refactor: remove inferred_categories from RecallState and update category merging logic

- Removed the inferred_categories field from RecallState to simplify state management.
- Updated the _merged_categories method to only merge caller-supplied categories, enhancing clarity in category handling.

.github/workflows/nightly.yml

@@ -1,127 +0,0 @@
-name: Nightly Canary Release
-
-on:
-  schedule:
-    - cron: '0 6 * * *' # daily at 6am UTC
-  workflow_dispatch:
-
-jobs:
-  check:
-    name: Check for new commits
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    outputs:
-      has_changes: ${{ steps.check.outputs.has_changes }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Check for commits in last 24h
-        id: check
-        run: |
-          RECENT=$(git log --since="24 hours ago" --oneline | head -1)
-          if [ -n "$RECENT" ]; then
-            echo "has_changes=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "has_changes=false" >> "$GITHUB_OUTPUT"
-          fi
-
-  build:
-    name: Build nightly packages
-    needs: check
-    if: needs.check.outputs.has_changes == 'true' || github.event_name == 'workflow_dispatch'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Stamp nightly versions
-        run: |
-          DATE=$(date +%Y%m%d)
-          for init_file in \
-            lib/crewai/src/crewai/__init__.py \
-            lib/crewai-tools/src/crewai_tools/__init__.py \
-            lib/crewai-files/src/crewai_files/__init__.py; do
-            CURRENT=$(python -c "
-          import re
-          text = open('$init_file').read()
-          print(re.search(r'__version__\s*=\s*\"(.*?)\"\s*$', text, re.MULTILINE).group(1))
-          ")
-            NIGHTLY="${CURRENT}.dev${DATE}"
-            sed -i "s/__version__ = .*/__version__ = \"${NIGHTLY}\"/" "$init_file"
-            echo "$init_file: $CURRENT -> $NIGHTLY"
-          done
-
-          # Update cross-package dependency pins to nightly versions
-          sed -i "s/\"crewai-tools==[^\"]*\"/\"crewai-tools==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai==[^\"]*\"/\"crewai==${NIGHTLY}\"/" lib/crewai-tools/pyproject.toml
-          echo "Updated cross-package dependency pins to ${NIGHTLY}"
-
-      - name: Build packages
-        run: |
-          uv build --all-packages
-          rm dist/.gitignore
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: dist/
-
-  publish:
-    name: Publish nightly to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/crewai
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: "0.8.4"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Download artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: dist
-          path: dist
-
-      - name: Publish to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          failed=0
-          for package in dist/*; do
-            if [[ "$package" == *"crewai_devtools"* ]]; then
-              echo "Skipping private package: $package"
-              continue
-            fi
-            echo "Publishing $package"
-            if ! uv publish "$package"; then
-              echo "Failed to publish $package"
-              failed=1
-            fi
-          done
-          if [ $failed -eq 1 ]; then
-            echo "Some packages failed to publish"
-            exit 1
-          fi

conftest.py

@@ -12,7 +12,6 @@ from dotenv import load_dotenv
 import pytest
 from vcr.request import Request  # type: ignore[import-untyped]
 
-
 try:
     import vcr.stubs.httpx_stubs as httpx_stubs  # type: ignore[import-untyped]
 except ModuleNotFoundError:

docs/en/changelog.mdx

@@ -4,38 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="Mar 04, 2026">
-  ## v1.10.1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## What's Changed
-
-  ### Features
-  - Upgrade Gemini GenAI
-
-  ### Bug Fixes
-  - Adjust executor listener value to avoid recursion
-  - Group parallel function response parts in a single Content object in Gemini
-  - Surface thought output from thinking models in Gemini
-  - Load MCP and platform tools when agent tools are None
-  - Support Jupyter environments with running event loops in A2A
-  - Use anonymous ID for ephemeral traces
-  - Conditionally pass plus header
-  - Skip signal handler registration in non-main threads for telemetry
-  - Inject tool errors as observations and resolve name collisions
-  - Upgrade pypdf from 4.x to 6.7.4 to resolve Dependabot alerts
-  - Resolve critical and high Dependabot security alerts
-
-  ### Documentation
-  - Sync Composio tool documentation across locales
-
-  ## Contributors
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
 <Update label="Feb 27, 2026">
   ## v1.10.1a1
 

docs/en/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "Moving from LangGraph to CrewAI: A Practical Guide for Engineers"
-description: If you already have built with LangGraph, learn how to quickly port your projects to CrewAI
-icon: switch
-mode: "wide"
----
-
-You've built agents with LangGraph. You've wrestled with `StateGraph`, wired up conditional edges, and debugged state dictionaries at 2 AM. It works — but somewhere along the way, you started wondering if there's a better path to production.
-
-There is. **CrewAI Flows** gives you the same power — event-driven orchestration, conditional routing, shared state — with dramatically less boilerplate and a mental model that maps cleanly to how you actually think about multi-step AI workflows.
-
-This article walks through the core concepts side by side, shows real code comparisons, and demonstrates why CrewAI Flows is the framework you'll want to reach for next.
-
----
-
-## The Mental Model Shift
-
-LangGraph asks you to think in **graphs**: nodes, edges, and state dictionaries. Every workflow is a directed graph where you explicitly wire transitions between computation steps. It's powerful, but the abstraction carries overhead — especially when your workflow is fundamentally sequential with a few decision points.
-
-CrewAI Flows asks you to think in **events**: methods that start things, methods that listen for results, and methods that route execution. The topology of your workflow emerges from decorator annotations rather than explicit graph construction. This isn't just syntactic sugar — it changes how you design, read, and maintain your pipelines.
-
-Here's the core mapping:
-
-| LangGraph Concept | CrewAI Flows Equivalent |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-Let's see what this looks like in practice.
-
----
-
-## Demo 1: A Simple Sequential Pipeline
-
-Imagine you're building a pipeline that takes a topic, researches it, writes a summary, and formats the output. Here's how each framework handles it.
-
-### LangGraph Approach
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-You define functions, register them as nodes, and manually wire every transition. For a simple sequence like this, there's a lot of ceremony.
-
-### CrewAI Flows Approach
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-Notice what's different: no graph construction, no edge wiring, no compile step. The execution order is declared right where the logic lives. `@start()` marks the entry point, and `@listen(method_name)` chains steps together. The state is a proper Pydantic model with type safety, validation, and IDE auto-completion.
-
----
-
-## Demo 2: Conditional Routing
-
-This is where things get interesting. Say you're building a content pipeline that routes to different processing paths based on the type of content detected.
-
-### LangGraph Approach
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-You need a separate routing function, explicit conditional edge mapping, and termination edges for every branch. The routing logic is decoupled from the node that produces the routing decision.
-
-### CrewAI Flows Approach
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-The `@router()` decorator turns a method into a decision point. It returns a string that matches a listener — no mapping dictionaries, no separate routing functions. The branching logic reads like a Python `if` statement because it *is* one.
-
----
-
-## Demo 3: Integrating AI Agent Crews into Flows
-
-Here's where CrewAI's real power shines. Flows aren't just for chaining LLM calls — they orchestrate full **Crews** of autonomous agents. This is something LangGraph simply doesn't have a native equivalent for.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-This is the key insight: **Flows provide the orchestration layer, and Crews provide the intelligence layer.** Each step in a Flow can spin up a full team of collaborating agents, each with their own roles, goals, and tools. You get structured, predictable control flow *and* autonomous agent collaboration — the best of both worlds.
-
-In LangGraph, achieving something similar means manually implementing agent communication protocols, tool-calling loops, and delegation logic inside your node functions. It's possible, but it's plumbing you're building from scratch every time.
-
----
-
-## Demo 4: Parallel Execution and Synchronization
-
-Real-world pipelines often need to fan out work and join the results. CrewAI Flows handles this elegantly with `and_` and `or_` operators.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-Multiple `@start()` decorators fire in parallel. The `and_()` combinator on the `@listen` decorator ensures `synthesize_report` only executes after *all three* upstream methods complete. There's also `or_()` for when you want to proceed as soon as *any* upstream task finishes.
-
-In LangGraph, you'd need to build a fan-out/fan-in pattern with parallel branches, a synchronization node, and careful state merging — all wired explicitly through edges.
-
----
-
-## Why CrewAI Flows for Production
-
-Beyond cleaner syntax, Flows deliver several production-critical advantages:
-
-**Built-in state persistence.** Flow state is backed by LanceDB, meaning your workflows can survive crashes, be resumed, and accumulate knowledge across runs. LangGraph requires you to configure a separate checkpointer.
-
-**Type-safe state management.** Pydantic models give you validation, serialization, and IDE support out of the box. LangGraph's `TypedDict` states don't validate at runtime.
-
-**First-class agent orchestration.** Crews are a native primitive. You define agents with roles, goals, backstories, and tools — and they collaborate autonomously within the structured envelope of a Flow. No need to reinvent multi-agent coordination.
-
-**Simpler mental model.** Decorators declare intent. `@start` means "begin here." `@listen(x)` means "run after x." `@router(x)` means "decide where to go after x." The code reads like the workflow it describes.
-
-**CLI integration.** Run flows with `crewai run`. No separate compilation step, no graph serialization. Your Flow is a Python class, and it runs like one.
-
----
-
-## Migration Cheat Sheet
-
-If you're sitting on a LangGraph codebase and want to move to CrewAI Flows, here's a practical conversion guide:
-
-1. **Map your state.** Convert your `TypedDict` to a Pydantic `BaseModel`. Add default values for all fields.
-2. **Convert nodes to methods.** Each `add_node` function becomes a method on your `Flow` subclass. Replace `state["field"]` reads with `self.state.field`.
-3. **Replace edges with decorators.** Your `add_edge(START, "first_node")` becomes `@start()` on the first method. Sequential `add_edge("a", "b")` becomes `@listen(a)` on method `b`.
-4. **Replace conditional edges with `@router`.** Your routing function and `add_conditional_edges()` mapping become a single `@router()` method that returns a route string.
-5. **Replace compile + invoke with kickoff.** Drop `graph.compile()`. Call `flow.kickoff()` instead.
-6. **Consider where Crews fit.** Any node where you have complex multi-step agent logic is a candidate for extraction into a Crew. This is where you'll see the biggest quality improvement.
-
----
-
-## Getting Started
-
-Install CrewAI and scaffold a new Flow project:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-This generates a project structure with a ready-to-edit Flow class, configuration files, and a `pyproject.toml` with `type = "flow"` already set. Run it with:
-
-```bash
-crewai run
-```
-
-From there, add your agents, wire up your listeners, and ship it.
-
----
-
-## Final Thoughts
-
-LangGraph taught the ecosystem that AI workflows need structure. That was an important lesson. But CrewAI Flows takes that lesson and delivers it in a form that's faster to write, easier to read, and more powerful in production — especially when your workflows involve multiple collaborating agents.
-
-If you're building anything beyond a single-agent chain, give Flows a serious look. The decorator-driven model, native Crew integration, and built-in state management mean you'll spend less time on plumbing and more time on the problems that matter.
-
-Start with `crewai create flow`. You won't look back.

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

@@ -4,38 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="2026년 3월 4일">
-  ## v1.10.1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## 변경 사항
-
-  ### 기능
-  - Gemini GenAI 업그레이드
-
-  ### 버그 수정
-  - 재귀를 피하기 위해 실행기 리스너 값을 조정
-  - Gemini에서 병렬 함수 응답 부분을 단일 Content 객체로 그룹화
-  - Gemini에서 사고 모델의 사고 출력을 표시
-  - 에이전트 도구가 None일 때 MCP 및 플랫폼 도구 로드
-  - A2A에서 실행 이벤트 루프가 있는 Jupyter 환경 지원
-  - 일시적인 추적을 위해 익명 ID 사용
-  - 조건부로 플러스 헤더 전달
-  - 원격 측정을 위해 비주 스레드에서 신호 처리기 등록 건너뛰기
-  - 도구 오류를 관찰로 주입하고 이름 충돌 해결
-  - Dependabot 경고를 해결하기 위해 pypdf를 4.x에서 6.7.4로 업그레이드
-  - 심각 및 높은 Dependabot 보안 경고 해결
-
-  ### 문서
-  - Composio 도구 문서를 지역별로 동기화
-
-  ## 기여자
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
 <Update label="2026년 2월 27일">
   ## v1.10.1a1
 

