for reasoning
+            raise Exception("LLM error during reasoning")
+        return "Fallback execution result"  # Return a value for task execution
+
+    agent.llm.call = mock_llm_call_error
+
+    result = agent.execute_task(task)
+
+    assert result == "Fallback execution result"
+    assert call_count[0] > 2  # Ensure we called the mock multiple times
+
+
+@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
+def test_agent_with_function_calling():
+    """Test agent with reasoning using function calling."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True,
+    )
+
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent,
+    )
+
+    agent.llm.supports_function_calling = lambda: True
+
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return json.dumps(
+                {"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
+            )
+        return "4"
+
+    agent.llm.call = mock_function_call
+
+    result = agent.execute_task(task)
+
+    assert result == "4"
+    assert "Reasoning Plan:" in task.description
+    assert "I'll solve this simple math problem: 2+2=4." in task.description
+
+
+@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
+def test_agent_with_function_calling_fallback():
+    """Test agent with reasoning using function calling that falls back to text parsing."""
+    llm = LLM("gpt-3.5-turbo")
+
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the reasoning feature",
+        backstory="I am a test agent created to verify the reasoning feature works correctly.",
+        llm=llm,
+        reasoning=True,
+        verbose=True,
+    )
+
+    task = Task(
+        description="Simple math task: What's 2+2?",
+        expected_output="The answer should be a number.",
+        agent=agent,
+    )
+
+    agent.llm.supports_function_calling = lambda: True
+
+    def mock_function_call(messages, *args, **kwargs):
+        if "tools" in kwargs:
+            return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
+        return "4"
+
+    agent.llm.call = mock_function_call
+
+    result = agent.execute_task(task)
+
+    assert result == "4"
+    assert "Reasoning Plan:" in task.description
+    assert "Invalid JSON that will trigger fallback" in task.description

lib/crewai/tests/agents/test_lite_agent.py

@@ -359,34 +359,17 @@ def test_sets_flow_context_when_inside_flow():
 
 @pytest.mark.vcr()
 def test_guardrail_is_called_using_string():
-    """Test that a string guardrail triggers events and retries correctly.
-
-    Uses a callable guardrail that deterministically fails on the first
-    attempt and passes on the second. This tests the guardrail event
-    machinery (started/completed events, retry loop) without depending
-    on the LLM to comply with contradictory constraints.
-    """
     guardrail_events: dict[str, list] = defaultdict(list)
     from crewai.events.event_types import (
         LLMGuardrailCompletedEvent,
         LLMGuardrailStartedEvent,
     )
 
-    # Deterministic guardrail: fail first call, pass second
-    call_count = {"n": 0}
-
-    def fail_then_pass_guardrail(output):
-        call_count["n"] += 1
-        if call_count["n"] == 1:
-            return (False, "Missing required format — please use a numbered list")
-        return (True, output)
-
     agent = Agent(
         role="Sports Analyst",
-        goal="List the best soccer players",
-        backstory="You are an expert at gathering and organizing information.",
-        guardrail=fail_then_pass_guardrail,
-        guardrail_max_retries=3,
+        goal="Gather information about the best soccer players",
+        backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
+        guardrail="""Only include Brazilian players, both women and men""",
     )
 
     condition = threading.Condition()
@@ -405,7 +388,7 @@ def test_guardrail_is_called_using_string():
             guardrail_events["completed"].append(event)
             condition.notify()
 
-    result = agent.kickoff(messages="Top 5 best soccer players in the world?")
+    result = agent.kickoff(messages="Top 10 best players in the world?")
 
     with condition:
         success = condition.wait_for(

lib/crewai/tests/cassettes/TestAgentMultimodalAnthropic.test_pdf_file[anthropic-claude-3-5-haiku-20241022].yaml