docs/ko/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "LangGraph에서 CrewAI로 옮기기: 엔지니어를 위한 실전 가이드"
-description: LangGraph로 이미 구축했다면, 프로젝트를 CrewAI로 빠르게 옮기는 방법을 알아보세요
-icon: switch
-mode: "wide"
----
-
-LangGraph로 에이전트를 구축해 왔습니다. `StateGraph`와 씨름하고, 조건부 에지를 연결하고, 새벽 2시에 상태 딕셔너리를 디버깅해 본 적도 있죠. 동작은 하지만 — 어느 순간부터 프로덕션으로 가는 더 나은 길이 없을까 고민하게 됩니다.
-
-있습니다. **CrewAI Flows**는 이벤트 기반 오케스트레이션, 조건부 라우팅, 공유 상태라는 동일한 힘을 훨씬 적은 보일러플레이트와 실제로 다단계 AI 워크플로우를 생각하는 방식에 잘 맞는 정신적 모델로 제공합니다.
-
-이 글은 핵심 개념을 나란히 비교하고 실제 코드 비교를 보여주며, 다음으로 손이 갈 프레임워크가 왜 CrewAI Flows인지 설명합니다.
-
----
-
-## 정신적 모델의 전환
-
-LangGraph는 **그래프**로 생각하라고 요구합니다: 노드, 에지, 그리고 상태 딕셔너리. 모든 워크플로우는 계산 단계 사이의 전이를 명시적으로 연결하는 방향 그래프입니다. 강력하지만, 특히 워크플로우가 몇 개의 결정 지점이 있는 순차적 흐름일 때 이 추상화는 오버헤드를 가져옵니다.
-
-CrewAI Flows는 **이벤트**로 생각하라고 요구합니다: 시작하는 메서드, 결과를 듣는 메서드, 실행을 라우팅하는 메서드. 워크플로우의 토폴로지는 명시적 그래프 구성 대신 데코레이터 어노테이션에서 드러납니다. 이것은 단순한 문법 설탕이 아니라 — 파이프라인을 설계하고 읽고 유지하는 방식을 바꿉니다.
-
-핵심 매핑은 다음과 같습니다:
-
-| LangGraph 개념 | CrewAI Flows 대응 |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-실제로 어떻게 보이는지 살펴보겠습니다.
-
----
-
-## 데모 1: 간단한 순차 파이프라인
-
-주제를 받아 조사하고, 요약을 작성한 뒤, 결과를 포맷팅하는 파이프라인을 만든다고 해봅시다. 각 프레임워크는 이렇게 처리합니다.
-
-### LangGraph 방식
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-함수를 정의하고 노드로 등록한 다음, 모든 전이를 수동으로 연결합니다. 이렇게 단순한 순서인데도 의례처럼 해야 할 작업이 많습니다.
-
-### CrewAI Flows 방식
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-눈에 띄는 차이점이 있습니다: 그래프 구성 없음, 에지 연결 없음, 컴파일 단계 없음. 실행 순서는 로직이 있는 곳에서 바로 선언됩니다. `@start()`는 진입점을 표시하고, `@listen(method_name)`은 단계들을 연결합니다. 상태는 타입 안전성, 검증, IDE 자동 완성까지 제공하는 제대로 된 Pydantic 모델입니다.
-
----
-
-## 데모 2: 조건부 라우팅
-
-여기서 흥미로워집니다. 콘텐츠 유형에 따라 서로 다른 처리 경로로 라우팅하는 파이프라인을 만든다고 해봅시다.
-
-### LangGraph 방식
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-별도의 라우팅 함수, 명시적 조건부 에지 매핑, 그리고 모든 분기에 대한 종료 에지가 필요합니다. 라우팅 결정 로직이 그 결정을 만들어 내는 노드와 분리됩니다.
-
-### CrewAI Flows 방식
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-`@router()` 데코레이터는 메서드를 결정 지점으로 만듭니다. 리스너와 매칭되는 문자열을 반환하므로, 매핑 딕셔너리도, 별도의 라우팅 함수도 필요 없습니다. 분기 로직이 Python `if` 문처럼 읽히는 이유는, 실제로 `if` 문이기 때문입니다.
-
----
-
-## 데모 3: AI 에이전트 Crew를 Flow에 통합하기
-
-여기서 CrewAI의 진짜 힘이 드러납니다. Flows는 LLM 호출을 연결하는 것에 그치지 않고 자율적인 에이전트 **Crew** 전체를 오케스트레이션합니다. 이는 LangGraph에 기본으로 대응되는 개념이 없습니다.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-핵심 인사이트는 다음과 같습니다: **Flows는 오케스트레이션 레이어를, Crews는 지능 레이어를 제공합니다.** Flow의 각 단계는 각자의 역할, 목표, 도구를 가진 협업 에이전트 팀을 띄울 수 있습니다. 구조화되고 예측 가능한 제어 흐름 *그리고* 자율적 에이전트 협업 — 두 세계의 장점을 모두 얻습니다.
-
-LangGraph에서 비슷한 것을 하려면 노드 함수 안에 에이전트 통신 프로토콜, 도구 호출 루프, 위임 로직을 직접 구현해야 합니다. 가능하긴 하지만, 매번 처음부터 배관을 만드는 셈입니다.
-
----
-
-## 데모 4: 병렬 실행과 동기화
-
-실제 파이프라인은 종종 작업을 병렬로 분기하고 결과를 합쳐야 합니다. CrewAI Flows는 `and_`와 `or_` 연산자로 이를 우아하게 처리합니다.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-여러 `@start()` 데코레이터는 병렬로 실행됩니다. `@listen` 데코레이터의 `and_()` 결합자는 `synthesize_report`가 *세 가지* 상위 메서드가 모두 완료된 뒤에만 실행되도록 보장합니다. *어떤* 상위 작업이든 끝나는 즉시 진행하고 싶다면 `or_()`도 사용할 수 있습니다.
-
-LangGraph에서는 병렬 분기, 동기화 노드, 신중한 상태 병합이 포함된 fan-out/fan-in 패턴을 만들어야 하며 — 모든 것을 에지로 명시적으로 연결해야 합니다.
-
----
-
-## 프로덕션에서 CrewAI Flows를 쓰는 이유
-
-깔끔한 문법을 넘어, Flows는 여러 프로덕션 핵심 이점을 제공합니다:
-
-**내장 상태 지속성.** Flow 상태는 LanceDB에 의해 백업되므로 워크플로우가 크래시에서 살아남고, 재개될 수 있으며, 실행 간에 지식을 축적할 수 있습니다. LangGraph는 별도의 체크포인터를 구성해야 합니다.
-
-**타입 안전한 상태 관리.** Pydantic 모델은 즉시 검증, 직렬화, IDE 지원을 제공합니다. LangGraph의 `TypedDict` 상태는 런타임 검증을 하지 않습니다.
-
-**일급 에이전트 오케스트레이션.** Crews는 기본 프리미티브입니다. 역할, 목표, 배경, 도구를 가진 에이전트를 정의하고, Flow의 구조적 틀 안에서 자율적으로 협업하게 합니다. 다중 에이전트 조율을 다시 만들 필요가 없습니다.
-
-**더 단순한 정신적 모델.** 데코레이터는 의도를 선언합니다. `@start`는 "여기서 시작", `@listen(x)`는 "x 이후 실행", `@router(x)`는 "x 이후 어디로 갈지 결정"을 의미합니다. 코드는 자신이 설명하는 워크플로우처럼 읽힙니다.
-
-**CLI 통합.** `crewai run`으로 Flows를 실행합니다. 별도의 컴파일 단계나 그래프 직렬화가 없습니다. Flow는 Python 클래스이며, 그대로 실행됩니다.
-
----
-
-## 마이그레이션 치트 시트
-
-LangGraph 코드베이스를 CrewAI Flows로 옮기고 싶다면, 다음의 실전 변환 가이드를 참고하세요:
-
-1. **상태를 매핑하세요.** `TypedDict`를 Pydantic `BaseModel`로 변환하고 모든 필드에 기본값을 추가하세요.
-2. **노드를 메서드로 변환하세요.** 각 `add_node` 함수는 `Flow` 서브클래스의 메서드가 됩니다. `state["field"]` 읽기는 `self.state.field`로 바꾸세요.
-3. **에지를 데코레이터로 교체하세요.** `add_edge(START, "first_node")`는 첫 메서드의 `@start()`가 됩니다. 순차적인 `add_edge("a", "b")`는 `b` 메서드의 `@listen(a)`가 됩니다.
-4. **조건부 에지는 `@router`로 교체하세요.** 라우팅 함수와 `add_conditional_edges()` 매핑은 하나의 `@router()` 메서드로 통합하고, 라우트 문자열을 반환하세요.
-5. **compile + invoke를 kickoff으로 교체하세요.** `graph.compile()`를 제거하고 `flow.kickoff()`를 호출하세요.
-6. **Crew가 들어갈 지점을 고려하세요.** 복잡한 다단계 에이전트 로직이 있는 노드는 Crew로 분리할 후보입니다. 이 부분에서 가장 큰 품질 향상을 체감할 수 있습니다.
-
----
-
-## 시작하기
-
-CrewAI를 설치하고 새 Flow 프로젝트를 스캐폴딩하세요:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-이렇게 하면 바로 편집 가능한 Flow 클래스, 설정 파일, 그리고 `type = "flow"`가 이미 설정된 `pyproject.toml`이 포함된 프로젝트 구조가 생성됩니다. 다음으로 실행하세요:
-
-```bash
-crewai run
-```
-
-그 다음부터는 에이전트를 추가하고 리스너를 연결한 뒤, 배포하면 됩니다.
-
----
-
-## 마무리
-
-LangGraph는 AI 워크플로우에 구조가 필요하다는 사실을 생태계에 일깨워 주었습니다. 중요한 교훈이었습니다. 하지만 CrewAI Flows는 그 교훈을 더 빠르게 쓰고, 더 쉽게 읽으며, 프로덕션에서 더 강력한 형태로 제공합니다 — 특히 워크플로우에 여러 에이전트의 협업이 포함될 때 그렇습니다.
-
-단일 에이전트 체인을 넘는 무엇인가를 만들고 있다면, Flows를 진지하게 검토해 보세요. 데코레이터 기반 모델, Crews의 네이티브 통합, 내장 상태 관리를 통해 배관 작업에 쓰는 시간을 줄이고, 중요한 문제에 더 많은 시간을 쓸 수 있습니다.
-
-`crewai create flow`로 시작하세요. 후회하지 않을 겁니다.

docs/pt-BR/changelog.mdx

@@ -4,38 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="04 mar 2026">
-  ## v1.10.1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## O que mudou
-
-  ### Recursos
-  - Atualizar Gemini GenAI
-
-  ### Correções de Bugs
-  - Ajustar o valor do listener do executor para evitar recursão
-  - Agrupar partes da resposta da função paralela em um único objeto Content no Gemini
-  - Exibir a saída de pensamento dos modelos de pensamento no Gemini
-  - Carregar ferramentas MCP e da plataforma quando as ferramentas do agente forem None
-  - Suportar ambientes Jupyter com loops de eventos em A2A
-  - Usar ID anônimo para rastreamentos efêmeros
-  - Passar condicionalmente o cabeçalho plus
-  - Ignorar o registro do manipulador de sinal em threads não principais para telemetria
-  - Injetar erros de ferramentas como observações e resolver colisões de nomes
-  - Atualizar pypdf de 4.x para 6.7.4 para resolver alertas do Dependabot
-  - Resolver alertas de segurança críticos e altos do Dependabot
-
-  ### Documentação
-  - Sincronizar a documentação da ferramenta Composio entre locais
-
-  ## Contribuidores
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
 <Update label="27 fev 2026">
   ## v1.10.1a1
 

docs/pt-BR/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "Migrando do LangGraph para o CrewAI: um guia prático para engenheiros"
-description: Se você já construiu com LangGraph, saiba como portar rapidamente seus projetos para o CrewAI
-icon: switch
-mode: "wide"
----
-
-Você construiu agentes com LangGraph. Já lutou com o `StateGraph`, ligou arestas condicionais e depurou dicionários de estado às 2 da manhã. Funciona — mas, em algum momento, você começou a se perguntar se existe um caminho melhor para produção.
-
-Existe. **CrewAI Flows** entrega o mesmo poder — orquestração orientada a eventos, roteamento condicional, estado compartilhado — com muito menos boilerplate e um modelo mental que se alinha a como você realmente pensa sobre fluxos de trabalho de IA em múltiplas etapas.
-
-Este artigo apresenta os conceitos principais lado a lado, mostra comparações reais de código e demonstra por que o CrewAI Flows é o framework que você vai querer usar a seguir.
-
----
-
-## A Mudança de Modelo Mental
-
-LangGraph pede que você pense em **grafos**: nós, arestas e dicionários de estado. Todo workflow é um grafo direcionado em que você conecta explicitamente as transições entre as etapas de computação. É poderoso, mas a abstração traz overhead — especialmente quando o seu fluxo é fundamentalmente sequencial com alguns pontos de decisão.
-
-CrewAI Flows pede que você pense em **eventos**: métodos que iniciam, métodos que escutam resultados e métodos que roteiam a execução. A topologia do workflow emerge de anotações com decorators, em vez de construção explícita do grafo. Isso não é apenas açúcar sintático — muda como você projeta, lê e mantém seus pipelines.
-
-Veja o mapeamento principal:
-
-| Conceito no LangGraph | Equivalente no CrewAI Flows |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-Vamos ver como isso fica na prática.
-
----
-
-## Demo 1: Um Pipeline Sequencial Simples
-
-Imagine que você está construindo um pipeline que recebe um tema, pesquisa, escreve um resumo e formata a saída. Veja como cada framework lida com isso.
-
-### Abordagem com LangGraph
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-Você define funções, registra-as como nós e conecta manualmente cada transição. Para uma sequência simples como essa, há muita cerimônia.
-
-### Abordagem com CrewAI Flows
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-Repare a diferença: nada de construção de grafo, de ligação de arestas, nem de etapa de compilação. A ordem de execução é declarada exatamente onde a lógica vive. `@start()` marca o ponto de entrada, e `@listen(method_name)` encadeia as etapas. O estado é um modelo Pydantic de verdade, com segurança de tipos, validação e auto-complete na IDE.
-
----
-
-## Demo 2: Roteamento Condicional
-
-Aqui é que fica interessante. Digamos que você está construindo um pipeline de conteúdo que roteia para diferentes caminhos de processamento com base no tipo de conteúdo detectado.
-
-### Abordagem com LangGraph
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-Você precisa de uma função de roteamento separada, de um mapeamento explícito de arestas condicionais e de arestas de término para cada ramificação. A lógica de roteamento fica desacoplada do nó que produz a decisão.
-
-### Abordagem com CrewAI Flows
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-O decorator `@router()` transforma um método em um ponto de decisão. Ele retorna uma string que corresponde a um listener — sem dicionários de mapeamento, sem funções de roteamento separadas. A lógica de ramificação parece um `if` em Python porque *é* um.
-
----
-
-## Demo 3: Integrando Crews de Agentes de IA em Flows
-
-É aqui que o verdadeiro poder do CrewAI aparece. Flows não servem apenas para encadear chamadas de LLM — elas orquestram **Crews** completas de agentes autônomos. Isso é algo para o qual o LangGraph simplesmente não tem um equivalente nativo.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-Este é o insight-chave: **Flows fornecem a camada de orquestração, e Crews fornecem a camada de inteligência.** Cada etapa em um Flow pode subir uma equipe completa de agentes colaborativos, cada um com seus próprios papéis, objetivos e ferramentas. Você obtém fluxo de controle estruturado e previsível *e* colaboração autônoma de agentes — o melhor dos dois mundos.
-
-No LangGraph, alcançar algo similar significa implementar manualmente protocolos de comunicação entre agentes, loops de chamada de ferramentas e lógica de delegação dentro das funções dos nós. É possível, mas é encanamento que você constrói do zero todas as vezes.
-
----
-
-## Demo 4: Execução Paralela e Sincronização
-
-Pipelines do mundo real frequentemente precisam dividir o trabalho e juntar os resultados. O CrewAI Flows lida com isso de forma elegante com os operadores `and_` e `or_`.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-Vários decorators `@start()` disparam em paralelo. O combinador `and_()` no decorator `@listen` garante que `synthesize_report` só execute depois que *todos os três* métodos upstream forem concluídos. Também existe `or_()` para quando você quer prosseguir assim que *qualquer* tarefa upstream terminar.
-
-No LangGraph, você precisaria construir um padrão fan-out/fan-in com ramificações paralelas, um nó de sincronização e uma mesclagem de estado cuidadosa — tudo conectado explicitamente por arestas.
-
----
-
-## Por que CrewAI Flows em Produção
-
-Além de uma sintaxe mais limpa, Flows entrega várias vantagens críticas para produção:
-
-**Persistência de estado integrada.** O estado do Flow é respaldado pelo LanceDB, o que significa que seus workflows podem sobreviver a falhas, ser retomados e acumular conhecimento entre execuções. No LangGraph, você precisa configurar um checkpointer separado.
-
-**Gerenciamento de estado com segurança de tipos.** Modelos Pydantic oferecem validação, serialização e suporte de IDE prontos para uso. Estados `TypedDict` do LangGraph não validam em runtime.
-
-**Orquestração de agentes de primeira classe.** Crews são um primitivo nativo. Você define agentes com papéis, objetivos, histórias e ferramentas — e eles colaboram de forma autônoma dentro do envelope estruturado de um Flow. Não é preciso reinventar a coordenação multiagente.
-
-**Modelo mental mais simples.** Decorators declaram intenção. `@start` significa "comece aqui". `@listen(x)` significa "execute depois de x". `@router(x)` significa "decida para onde ir depois de x". O código lê como o workflow que ele descreve.
-
-**Integração com CLI.** Execute flows com `crewai run`. Sem etapa de compilação separada, sem serialização de grafo. Seu Flow é uma classe Python, e ele roda como tal.
-
----
-
-## Cheat Sheet de Migração
-
-Se você está com uma base de código LangGraph e quer migrar para o CrewAI Flows, aqui vai um guia prático de conversão:
-
-1. **Mapeie seu estado.** Converta seu `TypedDict` para um `BaseModel` do Pydantic. Adicione valores padrão para todos os campos.
-2. **Converta nós em métodos.** Cada função de `add_node` vira um método na sua subclasse de `Flow`. Substitua leituras `state["field"]` por `self.state.field`.
-3. **Substitua arestas por decorators.** `add_edge(START, "first_node")` vira `@start()` no primeiro método. A sequência `add_edge("a", "b")` vira `@listen(a)` no método `b`.
-4. **Substitua arestas condicionais por `@router`.** A função de roteamento e o mapeamento do `add_conditional_edges()` viram um único método `@router()` que retorna a string de rota.
-5. **Troque compile + invoke por kickoff.** Remova `graph.compile()`. Chame `flow.kickoff()`.
-6. **Considere onde as Crews se encaixam.** Qualquer nó com lógica complexa de agentes em múltiplas etapas é um candidato a extração para uma Crew. É aqui que você verá a maior melhoria de qualidade.
-
----
-
-## Primeiros Passos
-
-Instale o CrewAI e crie o scaffold de um novo projeto Flow:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-Isso gera uma estrutura de projeto com uma classe Flow pronta para edição, arquivos de configuração e um `pyproject.toml` com `type = "flow"` já definido. Execute com:
-
-```bash
-crewai run
-```
-
-A partir daí, adicione seus agentes, conecte seus listeners e publique.
-
----
-
-## Considerações Finais
-
-O LangGraph ensinou ao ecossistema que workflows de IA precisam de estrutura. Essa foi uma lição importante. Mas o CrewAI Flows pega essa lição e a entrega de um jeito mais rápido de escrever, mais fácil de ler e mais poderoso em produção — especialmente quando seus workflows envolvem múltiplos agentes colaborando.
-
-Se você está construindo algo além de uma cadeia de agente único, dê uma olhada séria no Flows. O modelo baseado em decorators, a integração nativa com Crews e o gerenciamento de estado embutido significam menos tempo com encanamento e mais tempo nos problemas que importam.
-
-Comece com `crewai create flow`. Você não vai olhar para trás.

lib/crewai-files/pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 requires-python = ">=3.10, <3.14"
 dependencies = [
     "Pillow~=12.1.1",
-    "pypdf~=6.7.5",
+    "pypdf~=6.7.4",
     "python-magic>=0.4.27",
     "aiocache~=0.12.3",
     "aiofiles~=24.1.0",

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

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

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.10.1",
+    "crewai==1.10.1a1",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -108,7 +108,7 @@ stagehand = [
     "stagehand>=0.4.1",
 ]
 github = [
-    "gitpython>=3.1.41,<4",
+    "gitpython==3.1.38",
     "PyGithub==1.59.1",
 ]
 rag = [

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__ = "1.10.1"
+__version__ = "1.10.1a1"

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 +10,16 @@ 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 +28,51 @@ 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 +82,7 @@ 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 +119,8 @@ 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 +129,8 @@ 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/multion_tool/example.py

@@ -1,7 +1,7 @@
 import os
 
 from crewai import Agent, Crew, Task
-from multion_tool import MultiOnTool  # type: ignore[import-not-found]
+from multion_tool import MultiOnTool # type: ignore[import-not-found]
 
 
 os.environ["OPENAI_API_KEY"] = "Your Key"

lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py

@@ -17,11 +17,11 @@ Usage:
 
 import os
 
-from crewai import Agent, Crew, Process, Task
 from crewai.utilities.printer import Printer
 from dotenv import load_dotenv
 from stagehand.schemas import AvailableModel  # type: ignore[import-untyped]
 
+from crewai import Agent, Crew, Process, Task
 from crewai_tools import StagehandTool
 
 

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/pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http~=1.34.0",
     # Data Handling
     "chromadb~=1.1.0",
-    "tokenizers>=0.21,<1",
+    "tokenizers~=0.20.3",
     "openpyxl~=3.1.5",
     # Authentication and Security
     "python-dotenv~=1.1.1",
@@ -53,7 +53,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.10.1",
+    "crewai-tools==1.10.1a1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -88,7 +88,7 @@ bedrock = [
     "boto3~=1.40.45",
 ]
 google-genai = [
-    "google-genai~=1.65.0",
+    "google-genai~=1.49.0",
 ]
 azure-ai-inference = [
     "azure-ai-inference~=1.0.0b9",

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.10.1"
+__version__ = "1.10.1a1"
 _telemetry_submitted = False
 
 

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

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import asyncio
 from collections.abc import MutableMapping
-import concurrent.futures
 from functools import lru_cache
 import ssl
 import time
@@ -139,17 +138,14 @@ def fetch_agent_card(
         ttl_hash = int(time.time() // cache_ttl)
         return _fetch_agent_card_cached(endpoint, auth_hash, timeout, ttl_hash)
 
-    coro = afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
     try:
-        asyncio.get_running_loop()
-        has_running_loop = True
-    except RuntimeError:
-        has_running_loop = False
-
-    if has_running_loop:
-        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
-            return pool.submit(asyncio.run, coro).result()
-    return asyncio.run(coro)
+        return loop.run_until_complete(
+            afetch_agent_card(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
 
 
 async def afetch_agent_card(
@@ -207,17 +203,14 @@ def _fetch_agent_card_cached(
     """Cached sync version of fetch_agent_card."""
     auth = _auth_store.get(auth_hash)
 
-    coro = _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
     try:
-        asyncio.get_running_loop()
-        has_running_loop = True
-    except RuntimeError:
-        has_running_loop = False
-
-    if has_running_loop:
-        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
-            return pool.submit(asyncio.run, coro).result()
-    return asyncio.run(coro)
+        return loop.run_until_complete(
+            _afetch_agent_card_impl(endpoint=endpoint, auth=auth, timeout=timeout)
+        )
+    finally:
+        loop.close()
 
 
 @cached(ttl=300, serializer=PickleSerializer())  # type: ignore[untyped-decorator]

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

@@ -5,7 +5,6 @@ from __future__ import annotations
 import asyncio
 import base64
 from collections.abc import AsyncIterator, Callable, MutableMapping
-import concurrent.futures
 from contextlib import asynccontextmanager
 import logging
 from typing import TYPE_CHECKING, Any, Final, Literal
@@ -195,43 +194,56 @@ def execute_a2a_delegation(
 
     Returns:
         TaskStateResult with status, result/error, history, and agent_card.
+
+    Raises:
+        RuntimeError: If called from an async context with a running event loop.
     """
-    coro = aexecute_a2a_delegation(
-        endpoint=endpoint,
-        auth=auth,
-        timeout=timeout,
-        task_description=task_description,
-        context=context,
-        context_id=context_id,
-        task_id=task_id,
-        reference_task_ids=reference_task_ids,
-        metadata=metadata,
-        extensions=extensions,
-        conversation_history=conversation_history,
-        agent_id=agent_id,
-        agent_role=agent_role,
-        agent_branch=agent_branch,
-        response_model=response_model,
-        turn_number=turn_number,
-        updates=updates,
-        from_task=from_task,
-        from_agent=from_agent,
-        skill_id=skill_id,
-        client_extensions=client_extensions,
-        transport=transport,
-        accepted_output_modes=accepted_output_modes,
-        input_files=input_files,
-    )
     try:
         asyncio.get_running_loop()
-        has_running_loop = True
-    except RuntimeError:
-        has_running_loop = False
+        raise RuntimeError(
+            "execute_a2a_delegation() cannot be called from an async context. "
+            "Use 'await aexecute_a2a_delegation()' instead."
+        )
+    except RuntimeError as e:
+        if "no running event loop" not in str(e).lower():
+            raise
 
-    if has_running_loop:
-        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
-            return pool.submit(asyncio.run, coro).result()
-    return asyncio.run(coro)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(
+            aexecute_a2a_delegation(
+                endpoint=endpoint,
+                auth=auth,
+                timeout=timeout,
+                task_description=task_description,
+                context=context,
+                context_id=context_id,
+                task_id=task_id,
+                reference_task_ids=reference_task_ids,
+                metadata=metadata,
+                extensions=extensions,
+                conversation_history=conversation_history,
+                agent_id=agent_id,
+                agent_role=agent_role,
+                agent_branch=agent_branch,
+                response_model=response_model,
+                turn_number=turn_number,
+                updates=updates,
+                from_task=from_task,
+                from_agent=from_agent,
+                skill_id=skill_id,
+                client_extensions=client_extensions,
+                transport=transport,
+                accepted_output_modes=accepted_output_modes,
+                input_files=input_files,
+            )
+        )
+    finally:
+        try:
+            loop.run_until_complete(loop.shutdown_asyncgens())
+        finally:
+            loop.close()
 
 
 async def aexecute_a2a_delegation(

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

@@ -1156,15 +1156,11 @@ class Agent(BaseAgent):
         # Process platform apps and MCP tools
         if self.apps:
             platform_tools = self.get_platform_tools(self.apps)
-            if platform_tools:
-                if self.tools is None:
-                    self.tools = []
+            if platform_tools and self.tools is not None:
                 self.tools.extend(platform_tools)
         if self.mcps:
             mcps = self.get_mcp_tools(self.mcps)
-            if mcps:
-                if self.tools is None:
-                    self.tools = []
+            if mcps and self.tools is not None:
                 self.tools.extend(mcps)
 
         # Prepare tools

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/agent_builder/base_agent_executor_mixin.py

@@ -30,9 +30,12 @@ class CrewAgentExecutorMixin:
         memory = getattr(self.agent, "memory", None) or (
             getattr(self.crew, "_memory", None) if self.crew else None
         )
-        if memory is None or not self.task or memory.read_only:
+        if memory is None or not self.task or getattr(memory, "_read_only", False):
             return
-        if f"Action: {sanitize_tool_name('Delegate work to coworker')}" in output.text:
+        if (
+            f"Action: {sanitize_tool_name('Delegate work to coworker')}"
+            in output.text
+        ):
             return
         try:
             raw = (
@@ -45,4 +48,6 @@ class CrewAgentExecutorMixin:
             if extracted:
                 memory.remember_many(extracted, agent_role=self.agent.role)
         except Exception as e:
-            self.agent._logger.log("error", f"Failed to save to memory: {e}")
+            self.agent._logger.log(
+                "error", f"Failed to save to memory: {e}"
+            )

lib/crewai/src/crewai/agents/cache/__init__.py

@@ -1,4 +1,5 @@
 from crewai.agents.cache.cache_handler import CacheHandler
 
 
+
 __all__ = ["CacheHandler"]

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

@@ -9,7 +9,6 @@ from __future__ import annotations
 import asyncio
 from collections.abc import Callable
 from concurrent.futures import ThreadPoolExecutor, as_completed
-import contextvars
 import inspect
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast
@@ -756,7 +755,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 with ThreadPoolExecutor(max_workers=max_workers) as pool:
                     futures = {
                         pool.submit(
-                            contextvars.copy_context().run,
                             self._execute_single_native_tool_call,
                             call_id=call_id,
                             func_name=func_name,

lib/crewai/src/crewai/cli/authentication/__init__.py

@@ -1,4 +1,5 @@
 from crewai.cli.authentication.main import AuthenticationCommand
 
 
+
 __all__ = ["AuthenticationCommand"]

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

@@ -143,7 +143,7 @@ def create_folder_structure(
         (folder_path / "src" / folder_name).mkdir(parents=True)
         (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
         (folder_path / "src" / folder_name / "config").mkdir(parents=True)
-
+        
         # Copy AGENTS.md to project root (top-level projects only)
         package_dir = Path(__file__).parent
         agents_md_src = package_dir / "templates" / "AGENTS.md"

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

@@ -1,5 +1,5 @@
-from pathlib import Path
 import shutil
+from pathlib import Path
 
 import click
 

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

@@ -22,15 +22,14 @@ class PlusAPI:
     EPHEMERAL_TRACING_RESOURCE = "/crewai_plus/api/v1/tracing/ephemeral"
     INTEGRATIONS_RESOURCE = "/crewai_plus/api/v1/integrations"
 
-    def __init__(self, api_key: str | None = None) -> None:
+    def __init__(self, api_key: str) -> None:
         self.api_key = api_key
         self.headers = {
+            "Authorization": f"Bearer {api_key}",
             "Content-Type": "application/json",
             "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
             "X-Crewai-Version": get_crewai_version(),
         }
-        if api_key:
-            self.headers["Authorization"] = f"Bearer {api_key}"
         settings = Settings()
         if settings.org_uuid:
             self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
@@ -49,13 +48,8 @@ class PlusAPI:
         with httpx.Client(trust_env=False, verify=verify) as client:
             return client.request(method, url, headers=self.headers, **kwargs)
 
-    def login_to_tool_repository(
-        self, user_identifier: str | None = None
-    ) -> httpx.Response:
-        payload = {}
-        if user_identifier:
-            payload["user_identifier"] = user_identifier
-        return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login", json=payload)
+    def login_to_tool_repository(self) -> httpx.Response:
+        return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login")
 
     def get_tool(self, handle: str) -> httpx.Response:
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")

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]==1.10.1"
+    "crewai[tools]==1.10.1a1"
 ]
 
 [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]==1.10.1"
+    "crewai[tools]==1.10.1a1"
 ]
 
 [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]==1.10.1"
+    "crewai[tools]==1.10.1a1"
 ]
 
 [tool.crewai]

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

@@ -23,7 +23,6 @@ from crewai.cli.utils import (
     tree_copy,
     tree_find_and_replace,
 )
-from crewai.events.listeners.tracing.utils import get_user_id
 
 
 console = Console()
@@ -170,9 +169,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         console.print(f"Successfully installed {handle}", style="bold green")
 
     def login(self) -> None:
-        login_response = self.plus_api_client.login_to_tool_repository(
-            user_identifier=get_user_id()
-        )
+        login_response = self.plus_api_client.login_to_tool_repository()
 
         if login_response.status_code != 200:
             console.print(

lib/crewai/src/crewai/crews/__init__.py

@@ -1,4 +1,5 @@
 from crewai.crews.crew_output import CrewOutput
 
 
+
 __all__ = ["CrewOutput"]

lib/crewai/src/crewai/events/base_event_listener.py

@@ -23,3 +23,4 @@ class BaseEventListener(ABC):
         Args:
             crewai_event_bus: The event bus to register listeners on.
         """
+        pass

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

@@ -15,7 +15,6 @@ from crewai.cli.plus_api import PlusAPI
 from crewai.cli.version import get_crewai_version
 from crewai.events.listeners.tracing.types import TraceEvent
 from crewai.events.listeners.tracing.utils import (
-    get_user_id,
     is_tracing_enabled_in_context,
     should_auto_collect_first_time_traces,
 )
@@ -68,7 +67,7 @@ class TraceBatchManager:
                 api_key=get_auth_token(),
             )
         except AuthError:
-            self.plus_api = PlusAPI()
+            self.plus_api = PlusAPI(api_key="")
         self.ephemeral_trace_url = None
 
     def initialize_batch(
@@ -121,6 +120,7 @@ class TraceBatchManager:
             payload = {
                 "trace_id": self.current_batch.batch_id,
                 "execution_type": execution_metadata.get("execution_type", "crew"),
+                "user_identifier": execution_metadata.get("user_context", None),
                 "execution_context": {
                     "crew_fingerprint": execution_metadata.get("crew_fingerprint"),
                     "crew_name": execution_metadata.get("crew_name", None),
@@ -140,7 +140,6 @@ class TraceBatchManager:
             }
             if use_ephemeral:
                 payload["ephemeral_trace_id"] = self.current_batch.batch_id
-                payload["user_identifier"] = get_user_id()
 
             response = (
                 self.plus_api.initialize_ephemeral_trace_batch(payload)

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

@@ -86,11 +86,3 @@ class LLMStreamChunkEvent(LLMEventBase):
     tool_call: ToolCall | None = None
     call_type: LLMCallType | None = None
     response_id: str | None = None
-
-
-class LLMThinkingChunkEvent(LLMEventBase):
-    """Event emitted when a thinking/reasoning chunk is received from a thinking model"""
-
-    type: str = "llm_thinking_chunk"
-    chunk: str
-    response_id: str | None = None

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 import asyncio
 from collections.abc import Callable, Coroutine
 from concurrent.futures import ThreadPoolExecutor, as_completed
-import contextvars
 from datetime import datetime
 import inspect
 import json
@@ -303,7 +302,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             super().__init__(
                 suppress_flow_events=True,
                 tracing=current_tracing if current_tracing else None,
-                max_method_calls=self.max_iter * 10,
             )
             self._flow_initialized = True
 
@@ -405,7 +403,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 self._setup_native_tools()
         return "initialized"
 
-    @listen("max_iterations_exceeded")
+    @listen("force_final_answer")
     def force_final_answer(self) -> Literal["agent_finished"]:
         """Force agent to provide final answer when max iterations exceeded."""
         formatted_answer = handle_max_iterations_exceeded(
@@ -657,11 +655,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             return "tool_result_is_final"
 
         reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-        reasoning_message_post: LLMMessage = {
+        reasoning_message: LLMMessage = {
             "role": "user",
             "content": reasoning_prompt,
         }
-        self.state.messages.append(reasoning_message_post)
+        self.state.messages.append(reasoning_message)
 
         return "tool_completed"
 
@@ -729,7 +727,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             max_workers = min(8, len(runnable_tool_calls))
             with ThreadPoolExecutor(max_workers=max_workers) as pool:
                 future_to_idx = {
-                    pool.submit(contextvars.copy_context().run, self._execute_single_native_tool_call, tool_call): idx
+                    pool.submit(self._execute_single_native_tool_call, tool_call): idx
                     for idx, tool_call in enumerate(runnable_tool_calls)
                 }
                 ordered_results: list[dict[str, Any] | None] = [None] * len(
@@ -888,10 +886,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         call_id, func_name, func_args = info
 
         # Parse arguments
-        parsed_args, parse_error = parse_tool_call_args(func_args, func_name, call_id)
+        args_dict, parse_error = parse_tool_call_args(func_args, func_name, call_id)
         if parse_error is not None:
             return parse_error
-        args_dict: dict[str, Any] = parsed_args or {}
 
         # Get agent_key for event tracking
         agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
@@ -1110,11 +1107,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
     def check_max_iterations(
         self,
     ) -> Literal[
-        "max_iterations_exceeded", "continue_reasoning", "continue_reasoning_native"
+        "force_final_answer", "continue_reasoning", "continue_reasoning_native"
     ]:
         """Check if max iterations reached before proceeding with reasoning."""
         if has_reached_max_iterations(self.state.iterations, self.max_iter):
-            return "max_iterations_exceeded"
+            return "force_final_answer"
         if self.state.use_native_tools:
             return "continue_reasoning_native"
         return "continue_reasoning"

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

@@ -497,50 +497,6 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
     def __bool__(self) -> bool:
         return bool(self._list)
 
-    def index(self, value: T, start: SupportsIndex = 0, stop: SupportsIndex | None = None) -> int:  # type: ignore[override]
-        if stop is None:
-            return self._list.index(value, start)
-        return self._list.index(value, start, stop)
-
-    def count(self, value: T) -> int:
-        return self._list.count(value)
-
-    def sort(self, *, key: Any = None, reverse: bool = False) -> None:
-        with self._lock:
-            self._list.sort(key=key, reverse=reverse)
-
-    def reverse(self) -> None:
-        with self._lock:
-            self._list.reverse()
-
-    def copy(self) -> list[T]:
-        return self._list.copy()
-
-    def __add__(self, other: list[T]) -> list[T]:
-        return self._list + other
-
-    def __radd__(self, other: list[T]) -> list[T]:
-        return other + self._list
-
-    def __iadd__(self, other: Iterable[T]) -> LockedListProxy[T]:
-        with self._lock:
-            self._list += list(other)
-        return self
-
-    def __mul__(self, n: SupportsIndex) -> list[T]:
-        return self._list * n
-
-    def __rmul__(self, n: SupportsIndex) -> list[T]:
-        return self._list * n
-
-    def __imul__(self, n: SupportsIndex) -> LockedListProxy[T]:
-        with self._lock:
-            self._list *= n
-        return self
-
-    def __reversed__(self) -> Iterator[T]:
-        return reversed(self._list)
-
     def __eq__(self, other: object) -> bool:
         """Compare based on the underlying list contents."""
         if isinstance(other, LockedListProxy):
@@ -623,23 +579,6 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
     def __bool__(self) -> bool:
         return bool(self._dict)
 
-    def copy(self) -> dict[str, T]:
-        return self._dict.copy()
-
-    def __or__(self, other: dict[str, T]) -> dict[str, T]:
-        return self._dict | other
-
-    def __ror__(self, other: dict[str, T]) -> dict[str, T]:
-        return other | self._dict
-
-    def __ior__(self, other: dict[str, T]) -> LockedDictProxy[T]:
-        with self._lock:
-            self._dict |= other
-        return self
-
-    def __reversed__(self) -> Iterator[str]:
-        return reversed(self._dict)
-
     def __eq__(self, other: object) -> bool:
         """Compare based on the underlying dict contents."""
         if isinstance(other, LockedDictProxy):
@@ -681,10 +620,6 @@ class StateProxy(Generic[T]):
         if name in ("_proxy_state", "_proxy_lock"):
             object.__setattr__(self, name, value)
         else:
-            if isinstance(value, LockedListProxy):
-                value = value._list
-            elif isinstance(value, LockedDictProxy):
-                value = value._dict
             with object.__getattribute__(self, "_proxy_lock"):
                 setattr(object.__getattribute__(self, "_proxy_state"), name, value)
 