@@ -1,9 +1,15 @@
 interactions:
 - request:
     body: '{"max_tokens":4096,"messages":[{"role":"user","content":[{"type":"text","text":"\nCurrent
-      Task: What type of document is this?\n\nProvide your complete response:"},{"type":"document","source":{"type":"base64","media_type":"application/pdf","data":"JVBERi0xLjQKMSAwIG9iaiA8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4gZW5kb2JqCjIgMCBvYmogPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4gZW5kb2JqCjMgMCBvYmogPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSA+PiBlbmRvYmoKeHJlZgowIDQKMDAwMDAwMDAwMCA2NTUzNSBmCjAwMDAwMDAwMDkgMDAwMDAgbgowMDAwMDAwMDU4IDAwMDAwIG4KMDAwMDAwMDExNSAwMDAwMCBuCnRyYWlsZXIgPDwgL1NpemUgNCAvUm9vdCAxIDAgUiA+PgpzdGFydHhyZWYKMTk2CiUlRU9GCg=="},"cache_control":{"type":"ephemeral"}}]}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:"],"stream":false,"system":"You
+      Task: What type of document is this?\n\nBegin! This is VERY important to you,
+      use the tools available and give your best Final Answer, your job depends on
+      it!\n\nThought:"},{"type":"document","source":{"type":"base64","media_type":"application/pdf","data":"JVBERi0xLjQKMSAwIG9iaiA8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4gZW5kb2JqCjIgMCBvYmogPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4gZW5kb2JqCjMgMCBvYmogPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSA+PiBlbmRvYmoKeHJlZgowIDQKMDAwMDAwMDAwMCA2NTUzNSBmCjAwMDAwMDAwMDkgMDAwMDAgbgowMDAwMDAwMDU4IDAwMDAwIG4KMDAwMDAwMDExNSAwMDAwMCBuCnRyYWlsZXIgPDwgL1NpemUgNCAvUm9vdCAxIDAgUiA+PgpzdGFydHhyZWYKMTk2CiUlRU9GCg=="},"cache_control":{"type":"ephemeral"}}]}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:"],"stream":false,"system":"You
       are File Analyst. Expert at analyzing various file types.\nYour personal goal
-      is: Analyze and describe files accurately"}'
+      is: Analyze and describe files accurately\nTo give my best complete final answer
+      to the task respond using the exact following format:\n\nThought: I now can
+      give a great answer\nFinal Answer: Your final answer must be the great and the
+      most complete as possible, it must be outcome described.\n\nI MUST use these
+      formats, my job depends on it!"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -16,7 +22,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '950'
+      - '1351'
       content-type:
       - application/json
       host:
@@ -32,35 +38,35 @@ interactions:
       x-stainless-os:
       - X-STAINLESS-OS-XXX
       x-stainless-package-version:
-      - 0.73.0
+      - 0.71.1
       x-stainless-retry-count:
       - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.13.3
+      - 3.12.10
       x-stainless-timeout:
       - NOT_GIVEN
     method: POST
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01C8ZkZMunUVDUDd8mh1r1We","type":"message","role":"assistant","content":[{"type":"text","text":"I
-        apologize, but the image appears to be completely blank or white. Without
-        any visible text, graphics, or distinguishing features, I cannot determine
-        the type of document. The file is a PDF, but the content page seems to be
-        empty or failed to render properly."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1658,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":58,"service_tier":"standard","inference_geo":"not_available"}}'
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01AcygCF93tRhc7A3bfXMqe7","type":"message","role":"assistant","content":[{"type":"text","text":"Thought:
+        I can see this is a PDF document, but the image appears to be completely white
+        or blank. Without any visible content, I cannot definitively determine the
+        specific type of document.\n\nFinal Answer: The document is a PDF file, but
+        the provided image shows a blank white page with no discernible content or
+        text. More information or a clearer image would be needed to identify the
+        precise type of document."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1750,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":89,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Security-Policy:
-      - CSP-FILTERED
       Content-Type:
       - application/json
       Date:
-      - Thu, 12 Feb 2026 19:30:55 GMT
+      - Fri, 23 Jan 2026 19:08:04 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -86,7 +92,7 @@ interactions:
       anthropic-ratelimit-requests-remaining:
       - '3999'
       anthropic-ratelimit-requests-reset:
-      - '2026-02-12T19:30:53Z'
+      - '2026-01-23T19:08:01Z'
       anthropic-ratelimit-tokens-limit:
       - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
       anthropic-ratelimit-tokens-remaining:
@@ -100,112 +106,7 @@ interactions:
       strict-transport-security:
       - STS-XXX
       x-envoy-upstream-service-time:
-      - '2129'
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":[{"type":"text","text":"\nCurrent
-      Task: What type of document is this?\n\nProvide your complete response:"},{"type":"document","source":{"type":"base64","media_type":"application/pdf","data":"JVBERi0xLjQKMSAwIG9iaiA8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4gZW5kb2JqCjIgMCBvYmogPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4gZW5kb2JqCjMgMCBvYmogPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSA+PiBlbmRvYmoKeHJlZgowIDQKMDAwMDAwMDAwMCA2NTUzNSBmCjAwMDAwMDAwMDkgMDAwMDAgbgowMDAwMDAwMDU4IDAwMDAwIG4KMDAwMDAwMDExNSAwMDAwMCBuCnRyYWlsZXIgPDwgL1NpemUgNCAvUm9vdCAxIDAgUiA+PgpzdGFydHhyZWYKMTk2CiUlRU9GCg=="},"cache_control":{"type":"ephemeral"}}]}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:"],"stream":false,"system":"You
-      are File Analyst. Expert at analyzing various file types.\nYour personal goal
-      is: Analyze and describe files accurately"}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - application/json
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      anthropic-version:
-      - '2023-06-01'
-      connection:
-      - keep-alive
-      content-length:
-      - '950'
-      content-type:
-      - application/json
-      host:
-      - api.anthropic.com
-      x-api-key:
-      - X-API-KEY-XXX
-      x-stainless-arch:
-      - X-STAINLESS-ARCH-XXX
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - X-STAINLESS-OS-XXX
-      x-stainless-package-version:
-      - 0.73.0
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.3
-      x-stainless-timeout:
-      - NOT_GIVEN
-    method: POST
-    uri: https://api.anthropic.com/v1/messages
-  response:
-    body:
-      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_013jb7edagayZxqGs6ioACyU","type":"message","role":"assistant","content":[{"type":"text","text":"I
-        apologize, but the image appears to be completely blank or white. There are
-        no visible contents or text that I can analyze to determine the type of document.
-        Without any discernible information, I cannot definitively state what type
-        of document this is."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1658,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":55,"service_tier":"standard","inference_geo":"not_available"}}'
-    headers:
-      CF-RAY:
-      - CF-RAY-XXX
-      Connection:
-      - keep-alive
-      Content-Security-Policy:
-      - CSP-FILTERED
-      Content-Type:
-      - application/json
-      Date:
-      - Thu, 12 Feb 2026 19:30:58 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Robots-Tag:
-      - none
-      anthropic-organization-id:
-      - ANTHROPIC-ORGANIZATION-ID-XXX
-      anthropic-ratelimit-input-tokens-limit:
-      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
-      anthropic-ratelimit-input-tokens-remaining:
-      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
-      anthropic-ratelimit-input-tokens-reset:
-      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
-      anthropic-ratelimit-output-tokens-limit:
-      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
-      anthropic-ratelimit-output-tokens-remaining:
-      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
-      anthropic-ratelimit-output-tokens-reset:
-      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
-      anthropic-ratelimit-requests-limit:
-      - '4000'
-      anthropic-ratelimit-requests-remaining:
-      - '3999'
-      anthropic-ratelimit-requests-reset:
-      - '2026-02-12T19:30:56Z'
-      anthropic-ratelimit-tokens-limit:
-      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
-      anthropic-ratelimit-tokens-remaining:
-      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
-      anthropic-ratelimit-tokens-reset:
-      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
-      cf-cache-status:
-      - DYNAMIC
-      request-id:
-      - REQUEST-ID-XXX
-      strict-transport-security:
-      - STS-XXX
-      x-envoy-upstream-service-time:
-      - '2005'
+      - '2837'
     status:
       code: 200
       message: OK