@@ -757,7 +692,6 @@ class FlowMeta(type):
                     condition_type = getattr(
                         attr_value, "__condition_type__", OR_CONDITION
                     )
-
                     if (
                         hasattr(attr_value, "__trigger_condition__")
                         and attr_value.__trigger_condition__ is not None
@@ -835,7 +769,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         persistence: FlowPersistence | None = None,
         tracing: bool | None = None,
         suppress_flow_events: bool = False,
-        max_method_calls: int = 100,
         **kwargs: Any,
     ) -> None:
         """Initialize a new Flow instance.
@@ -844,7 +777,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             persistence: Optional persistence backend for storing flow states
             tracing: Whether to enable tracing. True=always enable, False=always disable, None=check environment/user settings
             suppress_flow_events: Whether to suppress flow event emissions (internal use)
-            max_method_calls: Maximum times a single method can be called per execution before raising RecursionError
             **kwargs: Additional state values to initialize or override
         """
         # Initialize basic instance attributes
@@ -860,8 +792,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._completed_methods: set[FlowMethodName] = (
             set()
         )  # Track completed methods for reload
-        self._method_call_counts: dict[FlowMethodName, int] = {}
-        self._max_method_calls = max_method_calls
         self._persistence: FlowPersistence | None = persistence
         self._is_execution_resuming: bool = False
         self._event_futures: list[Future[None]] = []
@@ -1898,7 +1828,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 self._method_outputs.clear()
                 self._pending_and_listeners.clear()
                 self._clear_or_listeners()
-                self._method_call_counts.clear()
             else:
                 # Only enter resumption mode if there are completed methods to
                 # replay.  When _completed_methods is empty (e.g. a pure
@@ -2640,16 +2569,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             - Skips execution if method was already completed (e.g., after reload)
             - Catches and logs any exceptions during execution, preventing individual listener failures from breaking the entire flow
         """
-        count = self._method_call_counts.get(listener_name, 0) + 1
-        if count > self._max_method_calls:
-            raise RecursionError(
-                f"Method '{listener_name}' has been called {self._max_method_calls} times in "
-                f"this flow execution, which indicates an infinite loop. "
-                f"This commonly happens when a @listen label matches the "
-                f"method's own name."
-            )
-        self._method_call_counts[listener_name] = count
-
         if listener_name in self._completed_methods:
             if self._is_execution_resuming:
                 # During resumption, skip execution but continue listeners

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/lite_agent.py

@@ -69,7 +69,9 @@ from crewai.llms.base_llm import BaseLLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.agent_utils import (
+    convert_tools_to_openai_schema,
     enforce_rpm_limit,
+    extract_tool_call_info,
     format_message_for_llm,
     get_llm_response,
     get_tool_names,
@@ -80,6 +82,7 @@ from crewai.utilities.agent_utils import (
     handle_unknown_error,
     has_reached_max_iterations,
     is_context_length_exceeded,
+    parse_tool_call_args,
     parse_tools,
     process_llm_response,
     render_text_description_and_args,
@@ -88,6 +91,7 @@ from crewai.utilities.converter import (
     Converter,
     ConverterError,
 )
+from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
 from crewai.utilities.i18n import I18N, get_i18n
@@ -274,6 +278,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     _printer: Printer = PrivateAttr(default_factory=Printer)
     _guardrail: GuardrailCallable | None = PrivateAttr(default=None)
     _guardrail_retry_count: int = PrivateAttr(default=0)
+    _use_native_tools: bool = PrivateAttr(default=False)
     _callbacks: list[TokenCalcHandler] = PrivateAttr(default_factory=list)
     _before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = (
         PrivateAttr(default_factory=get_before_llm_call_hooks)
@@ -517,6 +522,16 @@ class LiteAgent(FlowTrackable, BaseModel):
             self._iterations = 0
             self.tools_results = []
 
+            # Determine execution mode before building the system prompt so
+            # native mode gets a clean prompt without ReAct format instructions.
+            llm = cast(LLM, self.llm)
+            self._use_native_tools = bool(
+                hasattr(llm, "supports_function_calling")
+                and callable(getattr(llm, "supports_function_calling", None))
+                and llm.supports_function_calling()
+                and self._parsed_tools
+            )
+
             # Format messages for the LLM
             self._messages = self._format_messages(
                 messages, response_format=response_format, input_files=input_files
@@ -600,7 +615,7 @@ class LiteAgent(FlowTrackable, BaseModel):
 
     def _save_to_memory(self, output_text: str) -> None:
         """Extract discrete memories from the run and remember each. No-op if _memory is None or read-only."""
-        if self._memory is None or self._memory.read_only:
+        if self._memory is None or getattr(self._memory, "_read_only", False):
             return
         input_str = self._get_last_user_content() or "User request"
         try:
@@ -793,9 +808,18 @@ class LiteAgent(FlowTrackable, BaseModel):
             response_format: Optional response format to use instead of self.response_format
         """
         base_prompt = ""
-        if self._parsed_tools:
-            # Use the prompt template for agents with tools
-            base_prompt = self.i18n.slice("lite_agent_system_prompt_with_tools").format(
+        if self._parsed_tools and self._use_native_tools:
+            base_prompt = self.i18n.slice(
+                "lite_agent_system_prompt_native_tools"
+            ).format(
+                role=self.role,
+                backstory=self.backstory,
+                goal=self.goal,
+            )
+        elif self._parsed_tools:
+            base_prompt = self.i18n.slice(
+                "lite_agent_system_prompt_with_tools"
+            ).format(
                 role=self.role,
                 backstory=self.backstory,
                 goal=self.goal,
@@ -803,7 +827,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                 tool_names=get_tool_names(self._parsed_tools),
             )
         else:
-            # Use the prompt template for agents without tools
             base_prompt = self.i18n.slice(
                 "lite_agent_system_prompt_without_tools"
             ).format(
@@ -860,8 +883,501 @@ class LiteAgent(FlowTrackable, BaseModel):
     def _invoke_loop(
         self, response_model: type[BaseModel] | None = None
     ) -> AgentFinish:
-        """
-        Run the agent's thought process until it reaches a conclusion or max iterations.
+        """Run the agent's thought process until it reaches a conclusion or max iterations.
+
+        Checks if the LLM supports native function calling and uses that
+        approach if available, otherwise falls back to the ReAct text pattern.
+
+        Args:
+            response_model: Optional Pydantic model for native structured output.
+
+        Returns:
+            AgentFinish: The final result of the agent execution.
+        """
+        if self._use_native_tools:
+            return self._invoke_loop_native_tools(response_model=response_model)
+
+        return self._invoke_loop_react(response_model=response_model)
+
+    def _invoke_loop_native_tools(
+        self, response_model: type[BaseModel] | None = None
+    ) -> AgentFinish:
+        """Execute agent loop using native function calling.
+
+        Uses the LLM's native tool/function calling capability instead of the
+        text-based ReAct pattern. The LLM directly returns structured tool
+        calls which are executed and results fed back.
+
+        Args:
+            response_model: Optional Pydantic model for native structured output.
+
+        Returns:
+            AgentFinish: The final result of the agent execution.
+        """
+        openai_tools, available_functions, original_tools_by_name = (
+            convert_tools_to_openai_schema(self.tools)
+        )
+
+        while True:
+            try:
+                if has_reached_max_iterations(self._iterations, self.max_iterations):
+                    formatted_answer = handle_max_iterations_exceeded(
+                        None,
+                        printer=self._printer,
+                        i18n=self.i18n,
+                        messages=self._messages,
+                        llm=cast(LLM, self.llm),
+                        callbacks=self._callbacks,
+                        verbose=self.verbose,
+                    )
+                    self._show_logs(formatted_answer)
+                    return formatted_answer
+
+                enforce_rpm_limit(self.request_within_rpm_limit)
+
+                answer = get_llm_response(
+                    llm=cast(LLM, self.llm),
+                    messages=self._messages,
+                    callbacks=self._callbacks,
+                    printer=self._printer,
+                    tools=openai_tools,
+                    available_functions=None,
+                    from_agent=self,
+                    executor_context=self,
+                    response_model=response_model,
+                    verbose=self.verbose,
+                )
+
+                if (
+                    isinstance(answer, list)
+                    and answer
+                    and self._is_tool_call_list(answer)
+                ):
+                    tool_finish = self._handle_native_tool_calls(
+                        answer, available_functions, original_tools_by_name
+                    )
+                    if tool_finish is not None:
+                        return tool_finish
+                    continue
+
+                if isinstance(answer, BaseModel):
+                    output_json = answer.model_dump_json()
+                    formatted_answer = AgentFinish(
+                        thought="", output=answer, text=output_json
+                    )
+                    self._append_message(output_json)
+                    self._show_logs(formatted_answer)
+                    return formatted_answer
+
+                answer_str = str(answer) if not isinstance(answer, str) else answer
+                formatted_answer = AgentFinish(
+                    thought="", output=answer_str, text=answer_str
+                )
+                self._append_message(answer_str)
+                self._show_logs(formatted_answer)
+                return formatted_answer
+
+            except Exception as e:
+                if e.__class__.__module__.startswith("litellm"):
+                    raise e
+                if is_context_length_exceeded(e):
+                    handle_context_length(
+                        respect_context_window=self.respect_context_window,
+                        printer=self._printer,
+                        messages=self._messages,
+                        llm=cast(LLM, self.llm),
+                        callbacks=self._callbacks,
+                        i18n=self.i18n,
+                        verbose=self.verbose,
+                    )
+                    continue
+                handle_unknown_error(self._printer, e, verbose=self.verbose)
+                raise e
+            finally:
+                self._iterations += 1
+
+    @staticmethod
+    def _is_tool_call_list(response: list[Any]) -> bool:
+        """Check if a response is a list of native tool calls.
+
+        Supports OpenAI, Anthropic, Bedrock, and Gemini formats.
+        """
+        if not response:
+            return False
+        first_item = response[0]
+        if hasattr(first_item, "function") or (
+            isinstance(first_item, dict) and "function" in first_item
+        ):
+            return True
+        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
+        if (
+            isinstance(first_item, dict)
+            and "name" in first_item
+            and "input" in first_item
+        ):
+            return True
+        if hasattr(first_item, "function_call") and first_item.function_call:
+            return True
+        return False
+
+    def _handle_native_tool_calls(
+        self,
+        tool_calls: list[Any],
+        available_functions: dict[str, Callable[..., Any]],
+        original_tools_by_name: dict[str, BaseTool],
+    ) -> AgentFinish | None:
+        """Execute native tool calls and feed results back into message history.
+
+        Uses parallel execution via ``ThreadPoolExecutor`` when safe (no
+        ``result_as_answer`` or ``max_usage_count`` tools in the batch).
+        Falls back to sequential execution otherwise.
+
+        Args:
+            tool_calls: Tool call objects from the LLM response.
+            available_functions: Mapping of sanitized tool names to callables.
+            original_tools_by_name: Mapping of sanitized tool names to original
+                BaseTool instances.
+
+        Returns:
+            AgentFinish if a tool with result_as_answer=True was called,
+            None otherwise (loop continues).
+        """
+        from concurrent.futures import ThreadPoolExecutor, as_completed
+
+        parsed_calls = [
+            parsed
+            for tc in tool_calls
+            if (parsed := extract_tool_call_info(tc)) is not None
+        ]
+        if not parsed_calls:
+            return None
+
+        # Single assistant message with all tool calls (matches OpenAI API spec)
+        self._messages.append({
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": call_id,
+                    "type": "function",
+                    "function": {
+                        "name": func_name,
+                        "arguments": func_args
+                        if isinstance(func_args, str)
+                        else json.dumps(func_args),
+                    },
+                }
+                for call_id, func_name, func_args in parsed_calls
+            ],
+        })
+
+        # Determine if parallel execution is safe for this batch.
+        # Usage counters are not thread-safe, and result_as_answer requires
+        # immediate return, so both force sequential execution.
+        can_parallelize = len(parsed_calls) > 1 and not any(
+            (
+                original_tools_by_name.get(fn)
+                and (
+                    getattr(original_tools_by_name.get(fn), "result_as_answer", False)
+                    or getattr(original_tools_by_name.get(fn), "max_usage_count", None)
+                    is not None
+                )
+            )
+            for _, fn, _ in parsed_calls
+        )
+
+        if can_parallelize:
+            execution_plan = [
+                (cid, fn, fa, original_tools_by_name.get(fn))
+                for cid, fn, fa in parsed_calls
+            ]
+            max_workers = min(8, len(execution_plan))
+            ordered_results: list[dict[str, Any] | None] = [None] * len(
+                execution_plan
+            )
+            with ThreadPoolExecutor(max_workers=max_workers) as pool:
+                futures = {
+                    pool.submit(
+                        self._execute_native_tool_call,
+                        call_id=cid,
+                        func_name=fn,
+                        func_args=fa,
+                        available_functions=available_functions,
+                        original_tool=ot,
+                    ): idx
+                    for idx, (cid, fn, fa, ot) in enumerate(execution_plan)
+                }
+                for future in as_completed(futures):
+                    ordered_results[futures[future]] = future.result()
+
+            for exec_result in ordered_results:
+                if exec_result is None:
+                    continue
+                self._messages.append({
+                    "role": "tool",
+                    "tool_call_id": exec_result["call_id"],
+                    "name": exec_result["func_name"],
+                    "content": exec_result["result"],
+                })
+                if self.verbose:
+                    cache_tag = " (from cache)" if exec_result["from_cache"] else ""
+                    self._printer.print(
+                        content=f"Tool {exec_result['func_name']} executed{cache_tag}: {exec_result['result'][:200]}",
+                        color="green",
+                    )
+                orig = original_tools_by_name.get(exec_result["func_name"])
+                if orig and getattr(orig, "result_as_answer", False):
+                    finished = AgentFinish(
+                        thought="", output=exec_result["result"], text=exec_result["result"]
+                    )
+                    self._show_logs(finished)
+                    return finished
+        else:
+            # Sequential execution: process each call one at a time.
+            for call_id, func_name, func_args in parsed_calls:
+                exec_result = self._execute_native_tool_call(
+                    call_id=call_id,
+                    func_name=func_name,
+                    func_args=func_args,
+                    available_functions=available_functions,
+                    original_tool=original_tools_by_name.get(func_name),
+                )
+
+                self._messages.append({
+                    "role": "tool",
+                    "tool_call_id": exec_result["call_id"],
+                    "name": exec_result["func_name"],
+                    "content": exec_result["result"],
+                })
+                if self.verbose:
+                    cache_tag = " (from cache)" if exec_result["from_cache"] else ""
+                    self._printer.print(
+                        content=f"Tool {exec_result['func_name']} executed{cache_tag}: {exec_result['result'][:200]}",
+                        color="green",
+                    )
+
+                original_tool = original_tools_by_name.get(func_name)
+                if original_tool and getattr(original_tool, "result_as_answer", False):
+                    finished = AgentFinish(
+                        thought="", output=exec_result["result"], text=exec_result["result"]
+                    )
+                    self._show_logs(finished)
+                    return finished
+
+        reasoning_prompt = self.i18n.slice("post_tool_reasoning")
+        self._messages.append({"role": "user", "content": reasoning_prompt})
+        return None
+
+    def _execute_native_tool_call(
+        self,
+        *,
+        call_id: str,
+        func_name: str,
+        func_args: str | dict[str, Any],
+        available_functions: dict[str, Callable[..., Any]],
+        original_tool: BaseTool | None = None,
+    ) -> dict[str, Any]:
+        """Execute a single native tool call.
+
+        Handles argument parsing, usage-limit checks, caching, and hook
+        invocation.
+
+        Args:
+            call_id: The tool call ID from the LLM.
+            func_name: Sanitized tool function name.
+            func_args: Raw arguments (JSON string or dict).
+            available_functions: Mapping of tool names to callables.
+            original_tool: The original BaseTool instance, if available.
+
+        Returns:
+            Dict with keys ``call_id``, ``func_name``, ``result``,
+            ``from_cache``, and ``original_tool``.
+        """
+        from datetime import datetime
+
+        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,
+        )
+
+        args_dict, parse_error = parse_tool_call_args(
+            func_args, func_name, call_id, original_tool
+        )
+        if parse_error is not None:
+            return {
+                "call_id": call_id,
+                "func_name": func_name,
+                "result": cast(str, parse_error["result"]),
+                "from_cache": False,
+                "original_tool": original_tool,
+            }
+
+        if (
+            original_tool
+            and getattr(original_tool, "max_usage_count", None) is not None
+            and getattr(original_tool, "current_usage_count", 0)
+            >= original_tool.max_usage_count
+        ):
+            return {
+                "call_id": call_id,
+                "func_name": func_name,
+                "result": (
+                    f"Tool '{func_name}' has reached its usage limit of "
+                    f"{original_tool.max_usage_count} times and cannot be used anymore."
+                ),
+                "from_cache": False,
+                "original_tool": original_tool,
+            }
+
+        from_cache = False
+        result: str = f"Tool '{func_name}' not found"
+        input_str = json.dumps(args_dict) if args_dict else ""
+
+        if self._cache_handler:
+            cached = self._cache_handler.read(tool=func_name, input=input_str)
+            if cached is not None:
+                result = str(cached) if not isinstance(cached, str) else cached
+                from_cache = True
+
+        started_at = datetime.now()
+        crewai_event_bus.emit(
+            self,
+            event=ToolUsageStartedEvent(
+                tool_name=func_name,
+                tool_args=args_dict,
+                from_agent=self,
+                agent_key=self.key,
+            ),
+        )
+
+        structured_tool: CrewStructuredTool | None = next(
+            (t for t in self._parsed_tools if sanitize_tool_name(t.name) == func_name),
+            None,
+        )
+
+        hook_blocked = False
+        before_ctx = ToolCallHookContext(
+            tool_name=func_name,
+            tool_input=args_dict,
+            tool=structured_tool,  # type: ignore[arg-type]
+            agent=self,
+            task=None,
+            crew=None,
+        )
+        try:
+            for hook in get_before_tool_call_hooks():
+                if hook(before_ctx) is False:
+                    hook_blocked = True
+                    break
+        except Exception as hook_err:
+            if self.verbose:
+                self._printer.print(
+                    content=f"Error in before_tool_call hook: {hook_err}",
+                    color="red",
+                )
+
+        error_event_emitted = False
+        if hook_blocked:
+            result = f"Tool execution blocked by hook. Tool: {func_name}"
+        elif not from_cache and func_name in available_functions:
+            try:
+                raw_result = available_functions[func_name](**(args_dict or {}))
+                result = str(raw_result) if not isinstance(raw_result, str) else raw_result
+
+                if self._cache_handler:
+                    should_cache = True
+                    if (
+                        original_tool
+                        and hasattr(original_tool, "cache_function")
+                        and callable(original_tool.cache_function)
+                    ):
+                        should_cache = original_tool.cache_function(args_dict, raw_result)
+                    if should_cache:
+                        self._cache_handler.add(
+                            tool=func_name, input=input_str, output=raw_result
+                        )
+            except Exception as e:
+                result = f"Error executing tool '{func_name}': {e}"
+                error_event_emitted = True
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageErrorEvent(
+                        tool_name=func_name,
+                        tool_args=args_dict,
+                        from_agent=self,
+                        agent_key=self.key,
+                        error=e,
+                    ),
+                )
+
+        after_ctx = ToolCallHookContext(
+            tool_name=func_name,
+            tool_input=args_dict,
+            tool=structured_tool,  # type: ignore[arg-type]
+            agent=self,
+            task=None,
+            crew=None,
+            tool_result=result,
+        )
+        try:
+            for after_hook in get_after_tool_call_hooks():
+                after_result = after_hook(after_ctx)
+                if after_result is not None:
+                    result = after_result
+                    after_ctx.tool_result = result
+        except Exception as hook_err:
+            if self.verbose:
+                self._printer.print(
+                    content=f"Error in after_tool_call hook: {hook_err}",
+                    color="red",
+                )
+
+        if not error_event_emitted:
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageFinishedEvent(
+                    output=result,
+                    tool_name=func_name,
+                    tool_args=args_dict,
+                    from_agent=self,
+                    agent_key=self.key,
+                    started_at=started_at,
+                    finished_at=datetime.now(),
+                ),
+            )
+
+        self.tools_results.append({
+            "result": result,
+            "tool_name": func_name,
+            "tool_args": args_dict,
+        })
+
+        return {
+            "call_id": call_id,
+            "func_name": func_name,
+            "result": result,
+            "from_cache": from_cache,
+            "original_tool": original_tool,
+        }
+
+    def _invoke_loop_react(
+        self, response_model: type[BaseModel] | None = None
+    ) -> AgentFinish:
+        """Execute agent loop using the ReAct text-based pattern.
+
+        This is the fallback when the LLM does not support native function calling.
 
         Args:
             response_model: Optional Pydantic model for native structured output.
@@ -869,7 +1385,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         Returns:
             AgentFinish: The final result of the agent execution.
         """
-        # Execute the agent loop
         formatted_answer: AgentAction | AgentFinish | None = None
         while not isinstance(formatted_answer, AgentFinish):
             try:
@@ -949,7 +1464,6 @@ class LiteAgent(FlowTrackable, BaseModel):
 
             except Exception as e:
                 if e.__class__.__module__.startswith("litellm"):
-                    # Do not retry on litellm errors
                     raise e
                 if is_context_length_exceeded(e):
                     handle_context_length(

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

@@ -26,7 +26,6 @@ from crewai.events.types.llm_events import (
     LLMCallStartedEvent,
     LLMCallType,
     LLMStreamChunkEvent,
-    LLMThinkingChunkEvent,
 )
 from crewai.events.types.tool_usage_events import (
     ToolUsageErrorEvent,
@@ -369,6 +368,9 @@ class BaseLLM(ABC):
         """Emit LLM call started event."""
         from crewai.utilities.serialization import to_serializable
 
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
         crewai_event_bus.emit(
             self,
             event=LLMCallStartedEvent(
@@ -414,6 +416,9 @@ class BaseLLM(ABC):
         from_agent: Agent | None = None,
     ) -> None:
         """Emit LLM call failed event."""
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
         crewai_event_bus.emit(
             self,
             event=LLMCallFailedEvent(
@@ -444,6 +449,9 @@ class BaseLLM(ABC):
             call_type: The type of LLM call (LLM_CALL or TOOL_CALL).
             response_id: Unique ID for a particular LLM response, chunks have same response_id.
         """
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
         crewai_event_bus.emit(
             self,
             event=LLMStreamChunkEvent(
@@ -457,32 +465,6 @@ class BaseLLM(ABC):
             ),
         )
 
-    def _emit_thinking_chunk_event(
-        self,
-        chunk: str,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
-        response_id: str | None = None,
-    ) -> None:
-        """Emit thinking/reasoning chunk event from a thinking model.
-
-        Args:
-            chunk: The thinking text content.
-            from_task: The task that initiated the call.
-            from_agent: The agent that initiated the call.
-            response_id: Unique ID for a particular LLM response.
-        """
-        crewai_event_bus.emit(
-            self,
-            event=LLMThinkingChunkEvent(
-                chunk=chunk,
-                from_task=from_task,
-                from_agent=from_agent,
-                response_id=response_id,
-                call_id=get_current_call_id(),
-            ),
-        )
-
     def _handle_tool_execution(
         self,
         function_name: str,

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:

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")
@@ -1795,56 +1794,53 @@ 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

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

@@ -61,7 +61,6 @@ class GeminiCompletion(BaseLLM):
         interceptor: BaseInterceptor[Any, Any] | None = None,
         use_vertexai: bool | None = None,
         response_format: type[BaseModel] | None = None,
-        thinking_config: types.ThinkingConfig | None = None,
         **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
@@ -94,10 +93,6 @@ class GeminiCompletion(BaseLLM):
                          api_version="v1" is automatically configured.
             response_format: Pydantic model for structured output. Used as default when
                            response_model is not passed to call()/acall() methods.
-            thinking_config: ThinkingConfig for thinking models (gemini-2.5+, gemini-3+).
-                           Controls thought output via include_thoughts, thinking_budget,
-                           and thinking_level. When None, thinking models automatically
-                           get include_thoughts=True so thought content is surfaced.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -144,14 +139,6 @@ class GeminiCompletion(BaseLLM):
             version_match and float(version_match.group(1)) >= 2.0
         )
 
-        self.thinking_config = thinking_config
-        if (
-            self.thinking_config is None
-            and version_match
-            and float(version_match.group(1)) >= 2.5
-        ):
-            self.thinking_config = types.ThinkingConfig(include_thoughts=True)
-
     @property
     def stop(self) -> list[str]:
         """Get stop sequences sent to the API."""
@@ -533,9 +520,6 @@ class GeminiCompletion(BaseLLM):
         if self.safety_settings:
             config_params["safety_settings"] = self.safety_settings
 
-        if self.thinking_config is not None:
-            config_params["thinking_config"] = self.thinking_config
-
         return types.GenerateContentConfig(**config_params)
 
     def _convert_tools_for_interference(  # type: ignore[override]
@@ -634,17 +618,9 @@ class GeminiCompletion(BaseLLM):
                 function_response_part = types.Part.from_function_response(
                     name=tool_name, response=response_data
                 )
-                if (
-                    contents
-                    and contents[-1].role == "user"
-                    and contents[-1].parts
-                    and contents[-1].parts[-1].function_response is not None
-                ):
-                    contents[-1].parts.append(function_response_part)
-                else:
-                    contents.append(
-                        types.Content(role="user", parts=[function_response_part])
-                    )
+                contents.append(
+                    types.Content(role="user", parts=[function_response_part])
+                )
             elif role == "assistant" and message.get("tool_calls"):
                 raw_parts: list[Any] | None = message.get("raw_tool_call_parts")
                 if raw_parts and all(isinstance(p, types.Part) for p in raw_parts):
@@ -955,6 +931,15 @@ class GeminiCompletion(BaseLLM):
         if chunk.usage_metadata:
             usage_data = self._extract_token_usage(chunk)
 
+        if chunk.text:
+            full_response += chunk.text
+            self._emit_stream_chunk_event(
+                chunk=chunk.text,
+                from_task=from_task,
+                from_agent=from_agent,
+                response_id=response_id,
+            )
+
         if chunk.candidates:
             candidate = chunk.candidates[0]
             if candidate.content and candidate.content.parts:
@@ -991,21 +976,6 @@ class GeminiCompletion(BaseLLM):
                             call_type=LLMCallType.TOOL_CALL,
                             response_id=response_id,
                         )
-                    elif part.thought and part.text:
-                        self._emit_thinking_chunk_event(
-                            chunk=part.text,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            response_id=response_id,
-                        )
-                    elif part.text:
-                        full_response += part.text
-                        self._emit_stream_chunk_event(
-                            chunk=part.text,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            response_id=response_id,
-                        )
 
         return full_response, function_calls, usage_data
 
@@ -1359,7 +1329,7 @@ class GeminiCompletion(BaseLLM):
         text_parts = [
             part.text
             for part in candidate.content.parts
-            if part.text and not part.thought
+            if hasattr(part, "text") and part.text
         ]
 
         return "".join(text_parts)

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/__init__.py

@@ -19,7 +19,6 @@ from crewai.memory.types import (
     embed_texts,
 )
 
-
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),
     "EncodingFlow": ("crewai.memory.encoding_flow", "EncodingFlow"),

lib/crewai/src/crewai/memory/memory_scope.py

@@ -3,9 +3,11 @@
 from __future__ import annotations
 
 from datetime import datetime
-from typing import Any, Literal
+from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
+
+if TYPE_CHECKING:
+    from crewai.memory.unified_memory import Memory
 
 from crewai.memory.types import (
     _RECALL_OVERSAMPLE_FACTOR,
@@ -13,38 +15,22 @@ from crewai.memory.types import (
     MemoryRecord,
     ScopeInfo,
 )
-from crewai.memory.unified_memory import Memory
 
 
-class MemoryScope(BaseModel):
+class MemoryScope:
     """View of Memory restricted to a root path. All operations are scoped under that path."""
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    def __init__(self, memory: Memory, root_path: str) -> None:
+        """Initialize scope.
 
-    root_path: str = Field(default="/")
-
-    _memory: Memory = PrivateAttr()
-    _root: str = PrivateAttr()
-
-    @model_validator(mode="wrap")
-    @classmethod
-    def _accept_memory(cls, data: Any, handler: Any) -> MemoryScope:
-        """Extract memory dependency and normalize root path before validation."""
-        if isinstance(data, MemoryScope):
-            return data
-        memory = data.pop("memory")
-        instance: MemoryScope = handler(data)
-        instance._memory = memory
-        root = instance.root_path.rstrip("/") or ""
-        if root and not root.startswith("/"):
-            root = "/" + root
-        instance._root = root
-        return instance
-
-    @property
-    def read_only(self) -> bool:
-        """Whether the underlying memory is read-only."""
-        return self._memory.read_only
+        Args:
+            memory: The underlying Memory instance.
+            root_path: Root path for this scope (e.g. /agent/1).
+        """
+        self._memory = memory
+        self._root = root_path.rstrip("/") or ""
+        if self._root and not self._root.startswith("/"):
+            self._root = "/" + self._root
 
     def _scope_path(self, scope: str | None) -> str:
         if not scope or scope == "/":
@@ -66,7 +52,7 @@ class MemoryScope(BaseModel):
         importance: float | None = None,
         source: str | None = None,
         private: bool = False,
-    ) -> MemoryRecord | None:
+    ) -> MemoryRecord:
         """Remember content; scope is relative to this scope's root."""
         path = self._scope_path(scope)
         return self._memory.remember(
@@ -85,7 +71,7 @@ class MemoryScope(BaseModel):
         scope: str | None = None,
         categories: list[str] | None = None,
         limit: int = 10,
-        depth: Literal["shallow", "deep"] = "deep",
+        depth: str = "deep",
         source: str | None = None,
         include_private: bool = False,
     ) -> list[MemoryMatch]:
@@ -152,34 +138,34 @@ class MemoryScope(BaseModel):
         """Return a narrower scope under this scope."""
         child = path.strip("/")
         if not child:
-            return MemoryScope(memory=self._memory, root_path=self._root or "/")
+            return MemoryScope(self._memory, self._root or "/")
         base = self._root.rstrip("/") or ""
         new_root = f"{base}/{child}" if base else f"/{child}"
-        return MemoryScope(memory=self._memory, root_path=new_root)
+        return MemoryScope(self._memory, new_root)
 
 
-class MemorySlice(BaseModel):
+class MemorySlice:
     """View over multiple scopes: recall searches all, remember is a no-op when read_only."""
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    def __init__(
+        self,
+        memory: Memory,
+        scopes: list[str],
+        categories: list[str] | None = None,
+        read_only: bool = True,
+    ) -> None:
+        """Initialize slice.
 
-    scopes: list[str] = Field(default_factory=list)
-    categories: list[str] | None = Field(default=None)
-    read_only: bool = Field(default=True)
-
-    _memory: Memory = PrivateAttr()
-
-    @model_validator(mode="wrap")
-    @classmethod
-    def _accept_memory(cls, data: Any, handler: Any) -> MemorySlice:
-        """Extract memory dependency and normalize scopes before validation."""
-        if isinstance(data, MemorySlice):
-            return data
-        memory = data.pop("memory")
-        data["scopes"] = [s.rstrip("/") or "/" for s in data.get("scopes", [])]
-        instance: MemorySlice = handler(data)
-        instance._memory = memory
-        return instance
+        Args:
+            memory: The underlying Memory instance.
+            scopes: List of scope paths to include.
+            categories: Optional category filter for recall.
+            read_only: If True, remember() is a silent no-op.
+        """
+        self._memory = memory
+        self._scopes = [s.rstrip("/") or "/" for s in scopes]
+        self._categories = categories
+        self._read_only = read_only
 
     def remember(
         self,
@@ -192,7 +178,7 @@ class MemorySlice(BaseModel):
         private: bool = False,
     ) -> MemoryRecord | None:
         """Remember into an explicit scope. No-op when read_only=True."""
-        if self.read_only:
+        if self._read_only:
             return None
         return self._memory.remember(
             content,
@@ -210,14 +196,14 @@ class MemorySlice(BaseModel):
         scope: str | None = None,
         categories: list[str] | None = None,
         limit: int = 10,
-        depth: Literal["shallow", "deep"] = "deep",
+        depth: str = "deep",
         source: str | None = None,
         include_private: bool = False,
     ) -> list[MemoryMatch]:
         """Recall across all slice scopes; results merged and re-ranked."""
-        cats = categories or self.categories
+        cats = categories or self._categories
         all_matches: list[MemoryMatch] = []
-        for sc in self.scopes:
+        for sc in self._scopes:
             matches = self._memory.recall(
                 query,
                 scope=sc,
@@ -245,7 +231,7 @@ class MemorySlice(BaseModel):
     def list_scopes(self, path: str = "/") -> list[str]:
         """List scopes across all slice roots."""
         out: list[str] = []
-        for sc in self.scopes:
+        for sc in self._scopes:
             full = f"{sc.rstrip('/')}{path}" if sc != "/" else path
             out.extend(self._memory.list_scopes(full))
         return sorted(set(out))
@@ -257,23 +243,15 @@ class MemorySlice(BaseModel):
         oldest: datetime | None = None
         newest: datetime | None = None
         children: list[str] = []
-        for sc in self.scopes:
+        for sc in self._scopes:
             full = f"{sc.rstrip('/')}{path}" if sc != "/" else path
             inf = self._memory.info(full)
             total_records += inf.record_count
             all_categories.update(inf.categories)
             if inf.oldest_record:
-                oldest = (
-                    inf.oldest_record
-                    if oldest is None
-                    else min(oldest, inf.oldest_record)
-                )
+                oldest = inf.oldest_record if oldest is None else min(oldest, inf.oldest_record)
             if inf.newest_record:
-                newest = (
-                    inf.newest_record
-                    if newest is None
-                    else max(newest, inf.newest_record)
-                )
+                newest = inf.newest_record if newest is None else max(newest, inf.newest_record)
             children.extend(inf.child_scopes)
         return ScopeInfo(
             path=path,
@@ -287,7 +265,7 @@ class MemorySlice(BaseModel):
     def list_categories(self, path: str | None = None) -> dict[str, int]:
         """Categories and counts across slice scopes."""
         counts: dict[str, int] = {}
-        for sc in self.scopes:
+        for sc in self._scopes:
             full = (f"{sc.rstrip('/')}{path}" if sc != "/" else path) if path else sc
             for k, v in self._memory.list_categories(full).items():
                 counts[k] = counts.get(k, 0) + v

lib/crewai/src/crewai/memory/recall_flow.py

@@ -2,6 +2,7 @@
 
 Implements adaptive-depth retrieval with:
 - LLM query distillation into targeted sub-queries
+- Keyword-driven category filtering
 - Time-based filtering from temporal hints
 - Parallel multi-query, multi-scope search
 - Confidence-based routing with iterative deepening (budget loop)
@@ -80,8 +81,11 @@ class RecallFlow(Flow[RecallState]):
     # ------------------------------------------------------------------
 
     def _merged_categories(self) -> list[str] | None:
-        """Return caller-supplied categories, or None if empty."""
-        return self.state.categories or None
+        """Merge caller-supplied and LLM-inferred categories."""
+        merged = list(
+            set((self.state.categories or []))
+        )
+        return merged or None
 
     def _do_search(self) -> list[dict[str, Any]]:
         """Run parallel search across (embeddings x scopes) with filters.