lib/crewai/tests/cassettes/TestAgentMultimodalFileTypes.test_pdf_anthropic.yaml

@@ -1,9 +1,14 @@
 interactions:
 - request:
     body: '{"max_tokens":4096,"messages":[{"role":"user","content":[{"type":"text","text":"\nCurrent
-      Task: What is this document?\n\nProvide your complete response:"},{"type":"document","source":{"type":"base64","media_type":"application/pdf","data":"JVBERi0xLjQKMSAwIG9iaiA8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4gZW5kb2JqCjIgMCBvYmogPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4gZW5kb2JqCjMgMCBvYmogPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSA+PiBlbmRvYmoKeHJlZgowIDQKMDAwMDAwMDAwMCA2NTUzNSBmCjAwMDAwMDAwMDkgMDAwMDAgbgowMDAwMDAwMDU4IDAwMDAwIG4KMDAwMDAwMDExNSAwMDAwMCBuCnRyYWlsZXIgPDwgL1NpemUgNCAvUm9vdCAxIDAgUiA+PgpzdGFydHhyZWYKMTk2CiUlRU9GCg=="},"cache_control":{"type":"ephemeral"}}]}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:"],"stream":false,"system":"You
+      Task: What is this document?\n\nBegin! This is VERY important to you, use the
+      tools available and give your best Final Answer, your job depends on it!\n\nThought:"},{"type":"document","source":{"type":"base64","media_type":"application/pdf","data":"JVBERi0xLjQKMSAwIG9iaiA8PCAvVHlwZSAvQ2F0YWxvZyAvUGFnZXMgMiAwIFIgPj4gZW5kb2JqCjIgMCBvYmogPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFszIDAgUl0gL0NvdW50IDEgPj4gZW5kb2JqCjMgMCBvYmogPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXSA+PiBlbmRvYmoKeHJlZgowIDQKMDAwMDAwMDAwMCA2NTUzNSBmCjAwMDAwMDAwMDkgMDAwMDAgbgowMDAwMDAwMDU4IDAwMDAwIG4KMDAwMDAwMDExNSAwMDAwMCBuCnRyYWlsZXIgPDwgL1NpemUgNCAvUm9vdCAxIDAgUiA+PgpzdGFydHhyZWYKMTk2CiUlRU9GCg=="},"cache_control":{"type":"ephemeral"}}]}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:"],"stream":false,"system":"You
       are File Analyst. Expert at analyzing various file types.\nYour personal goal
-      is: Analyze and describe files accurately"}'
+      is: Analyze and describe files accurately\nTo give my best complete final answer
+      to the task respond using the exact following format:\n\nThought: I now can
+      give a great answer\nFinal Answer: Your final answer must be the great and the
+      most complete as possible, it must be outcome described.\n\nI MUST use these
+      formats, my job depends on it!"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -16,7 +21,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '942'
+      - '1343'
       content-type:
       - application/json
       host:
@@ -32,35 +37,34 @@ interactions:
       x-stainless-os:
       - X-STAINLESS-OS-XXX
       x-stainless-package-version:
-      - 0.73.0
+      - 0.71.1
       x-stainless-retry-count:
       - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.13.3
+      - 3.12.10
       x-stainless-timeout:
       - NOT_GIVEN
     method: POST
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01RnyTYpTE9Dd8BfwyMfuwum","type":"message","role":"assistant","content":[{"type":"text","text":"I
-        apologize, but the image appears to be blank or completely white. Without
-        any visible text or content, I cannot determine the type or nature of the
-        document. If you intended to share a specific document, you may want to check
-        the file and try uploading it again."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1656,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":59,"service_tier":"standard","inference_geo":"not_available"}}'
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01XwAhfdaMxwTNzTy7YhmA5e","type":"message","role":"assistant","content":[{"type":"text","text":"Thought:
+        I can see this is a PDF document, but the image appears to be blank or completely
+        white. Without any visible text or content, I cannot determine the specific
+        type or purpose of this document.\n\nFinal Answer: The document appears to
+        be a blank white PDF page with no discernible text, images, or content visible.
+        It could be an empty document, a scanning error, or a placeholder file."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1748,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":88,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Security-Policy:
-      - CSP-FILTERED
       Content-Type:
       - application/json
       Date:
-      - Thu, 12 Feb 2026 19:29:25 GMT
+      - Fri, 23 Jan 2026 19:08:19 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -86,7 +90,7 @@ interactions:
       anthropic-ratelimit-requests-remaining:
       - '3999'
       anthropic-ratelimit-requests-reset:
-      - '2026-02-12T19:29:23Z'
+      - '2026-01-23T19:08:16Z'
       anthropic-ratelimit-tokens-limit:
       - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
       anthropic-ratelimit-tokens-remaining:
@@ -100,111 +104,7 @@ interactions:
       strict-transport-security:
       - STS-XXX
       x-envoy-upstream-service-time:
-      - '2072'
-    status:
-      code: 200
-      message: OK
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lib/crewai/tests/cassettes/TestAgentMultimodalFileTypes.test_pdf_openai_responses.yaml

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