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

@@ -6,9 +6,7 @@ from concurrent.futures import Future, ThreadPoolExecutor
 from datetime import datetime
 import threading
 import time
-from typing import TYPE_CHECKING, Annotated, Any, Literal
-
-from pydantic import BaseModel, ConfigDict, Field, PlainValidator, PrivateAttr
+from typing import TYPE_CHECKING, Any, Literal
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.memory_events import (
@@ -41,18 +39,13 @@ if TYPE_CHECKING:
     )
 
 
-def _passthrough(v: Any) -> Any:
-    """PlainValidator that accepts any value, bypassing strict union discrimination."""
-    return v
-
-
 def _default_embedder() -> OpenAIEmbeddingFunction:
     """Build default OpenAI embedder for memory."""
     spec: OpenAIProviderSpec = {"provider": "openai", "config": {}}
     return build_embedder(spec)
 
 
-class Memory(BaseModel):
+class Memory:
     """Unified memory: standalone, LLM-analyzed, with intelligent recall flow.
 
     Works without agent/crew. Uses LLM to infer scope, categories, importance on save.
@@ -60,119 +53,116 @@ class Memory(BaseModel):
     pluggable storage (LanceDB default).
     """
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    def __init__(
+        self,
+        llm: BaseLLM | str = "gpt-4o-mini",
+        storage: StorageBackend | str = "lancedb",
+        embedder: Any = None,
+        # -- Scoring weights --
+        # These three weights control how recall results are ranked.
+        # The composite score is: semantic_weight * similarity + recency_weight * decay + importance_weight * importance.
+        # They should sum to ~1.0 for intuitive scoring.
+        recency_weight: float = 0.3,
+        semantic_weight: float = 0.5,
+        importance_weight: float = 0.2,
+        # How quickly old memories lose relevance. The recency score halves every
+        # N days (exponential decay). Lower = faster forgetting; higher = longer relevance.
+        recency_half_life_days: int = 30,
+        # -- Consolidation --
+        # When remembering new content, if an existing record has similarity >= this
+        # threshold, the LLM is asked to merge/update/delete. Set to 1.0 to disable.
+        consolidation_threshold: float = 0.85,
+        # Max existing records to compare against when checking for consolidation.
+        consolidation_limit: int = 5,
+        # -- Save defaults --
+        # Importance assigned to new memories when no explicit value is given and
+        # the LLM analysis path is skipped (all fields provided by the caller).
+        default_importance: float = 0.5,
+        # -- Recall depth control --
+        # These thresholds govern the RecallFlow router that decides between
+        # returning results immediately ("synthesize") vs. doing an extra
+        # LLM-driven exploration round ("explore_deeper").
+        #   confidence >= confidence_threshold_high  => always synthesize
+        #   confidence <  confidence_threshold_low   => explore deeper (if budget > 0)
+        #   complex query + confidence < complex_query_threshold => explore deeper
+        confidence_threshold_high: float = 0.8,
+        confidence_threshold_low: float = 0.5,
+        complex_query_threshold: float = 0.7,
+        # How many LLM-driven exploration rounds the RecallFlow is allowed to run.
+        # 0 = always shallow (vector search only); higher = more thorough but slower.
+        exploration_budget: int = 1,
+        # Queries shorter than this skip LLM analysis (saving ~1-3s).
+        # Longer queries (full task descriptions) benefit from LLM distillation.
+        query_analysis_threshold: int = 200,
+        # When True, all write operations (remember, remember_many) are silently
+        # skipped. Useful for sharing a read-only view of memory across agents
+        # without any of them persisting new memories.
+        read_only: bool = False,
+    ) -> None:
+        """Initialize Memory.
 
-    llm: Annotated[BaseLLM | str, PlainValidator(_passthrough)] = Field(
-        default="gpt-4o-mini",
-        description="LLM for analysis (model name or BaseLLM instance).",
-    )
-    storage: Annotated[StorageBackend | str, PlainValidator(_passthrough)] = Field(
-        default="lancedb",
-        description="Storage backend instance or path string.",
-    )
-    embedder: Any = Field(
-        default=None,
-        description="Embedding callable, provider config dict, or None for default OpenAI.",
-    )
-    recency_weight: float = Field(
-        default=0.3,
-        description="Weight for recency in the composite relevance score.",
-    )
-    semantic_weight: float = Field(
-        default=0.5,
-        description="Weight for semantic similarity in the composite relevance score.",
-    )
-    importance_weight: float = Field(
-        default=0.2,
-        description="Weight for importance in the composite relevance score.",
-    )
-    recency_half_life_days: int = Field(
-        default=30,
-        description="Recency score halves every N days (exponential decay).",
-    )
-    consolidation_threshold: float = Field(
-        default=0.85,
-        description="Similarity above which consolidation is triggered on save.",
-    )
-    consolidation_limit: int = Field(
-        default=5,
-        description="Max existing records to compare during consolidation.",
-    )
-    default_importance: float = Field(
-        default=0.5,
-        description="Default importance when not provided or inferred.",
-    )
-    confidence_threshold_high: float = Field(
-        default=0.8,
-        description="Recall confidence above which results are returned directly.",
-    )
-    confidence_threshold_low: float = Field(
-        default=0.5,
-        description="Recall confidence below which deeper exploration is triggered.",
-    )
-    complex_query_threshold: float = Field(
-        default=0.7,
-        description="For complex queries, explore deeper below this confidence.",
-    )
-    exploration_budget: int = Field(
-        default=1,
-        description="Number of LLM-driven exploration rounds during deep recall.",
-    )
-    query_analysis_threshold: int = Field(
-        default=200,
-        description="Queries shorter than this skip LLM analysis during deep recall.",
-    )
-    read_only: bool = Field(
-        default=False,
-        description="If True, remember() and remember_many() are silent no-ops.",
-    )
-
-    _config: MemoryConfig = PrivateAttr()
-    _llm_instance: BaseLLM | None = PrivateAttr(default=None)
-    _embedder_instance: Any = PrivateAttr(default=None)
-    _storage: StorageBackend = PrivateAttr()
-    _save_pool: ThreadPoolExecutor = PrivateAttr(
-        default_factory=lambda: ThreadPoolExecutor(
-            max_workers=1, thread_name_prefix="memory-save"
-        )
-    )
-    _pending_saves: list[Future[Any]] = PrivateAttr(default_factory=list)
-    _pending_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
-
-    def model_post_init(self, __context: Any) -> None:
-        """Initialize runtime state from field values."""
+        Args:
+            llm: LLM for analysis (model name or BaseLLM instance).
+            storage: Backend: "lancedb" or a StorageBackend instance.
+            embedder: Embedding callable, provider config dict, or None (default OpenAI).
+            recency_weight: Weight for recency in the composite relevance score.
+            semantic_weight: Weight for semantic similarity in the composite relevance score.
+            importance_weight: Weight for importance in the composite relevance score.
+            recency_half_life_days: Recency score halves every N days (exponential decay).
+            consolidation_threshold: Similarity above which consolidation is triggered on save.
+            consolidation_limit: Max existing records to compare during consolidation.
+            default_importance: Default importance when not provided or inferred.
+            confidence_threshold_high: Recall confidence above which results are returned directly.
+            confidence_threshold_low: Recall confidence below which deeper exploration is triggered.
+            complex_query_threshold: For complex queries, explore deeper below this confidence.
+            exploration_budget: Number of LLM-driven exploration rounds during deep recall.
+            query_analysis_threshold: Queries shorter than this skip LLM analysis during deep recall.
+            read_only: If True, remember() and remember_many() are silent no-ops.
+        """
+        self._read_only = read_only
         self._config = MemoryConfig(
-            recency_weight=self.recency_weight,
-            semantic_weight=self.semantic_weight,
-            importance_weight=self.importance_weight,
-            recency_half_life_days=self.recency_half_life_days,
-            consolidation_threshold=self.consolidation_threshold,
-            consolidation_limit=self.consolidation_limit,
-            default_importance=self.default_importance,
-            confidence_threshold_high=self.confidence_threshold_high,
-            confidence_threshold_low=self.confidence_threshold_low,
-            complex_query_threshold=self.complex_query_threshold,
-            exploration_budget=self.exploration_budget,
-            query_analysis_threshold=self.query_analysis_threshold,
+            recency_weight=recency_weight,
+            semantic_weight=semantic_weight,
+            importance_weight=importance_weight,
+            recency_half_life_days=recency_half_life_days,
+            consolidation_threshold=consolidation_threshold,
+            consolidation_limit=consolidation_limit,
+            default_importance=default_importance,
+            confidence_threshold_high=confidence_threshold_high,
+            confidence_threshold_low=confidence_threshold_low,
+            complex_query_threshold=complex_query_threshold,
+            exploration_budget=exploration_budget,
+            query_analysis_threshold=query_analysis_threshold,
         )
 
-        self._llm_instance = None if isinstance(self.llm, str) else self.llm
-        self._embedder_instance = (
-            self.embedder
-            if (self.embedder is not None and not isinstance(self.embedder, dict))
+        # Store raw config for lazy initialization. LLM and embedder are only
+        # built on first access so that Memory() never fails at construction
+        # time (e.g. when auto-created by Flow without an API key set).
+        self._llm_config: BaseLLM | str = llm
+        self._llm_instance: BaseLLM | None = None if isinstance(llm, str) else llm
+        self._embedder_config: Any = embedder
+        self._embedder_instance: Any = (
+            embedder
+            if (embedder is not None and not isinstance(embedder, dict))
             else None
         )
 
-        if isinstance(self.storage, str):
+        if isinstance(storage, str):
             from crewai.memory.storage.lancedb_storage import LanceDBStorage
 
-            self._storage = (
-                LanceDBStorage()
-                if self.storage == "lancedb"
-                else LanceDBStorage(path=self.storage)
-            )
+            self._storage = LanceDBStorage() if storage == "lancedb" else LanceDBStorage(path=storage)
         else:
-            self._storage = self.storage
+            self._storage = storage
+
+        # Background save queue. max_workers=1 serializes saves to avoid
+        # concurrent storage mutations (two saves finding the same similar
+        # record and both trying to update/delete it). Within each save,
+        # the parallel LLM calls still run on their own thread pool.
+        self._save_pool = ThreadPoolExecutor(
+            max_workers=1, thread_name_prefix="memory-save"
+        )
+        self._pending_saves: list[Future[Any]] = []
+        self._pending_lock = threading.Lock()
 
     _MEMORY_DOCS_URL = "https://docs.crewai.com/concepts/memory"
 
@@ -183,7 +173,11 @@ class Memory(BaseModel):
             from crewai.llm import LLM
 
             try:
-                model_name = self.llm if isinstance(self.llm, str) else str(self.llm)
+                model_name = (
+                    self._llm_config
+                    if isinstance(self._llm_config, str)
+                    else str(self._llm_config)
+                )
                 self._llm_instance = LLM(model=model_name)
             except Exception as e:
                 raise RuntimeError(
@@ -203,8 +197,8 @@ class Memory(BaseModel):
         """Lazy embedder initialization -- only created when first needed."""
         if self._embedder_instance is None:
             try:
-                if isinstance(self.embedder, dict):
-                    self._embedder_instance = build_embedder(self.embedder)
+                if isinstance(self._embedder_config, dict):
+                    self._embedder_instance = build_embedder(self._embedder_config)
                 else:
                     self._embedder_instance = _default_embedder()
             except Exception as e:
@@ -362,7 +356,7 @@ class Memory(BaseModel):
         Raises:
             Exception: On save failure (events emitted).
         """
-        if self.read_only:
+        if self._read_only:
             return None
         _source_type = "unified_memory"
         try:
@@ -450,7 +444,7 @@ class Memory(BaseModel):
         Returns:
             Empty list (records are not available until the background save completes).
         """
-        if not contents or self.read_only:
+        if not contents or self._read_only:
             return []
 
         self._submit_save(

lib/crewai/src/crewai/telemetry/__init__.py

@@ -1,4 +1,5 @@
 from crewai.telemetry.telemetry import Telemetry
 
 
+
 __all__ = ["Telemetry"]

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -173,12 +173,6 @@ class Telemetry:
 
         self._original_handlers: dict[int, Any] = {}
 
-        if threading.current_thread() is not threading.main_thread():
-            logger.debug(
-                "Skipping signal handler registration: not running in main thread"
-            )
-            return
-
         self._register_signal_handler(signal.SIGTERM, SigTermEvent, shutdown=True)
         self._register_signal_handler(signal.SIGINT, SigIntEvent, shutdown=True)
         if hasattr(signal, "SIGHUP"):

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

@@ -1,6 +1,7 @@
 from crewai.tools.base_tool import BaseTool, EnvVar, tool
 
 
+
 __all__ = [
     "BaseTool",
     "EnvVar",

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/tools/memory_tools.py

@@ -121,7 +121,7 @@ def create_memory_tools(memory: Any) -> list[BaseTool]:
             description=i18n.tools("recall_memory"),
         ),
     ]
-    if not memory.read_only:
+    if not getattr(memory, "_read_only", False):
         tools.append(
             RememberTool(
                 memory=memory,

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

@@ -30,6 +30,7 @@
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
     "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
+    "lite_agent_system_prompt_native_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\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!",
     "lite_agent_response_format": "Format your final answer according to the following OpenAPI schema: {response_format}\n\nIMPORTANT: Preserve the original content exactly as-is. Do NOT rewrite, paraphrase, or modify the meaning of the content. Only structure it to match the schema format.\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
     "knowledge_search_query": "The original query is: {task_prompt}.",

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

@@ -1245,26 +1245,34 @@ def _setup_before_llm_call_hooks(
 
 def _setup_after_llm_call_hooks(
     executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
-    answer: str | BaseModel,
+    answer: str | BaseModel | list[Any],
     printer: Printer,
     verbose: bool = True,
-) -> str | BaseModel:
+) -> str | BaseModel | list[Any]:
     """Setup and invoke after_llm_call hooks for the executor context.
 
     Args:
         executor_context: The executor context to setup the hooks for.
-        answer: The LLM response (string or Pydantic model).
+        answer: The LLM response (string, Pydantic model, or list of native
+            tool calls).
         printer: Printer instance for error logging.
         verbose: Whether to print output.
 
     Returns:
-        The potentially modified response (string or Pydantic model).
+        The potentially modified response. List-type answers (native tool
+        calls) are always returned unchanged so that callers can rely on
+        ``isinstance(answer, list)`` checks.
     """
     if executor_context and executor_context.after_llm_call_hooks:
         from crewai.hooks.llm_hooks import LLMCallHookContext
 
         original_messages = executor_context.messages
 
+        # Native tool-call lists must survive hooks unchanged.  We provide a
+        # stringified representation to hook context for observability but
+        # always return the original list so callers can detect tool calls.
+        is_tool_call_list = isinstance(answer, list)
+
         # For Pydantic models, serialize to JSON for hooks
         if isinstance(answer, BaseModel):
             pydantic_answer = answer
@@ -1303,6 +1311,9 @@ def _setup_after_llm_call_hooks(
             else:
                 executor_context.messages = []
 
+        if is_tool_call_list:
+            return answer
+
         # If hooks modified the response, update answer accordingly
         if pydantic_answer is not None:
             # For Pydantic models, reparse the JSON if it was modified

lib/crewai/tests/agents/test_agent.py

@@ -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_executor.py

@@ -123,7 +123,7 @@ class TestAgentExecutor:
         executor.state.iterations = 10
 
         result = executor.check_max_iterations()
-        assert result == "max_iterations_exceeded"
+        assert result == "force_final_answer"
 
     def test_route_by_answer_type_action(self, mock_dependencies):
         """Test routing for AgentAction."""

lib/crewai/tests/agents/test_lite_agent.py

@@ -1136,7 +1136,7 @@ def test_lite_agent_memory_instance_recall_and_save_called():
         successful_requests=1,
     )
     mock_memory = Mock()
-    mock_memory.read_only = False
+    mock_memory._read_only = False
     mock_memory.recall.return_value = []
     mock_memory.extract_memories.return_value = ["Fact one.", "Fact two."]
 
@@ -1160,3 +1160,315 @@ def test_lite_agent_memory_instance_recall_and_save_called():
     mock_memory.remember_many.assert_called_once_with(
         ["Fact one.", "Fact two."], agent_role="Test"
     )
+
+
+# ---------------------------------------------------------------------------
+# Native tool calling tests
+# ---------------------------------------------------------------------------
+
+
+class _NativeToolCallLLM(BaseLLM):
+    """Fake LLM that supports native function calling and returns tool calls."""
+
+    def __init__(self, tool_calls=None, final_answer="42"):
+        super().__init__(model="fake-native-fc-model")
+        self._tool_calls = tool_calls or []
+        self._final_answer = final_answer
+        self._call_index = 0
+
+    def call(
+        self,
+        messages,
+        tools=None,
+        callbacks=None,
+        available_functions=None,
+        from_task=None,
+        from_agent=None,
+        response_model=None,
+    ):
+        idx = self._call_index
+        self._call_index += 1
+        if idx < len(self._tool_calls):
+            return self._tool_calls[idx]
+        return self._final_answer
+
+    def supports_function_calling(self) -> bool:
+        return True
+
+    def supports_stop_words(self) -> bool:
+        return False
+
+    def get_context_window_size(self) -> int:
+        return 8192
+
+
+class _ReactOnlyLLM(BaseLLM):
+    """Fake LLM that does NOT support function calling."""
+
+    def __init__(self, response="Thought: done\nFinal Answer: hello"):
+        super().__init__(model="fake-react-only-model")
+        self._response = response
+
+    def call(self, messages, **kwargs):
+        return self._response
+
+    def supports_function_calling(self) -> bool:
+        return False
+
+    def supports_stop_words(self) -> bool:
+        return True
+
+    def get_context_window_size(self) -> int:
+        return 8192
+
+
+def test_lite_agent_native_mode_detection_with_fc_llm():
+    """LiteAgent should set _use_native_tools=True when LLM supports function calling and tools exist."""
+    llm = _NativeToolCallLLM(final_answer="done")
+    agent = LiteAgent(
+        role="Tester", goal="Test", backstory="Test agent",
+        llm=llm, tools=[SecretLookupTool()],
+    )
+    agent.kickoff("test")
+    assert agent._use_native_tools is True
+
+
+def test_lite_agent_native_mode_detection_without_fc_llm():
+    """LiteAgent should set _use_native_tools=False when LLM does not support function calling."""
+    llm = _ReactOnlyLLM()
+    agent = LiteAgent(
+        role="Tester", goal="Test", backstory="Test agent",
+        llm=llm, tools=[SecretLookupTool()],
+    )
+    agent.kickoff("test")
+    assert agent._use_native_tools is False
+
+
+def test_lite_agent_native_mode_detection_no_tools():
+    """LiteAgent should set _use_native_tools=False when there are no tools."""
+    llm = _NativeToolCallLLM(final_answer="no tools needed")
+    agent = LiteAgent(
+        role="Tester", goal="Test", backstory="Test agent",
+        llm=llm, tools=[],
+    )
+    agent.kickoff("test")
+    assert agent._use_native_tools is False
+
+
+def test_lite_agent_native_mode_system_prompt_has_no_react_instructions():
+    """In native mode the system prompt should NOT contain ReAct Action/Action Input instructions."""
+    llm = _NativeToolCallLLM(final_answer="result")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute things", backstory="A math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    agent.kickoff("What is 1+1?")
+
+    system_msg = agent._messages[0]
+    assert system_msg["role"] == "system"
+    content = system_msg["content"]
+    assert "Action:" not in content
+    assert "Action Input:" not in content
+    assert "Observation:" not in content
+    assert "Calculator" in content
+    assert "Compute things" in content
+
+
+def test_lite_agent_react_mode_system_prompt_has_react_instructions():
+    """In ReAct mode the system prompt SHOULD contain Action/Action Input instructions."""
+    llm = _ReactOnlyLLM()
+    agent = LiteAgent(
+        role="Calculator", goal="Compute things", backstory="A math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    agent.kickoff("What is 1+1?")
+
+    system_msg = agent._messages[0]
+    content = system_msg["content"]
+    assert "Action:" in content
+    assert "Action Input:" in content
+
+
+def _make_openai_tool_call(call_id, name, arguments):
+    """Helper to create an OpenAI-style tool call object."""
+    tc = Mock()
+    tc.id = call_id
+    func = Mock()
+    func.name = name
+    func.arguments = arguments
+    tc.function = func
+    return tc
+
+
+def test_lite_agent_native_tool_execution():
+    """Verify LiteAgent executes native tool calls and feeds results back to the LLM."""
+    tool_call = [_make_openai_tool_call("call_1", "calculate", '{"expression": "6*7"}')]
+
+    llm = _NativeToolCallLLM(tool_calls=[tool_call], final_answer="The answer is 42")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    result = agent.kickoff("What is 6 * 7?")
+
+    assert "42" in result.raw
+    assert len(agent.tools_results) == 1
+    assert agent.tools_results[0]["tool_name"] == "calculate"
+
+
+def test_lite_agent_native_parallel_tool_calls():
+    """When LLM returns multiple tool calls, they should all be executed."""
+    tool_calls = [
+        _make_openai_tool_call("call_1", "calculate", '{"expression": "2+3"}'),
+        _make_openai_tool_call("call_2", "calculate", '{"expression": "4+5"}'),
+    ]
+
+    llm = _NativeToolCallLLM(tool_calls=[tool_calls], final_answer="5 and 9")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    result = agent.kickoff("What is 2+3 and 4+5?")
+
+    assert len(agent.tools_results) == 2
+    tool_names = [r["tool_name"] for r in agent.tools_results]
+    assert tool_names == ["calculate", "calculate"]
+
+    tool_messages = [m for m in agent._messages if m.get("role") == "tool"]
+    assert len(tool_messages) == 2
+
+    assistant_tc_messages = [
+        m for m in agent._messages
+        if m.get("role") == "assistant" and m.get("tool_calls")
+    ]
+    assert len(assistant_tc_messages) == 1
+    assert len(assistant_tc_messages[0]["tool_calls"]) == 2
+
+
+def test_lite_agent_native_tool_usage_count_no_double_increment():
+    """current_usage_count must increment exactly once per native tool call.
+
+    BaseTool.run() already increments the counter internally, so the native
+    tool call handler must not add a second increment.
+    """
+    tool_call = [_make_openai_tool_call("call_1", "calculate", '{"expression": "1+1"}')]
+
+    llm = _NativeToolCallLLM(tool_calls=[tool_call], final_answer="2")
+    calc_tool = CalculatorTool()
+    assert calc_tool.current_usage_count == 0
+
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[calc_tool],
+    )
+    agent.kickoff("What is 1+1?")
+
+    assert calc_tool.current_usage_count == 1
+
+
+def test_lite_agent_native_tool_max_usage_count_respected():
+    """A tool with max_usage_count=1 should be usable exactly once, not blocked after 1 call."""
+    call_round_1 = [_make_openai_tool_call("c1", "calculate", '{"expression": "1+1"}')]
+    call_round_2 = [_make_openai_tool_call("c2", "calculate", '{"expression": "2+2"}')]
+
+    llm = _NativeToolCallLLM(
+        tool_calls=[call_round_1, call_round_2], final_answer="done"
+    )
+    calc_tool = CalculatorTool()
+    calc_tool.max_usage_count = 2
+
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[calc_tool],
+    )
+    agent.kickoff("Compute 1+1 then 2+2")
+
+    executed = [r for r in agent.tools_results if "usage limit" not in r["result"]]
+    assert len(executed) == 2
+    assert calc_tool.current_usage_count == 2
+
+
+def test_lite_agent_native_tool_calls_with_after_llm_hook():
+    """Native tool calls must be processed even when after_llm_call hooks are active.
+
+    Regression test: _setup_after_llm_call_hooks was converting the list of
+    tool calls to a string via str(), causing isinstance(answer, list) to fail
+    in _invoke_loop_native_tools and silently returning the stringified list as
+    the agent's final answer.
+    """
+    hook_called = {"count": 0}
+
+    def after_hook(context):
+        hook_called["count"] += 1
+        return None
+
+    tool_call = [_make_openai_tool_call("call_1", "calculate", '{"expression": "6*7"}')]
+
+    llm = _NativeToolCallLLM(tool_calls=[tool_call], final_answer="The answer is 42")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    agent._after_llm_call_hooks.append(after_hook)
+
+    result = agent.kickoff("What is 6 * 7?")
+
+    assert hook_called["count"] >= 1
+    assert len(agent.tools_results) == 1
+    assert agent.tools_results[0]["tool_name"] == "calculate"
+    assert "42" in result.raw
+
+
+def test_lite_agent_native_parallel_tool_calls_with_after_llm_hook():
+    """Multiple native tool calls in a single response must work with hooks active."""
+    hook_called = {"count": 0}
+
+    def after_hook(context):
+        hook_called["count"] += 1
+        return None
+
+    tool_calls = [
+        _make_openai_tool_call("call_1", "calculate", '{"expression": "2+3"}'),
+        _make_openai_tool_call("call_2", "calculate", '{"expression": "4+5"}'),
+    ]
+
+    llm = _NativeToolCallLLM(tool_calls=[tool_calls], final_answer="5 and 9")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[CalculatorTool()],
+    )
+    agent._after_llm_call_hooks.append(after_hook)
+
+    result = agent.kickoff("What is 2+3 and 4+5?")
+
+    assert hook_called["count"] >= 1
+    assert len(agent.tools_results) == 2
+    tool_names = [r["tool_name"] for r in agent.tools_results]
+    assert tool_names == ["calculate", "calculate"]
+
+
+def test_lite_agent_native_duplicate_tool_names_resolved():
+    """Two tools with the same sanitized name should both be usable via dedup suffixes.
+
+    convert_tools_to_openai_schema renames duplicates (e.g. calculate -> calculate_2).
+    The original_tools_by_name mapping must honour these deduplicated names so
+    result_as_answer, max_usage_count, and usage tracking work for every tool.
+    """
+    tool_a = CalculatorTool()
+    tool_a.result_as_answer = True
+
+    tool_b = CalculatorTool()
+
+    tool_call = [
+        _make_openai_tool_call("c1", "calculate_2", '{"expression": "9+1"}'),
+    ]
+    llm = _NativeToolCallLLM(tool_calls=[tool_call], final_answer="fallback")
+    agent = LiteAgent(
+        role="Calculator", goal="Compute", backstory="Math agent",
+        llm=llm, tools=[tool_a, tool_b],
+    )
+    agent.kickoff("What is 9+1?")
+
+    assert len(agent.tools_results) == 1
+    assert agent.tools_results[0]["tool_name"] == "calculate_2"
+    assert "10" in agent.tools_results[0]["result"]

lib/crewai/tests/cassettes/llms/anthropic/test_tool_search_discovers_and_calls_tool.yaml

@@ -1,137 +0,0 @@
-interactions:
-- request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather
-      in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"type":"tool_search_tool_bm25_20251119","name":"tool_search_tool_bm25"},{"name":"get_weather","description":"Get
-      current weather conditions for a specified location","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_weather"}},"required":["input"]},"defer_loading":true},{"name":"search_files","description":"Search
-      through files in the workspace by name or content","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for search_files"}},"required":["input"]},"defer_loading":true},{"name":"read_database","description":"Read
-      records from a database table with optional filtering","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_database"}},"required":["input"]},"defer_loading":true},{"name":"write_database","description":"Write
-      or update records in a database table","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for write_database"}},"required":["input"]},"defer_loading":true},{"name":"send_email","description":"Send
-      an email message to one or more recipients","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for send_email"}},"required":["input"]},"defer_loading":true},{"name":"read_email","description":"Read
-      emails from inbox with filtering options","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_email"}},"required":["input"]},"defer_loading":true},{"name":"create_ticket","description":"Create
-      a new support ticket in the ticketing system","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_ticket"}},"required":["input"]},"defer_loading":true},{"name":"update_ticket","description":"Update
-      an existing support ticket status or description","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for update_ticket"}},"required":["input"]},"defer_loading":true},{"name":"list_users","description":"List
-      all users in the system with optional filters","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for list_users"}},"required":["input"]},"defer_loading":true},{"name":"get_user_profile","description":"Get
-      detailed profile information for a specific user","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_user_profile"}},"required":["input"]},"defer_loading":true},{"name":"deploy_service","description":"Deploy
-      a service to the specified environment","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for deploy_service"}},"required":["input"]},"defer_loading":true},{"name":"rollback_service","description":"Rollback
-      a service deployment to a previous version","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for rollback_service"}},"required":["input"]},"defer_loading":true},{"name":"get_service_logs","description":"Get
-      service logs filtered by time range and severity","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_service_logs"}},"required":["input"]},"defer_loading":true},{"name":"run_sql_query","description":"Run
-      a read-only SQL query against the analytics database","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for run_sql_query"}},"required":["input"]},"defer_loading":true},{"name":"create_dashboard","description":"Create
-      a new monitoring dashboard with widgets","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_dashboard"}},"required":["input"]},"defer_loading":true}]}'
-    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:
-      - '3952'
-      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-sonnet-4-5-20250929","id":"msg_01DAGCoL6C12u6yAgR1UqNAs","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll
-        search for a weather-related tool to help you get the weather information
-        for Tokyo."},{"type":"server_tool_use","id":"srvtoolu_0176qgHeeBpSygYAnUzKHCfh","name":"tool_search_tool_bm25","input":{"query":"weather
-        Tokyo current conditions forecast"},"caller":{"type":"direct"}},{"type":"tool_search_tool_result","tool_use_id":"srvtoolu_0176qgHeeBpSygYAnUzKHCfh","content":{"type":"tool_search_tool_search_result","tool_references":[{"type":"tool_reference","tool_name":"get_weather"}]}},{"type":"text","text":"Great!
-        I found a weather tool. Let me get the current weather conditions for Tokyo."},{"type":"tool_use","id":"toolu_01R3FavQLuTrwNvEk9gMaViK","name":"get_weather","input":{"input":"Tokyo"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1566,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":155,"service_tier":"standard","inference_geo":"not_available","server_tool_use":{"web_search_requests":0,"web_fetch_requests":0}}}'
-    headers:
-      CF-RAY:
-      - CF-RAY-XXX
-      Connection:
-      - keep-alive
-      Content-Security-Policy:
-      - CSP-FILTERED
-      Content-Type:
-      - application/json
-      Date:
-      - Sun, 08 Mar 2026 21:04:12 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:
-      - '20000'
-      anthropic-ratelimit-requests-remaining:
-      - '19999'
-      anthropic-ratelimit-requests-reset:
-      - '2026-03-08T21:04:07Z'
-      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
-      vary:
-      - Accept-Encoding
-      x-envoy-upstream-service-time:
-      - '4330'
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/cassettes/llms/anthropic/test_tool_search_saves_input_tokens.yaml

@@ -1,112 +0,0 @@
-interactions:
-- request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather
-      in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"name":"get_weather","description":"Get
-      current weather conditions for a specified location","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_weather"}},"required":["input"]}},{"name":"search_files","description":"Search
-      through files in the workspace by name or content","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for search_files"}},"required":["input"]}},{"name":"read_database","description":"Read
-      records from a database table with optional filtering","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_database"}},"required":["input"]}},{"name":"write_database","description":"Write
-      or update records in a database table","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for write_database"}},"required":["input"]}},{"name":"send_email","description":"Send
-      an email message to one or more recipients","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for send_email"}},"required":["input"]}},{"name":"read_email","description":"Read
-      emails from inbox with filtering options","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_email"}},"required":["input"]}},{"name":"create_ticket","description":"Create
-      a new support ticket in the ticketing system","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_ticket"}},"required":["input"]}},{"name":"update_ticket","description":"Update
-      an existing support ticket status or description","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for update_ticket"}},"required":["input"]}},{"name":"list_users","description":"List
-      all users in the system with optional filters","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for list_users"}},"required":["input"]}},{"name":"get_user_profile","description":"Get
-      detailed profile information for a specific user","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_user_profile"}},"required":["input"]}},{"name":"deploy_service","description":"Deploy
-      a service to the specified environment","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for deploy_service"}},"required":["input"]}},{"name":"rollback_service","description":"Rollback
-      a service deployment to a previous version","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for rollback_service"}},"required":["input"]}},{"name":"get_service_logs","description":"Get
-      service logs filtered by time range and severity","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_service_logs"}},"required":["input"]}},{"name":"run_sql_query","description":"Run
-      a read-only SQL query against the analytics database","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for run_sql_query"}},"required":["input"]}},{"name":"create_dashboard","description":"Create
-      a new monitoring dashboard with widgets","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_dashboard"}},"required":["input"]}}]}'
-    headers:
-      accept:
-      - application/json
-      anthropic-version:
-      - '2023-06-01'
-      connection:
-      - keep-alive
-      content-type:
-      - application/json
-      host:
-      - api.anthropic.com
-    method: POST
-    uri: https://api.anthropic.com/v1/messages
-  response:
-    body:
-      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_01NoSearch001","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NoSearch001","name":"get_weather","input":{"input":"Tokyo"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1943,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":54,"service_tier":"standard"}}'
-    headers:
-      Content-Type:
-      - application/json
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather
-      in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"type":"tool_search_tool_bm25_20251119","name":"tool_search_tool_bm25"},{"name":"get_weather","description":"Get
-      current weather conditions for a specified location","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_weather"}},"required":["input"]},"defer_loading":true},{"name":"search_files","description":"Search
-      through files in the workspace by name or content","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for search_files"}},"required":["input"]},"defer_loading":true},{"name":"read_database","description":"Read
-      records from a database table with optional filtering","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_database"}},"required":["input"]},"defer_loading":true},{"name":"write_database","description":"Write
-      or update records in a database table","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for write_database"}},"required":["input"]},"defer_loading":true},{"name":"send_email","description":"Send
-      an email message to one or more recipients","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for send_email"}},"required":["input"]},"defer_loading":true},{"name":"read_email","description":"Read
-      emails from inbox with filtering options","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for read_email"}},"required":["input"]},"defer_loading":true},{"name":"create_ticket","description":"Create
-      a new support ticket in the ticketing system","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_ticket"}},"required":["input"]},"defer_loading":true},{"name":"update_ticket","description":"Update
-      an existing support ticket status or description","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for update_ticket"}},"required":["input"]},"defer_loading":true},{"name":"list_users","description":"List
-      all users in the system with optional filters","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for list_users"}},"required":["input"]},"defer_loading":true},{"name":"get_user_profile","description":"Get
-      detailed profile information for a specific user","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_user_profile"}},"required":["input"]},"defer_loading":true},{"name":"deploy_service","description":"Deploy
-      a service to the specified environment","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for deploy_service"}},"required":["input"]},"defer_loading":true},{"name":"rollback_service","description":"Rollback
-      a service deployment to a previous version","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for rollback_service"}},"required":["input"]},"defer_loading":true},{"name":"get_service_logs","description":"Get
-      service logs filtered by time range and severity","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for get_service_logs"}},"required":["input"]},"defer_loading":true},{"name":"run_sql_query","description":"Run
-      a read-only SQL query against the analytics database","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for run_sql_query"}},"required":["input"]},"defer_loading":true},{"name":"create_dashboard","description":"Create
-      a new monitoring dashboard with widgets","input_schema":{"type":"object","properties":{"input":{"type":"string","description":"Input
-      for create_dashboard"}},"required":["input"]},"defer_loading":true}]}'
-    headers:
-      accept:
-      - application/json
-      anthropic-version:
-      - '2023-06-01'
-      connection:
-      - keep-alive
-      content-type:
-      - application/json
-      host:
-      - api.anthropic.com
-    method: POST
-    uri: https://api.anthropic.com/v1/messages
-  response:
-    body:
-      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_01WithSearch001","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll search for a weather tool."},{"type":"server_tool_use","id":"srvtoolu_01Search001","name":"tool_search_tool_bm25","input":{"query":"weather conditions"},"caller":{"type":"direct"}},{"type":"tool_search_tool_result","tool_use_id":"srvtoolu_01Search001","content":{"type":"tool_search_tool_search_result","tool_references":[{"type":"tool_reference","tool_name":"get_weather"}]}},{"type":"text","text":"Found it. Let me get the weather for Tokyo."},{"type":"tool_use","id":"toolu_01WithSearch001","name":"get_weather","input":{"input":"Tokyo"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1566,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":155,"service_tier":"standard"}}'
-    headers:
-      Content-Type:
-      - application/json
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/cli/test_plus_api.py

@@ -28,19 +28,7 @@ class TestPlusAPI(unittest.TestCase):
         response = self.api.login_to_tool_repository()
 
         mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/tools/login", json={}
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai.cli.plus_api.PlusAPI._make_request")
-    def test_login_to_tool_repository_with_user_identifier(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-
-        response = self.api.login_to_tool_repository(user_identifier="test-hash-123")
-
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/tools/login", json={"user_identifier": "test-hash-123"}
+            "POST", "/crewai_plus/api/v1/tools/login"
         )
         self.assertEqual(response, mock_response)
 
@@ -79,7 +67,7 @@ class TestPlusAPI(unittest.TestCase):
         response = self.api.login_to_tool_repository()
 
         self.assert_request_with_org_id(
-            mock_client_instance, "POST", "/crewai_plus/api/v1/tools/login", json={}
+            mock_client_instance, "POST", "/crewai_plus/api/v1/tools/login"
         )
         self.assertEqual(response, mock_response)
 

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

@@ -1121,345 +1121,3 @@ def test_anthropic_cached_prompt_tokens_with_tools():
     assert usage.successful_requests == 2
     # The second call should have cached prompt tokens
     assert usage.cached_prompt_tokens > 0
-
-
-# ---- Tool Search Tool Tests ----
-
-
-def test_tool_search_true_injects_bm25_and_defer_loading():
-    """tool_search=True should inject bm25 tool search and defer all tools."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-
-    crewai_tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get weather for a location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"location": {"type": "string"}},
-                    "required": ["location"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "calculator",
-                "description": "Perform math calculations",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"expression": {"type": "string"}},
-                    "required": ["expression"],
-                },
-            },
-        },
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_anthropic(
-        [{"role": "user", "content": "Hello"}]
-    )
-    params = llm._prepare_completion_params(
-        formatted_messages, system_message, crewai_tools
-    )
-
-    tools = params["tools"]
-    # Should have 3 tools: tool_search + 2 regular
-    assert len(tools) == 3
-
-    # First tool should be the bm25 tool search tool
-    assert tools[0]["type"] == "tool_search_tool_bm25_20251119"
-    assert tools[0]["name"] == "tool_search_tool_bm25"
-    assert "input_schema" not in tools[0]
-
-    # All regular tools should have defer_loading=True
-    for t in tools[1:]:
-        assert t.get("defer_loading") is True, f"Tool {t['name']} missing defer_loading"
-
-
-def test_tool_search_regex_config():
-    """tool_search with regex config should use regex variant."""
-    from crewai.llms.providers.anthropic.completion import AnthropicToolSearchConfig
-
-    config = AnthropicToolSearchConfig(type="regex")
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=config)
-
-    crewai_tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "tool_a",
-                "description": "First tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"q": {"type": "string"}},
-                    "required": ["q"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "tool_b",
-                "description": "Second tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"q": {"type": "string"}},
-                    "required": ["q"],
-                },
-            },
-        },
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_anthropic(
-        [{"role": "user", "content": "Hello"}]
-    )
-    params = llm._prepare_completion_params(
-        formatted_messages, system_message, crewai_tools
-    )
-
-    tools = params["tools"]
-    assert tools[0]["type"] == "tool_search_tool_regex_20251119"
-    assert tools[0]["name"] == "tool_search_tool_regex"
-
-
-def test_tool_search_disabled_by_default():
-    """tool_search=None (default) should NOT inject anything."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5")
-
-    crewai_tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "description": "A test tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"q": {"type": "string"}},
-                    "required": ["q"],
-                },
-            },
-        },
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_anthropic(
-        [{"role": "user", "content": "Hello"}]
-    )
-    params = llm._prepare_completion_params(
-        formatted_messages, system_message, crewai_tools
-    )
-
-    tools = params["tools"]
-    assert len(tools) == 1
-    for t in tools:
-        assert t.get("type", "") not in (
-            "tool_search_tool_bm25_20251119",
-            "tool_search_tool_regex_20251119",
-        )
-        assert "defer_loading" not in t
-
-
-def test_tool_search_no_duplicate_when_manually_provided():
-    """If user passes a tool search tool manually, don't inject a duplicate."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-
-    # User manually includes a tool search tool
-    tools_with_search = [
-        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
-        {
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "description": "A test tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"q": {"type": "string"}},
-                    "required": ["q"],
-                },
-            },
-        },
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_anthropic(
-        [{"role": "user", "content": "Hello"}]
-    )
-    params = llm._prepare_completion_params(
-        formatted_messages, system_message, tools_with_search
-    )
-
-    tools = params["tools"]
-    search_tools = [
-        t for t in tools
-        if t.get("type", "").startswith("tool_search_tool")
-    ]
-    # Should only have 1 tool search tool (the user's manual one)
-    assert len(search_tools) == 1
-    assert search_tools[0]["type"] == "tool_search_tool_regex_20251119"
-
-
-def test_tool_search_passthrough_preserves_tool_search_type():
-    """_convert_tools_for_interference should pass through tool search tools unchanged."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5")
-
-    tools = [
-        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"},
-        {
-            "name": "get_weather",
-            "description": "Get weather",
-            "input_schema": {
-                "type": "object",
-                "properties": {"location": {"type": "string"}},
-                "required": ["location"],
-            },
-        },
-    ]
-
-    converted = llm._convert_tools_for_interference(tools)
-    assert len(converted) == 2
-    # Tool search tool should be passed through exactly
-    assert converted[0] == {
-        "type": "tool_search_tool_regex_20251119",
-        "name": "tool_search_tool_regex",
-    }
-    # Regular tool should be preserved
-    assert converted[1]["name"] == "get_weather"
-    assert "input_schema" in converted[1]
-
-
-def test_tool_search_single_tool_skips_search_and_forces_choice():
-    """With only 1 tool, tool_search is skipped (nothing to search) and the
-    normal forced tool_choice optimisation still applies."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-
-    crewai_tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "description": "A test tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"q": {"type": "string"}},
-                    "required": ["q"],
-                },
-            },
-        },
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_anthropic(
-        [{"role": "user", "content": "Hello"}]
-    )
-    params = llm._prepare_completion_params(
-        formatted_messages,
-        system_message,
-        crewai_tools,
-        available_functions={"test_tool": lambda q: "result"},
-    )
-
-    # Single tool — tool_search skipped, tool_choice forced as normal
-    assert "tool_choice" in params
-    assert params["tool_choice"]["name"] == "test_tool"
-
-    # No tool search tool should be injected
-    tool_types = [t.get("type", "") for t in params["tools"]]
-    for ts_type in ("tool_search_tool_bm25_20251119", "tool_search_tool_regex_20251119"):
-        assert ts_type not in tool_types
-
-    # No defer_loading on the single tool
-    assert "defer_loading" not in params["tools"][0]
-
-
-def test_tool_search_via_llm_class():
-    """Verify tool_search param passes through LLM class correctly."""
-    from crewai.llms.providers.anthropic.completion import (
-        AnthropicCompletion,
-        AnthropicToolSearchConfig,
-    )
-
-    # Test with True
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-    assert isinstance(llm, AnthropicCompletion)
-    assert llm.tool_search is not None
-    assert llm.tool_search.type == "bm25"
-
-    # Test with config
-    llm2 = LLM(
-        model="anthropic/claude-sonnet-4-5",
-        tool_search=AnthropicToolSearchConfig(type="regex"),
-    )
-    assert llm2.tool_search is not None
-    assert llm2.tool_search.type == "regex"
-
-    # Test without (default)
-    llm3 = LLM(model="anthropic/claude-sonnet-4-5")
-    assert llm3.tool_search is None
-
-
-# Many tools shared by the VCR tests below
-_MANY_TOOLS = [
-    {
-        "name": name,
-        "description": desc,
-        "input_schema": {
-            "type": "object",
-            "properties": {"input": {"type": "string", "description": f"Input for {name}"}},
-            "required": ["input"],
-        },
-    }
-    for name, desc in [
-        ("get_weather", "Get current weather conditions for a specified location"),
-        ("search_files", "Search through files in the workspace by name or content"),
-        ("read_database", "Read records from a database table with optional filtering"),
-        ("write_database", "Write or update records in a database table"),
-        ("send_email", "Send an email message to one or more recipients"),
-        ("read_email", "Read emails from inbox with filtering options"),
-        ("create_ticket", "Create a new support ticket in the ticketing system"),
-        ("update_ticket", "Update an existing support ticket status or description"),
-        ("list_users", "List all users in the system with optional filters"),
-        ("get_user_profile", "Get detailed profile information for a specific user"),
-        ("deploy_service", "Deploy a service to the specified environment"),
-        ("rollback_service", "Rollback a service deployment to a previous version"),
-        ("get_service_logs", "Get service logs filtered by time range and severity"),
-        ("run_sql_query", "Run a read-only SQL query against the analytics database"),
-        ("create_dashboard", "Create a new monitoring dashboard with widgets"),
-    ]
-]
-
-
-@pytest.mark.vcr()
-def test_tool_search_discovers_and_calls_tool():
-    """Tool search should discover the right tool and return a tool_use block."""
-    llm = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-
-    result = llm.call(
-        "What is the weather in Tokyo?",
-        tools=_MANY_TOOLS,
-    )
-
-    # Should return tool_use blocks (list) since no available_functions provided
-    assert isinstance(result, list)
-    assert len(result) >= 1
-    # The discovered tool should be get_weather
-    tool_names = [getattr(block, "name", None) for block in result]
-    assert "get_weather" in tool_names
-
-
-@pytest.mark.vcr()
-def test_tool_search_saves_input_tokens():
-    """Tool search with deferred loading should use fewer input tokens than loading all tools."""
-    # Call WITHOUT tool search — all 15 tools loaded upfront
-    llm_no_search = LLM(model="anthropic/claude-sonnet-4-5")
-    llm_no_search.call("What is the weather in Tokyo?", tools=_MANY_TOOLS)
-    usage_no_search = llm_no_search.get_token_usage_summary()
-
-    # Call WITH tool search — tools deferred
-    llm_search = LLM(model="anthropic/claude-sonnet-4-5", tool_search=True)
-    llm_search.call("What is the weather in Tokyo?", tools=_MANY_TOOLS)
-    usage_search = llm_search.get_token_usage_summary()
-
-    # Tool search should use fewer input tokens
-    assert usage_search.prompt_tokens < usage_no_search.prompt_tokens, (
-        f"Expected tool_search ({usage_search.prompt_tokens}) to use fewer input tokens "
-        f"than no search ({usage_no_search.prompt_tokens})"
-    )

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

@@ -967,211 +967,3 @@ def test_bedrock_agent_kickoff_structured_output_with_tools():
     assert result.pydantic.result == 42, f"Expected result 42 but got {result.pydantic.result}"
     assert result.pydantic.operation, "Operation should not be empty"
     assert result.pydantic.explanation, "Explanation should not be empty"
-
-
-def test_bedrock_groups_three_tool_results():
-    """Consecutive tool results should be grouped into one Bedrock user message."""
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    messages = [
-        {"role": "user", "content": "Use all three tools, then continue."},
-        {
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [
-                {
-                    "id": "tool-1",
-                    "type": "function",
-                    "function": {
-                        "name": "lookup_weather",
-                        "arguments": '{"location": "New York"}',
-                    },
-                },
-                {
-                    "id": "tool-2",
-                    "type": "function",
-                    "function": {
-                        "name": "lookup_news",
-                        "arguments": '{"topic": "AI"}',
-                    },
-                },
-                {
-                    "id": "tool-3",
-                    "type": "function",
-                    "function": {
-                        "name": "lookup_stock",
-                        "arguments": '{"ticker": "AMZN"}',
-                    },
-                },
-            ],
-        },
-        {"role": "tool", "tool_call_id": "tool-1", "content": "72F and sunny"},
-        {"role": "tool", "tool_call_id": "tool-2", "content": "AI news summary"},
-        {"role": "tool", "tool_call_id": "tool-3", "content": "AMZN up 1.2%"},
-    ]
-
-    formatted_messages, system_message = llm._format_messages_for_converse(messages)
-
-    assert system_message is None
-    assert [message["role"] for message in formatted_messages] == [
-        "user",
-        "assistant",
-        "user",
-    ]
-    assert len(formatted_messages[1]["content"]) == 3
-
-    tool_results = formatted_messages[2]["content"]
-    assert len(tool_results) == 3
-    assert [block["toolResult"]["toolUseId"] for block in tool_results] == [
-        "tool-1",
-        "tool-2",
-        "tool-3",
-    ]
-    assert [block["toolResult"]["content"][0]["text"] for block in tool_results] == [
-        "72F and sunny",
-        "AI news summary",
-        "AMZN up 1.2%",
-    ]
-
-
-def test_bedrock_parallel_tool_results_grouped():
-    """Regression test for issue #4749.
-
-    When an assistant message contains multiple parallel tool calls,
-    Bedrock requires all corresponding tool results to be grouped
-    in a single user message. Previously each tool result was emitted
-    as a separate user message, causing:
-        ValidationException: Expected toolResult blocks at messages.2.content
-    """
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    messages = [
-        {"role": "user", "content": "Calculate 25 + 17 AND 10 * 5"},
-        {
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [
-                {
-                    "id": "call_add",
-                    "type": "function",
-                    "function": {"name": "add_tool", "arguments": '{"a": 25, "b": 17}'},
-                },
-                {
-                    "id": "call_mul",
-                    "type": "function",
-                    "function": {"name": "multiply_tool", "arguments": '{"a": 10, "b": 5}'},
-                },
-            ],
-        },
-        {"role": "tool", "tool_call_id": "call_add", "content": "42"},
-        {"role": "tool", "tool_call_id": "call_mul", "content": "50"},
-    ]
-
-    converse_msgs, system_msg = llm._format_messages_for_converse(messages)
-
-    # Find the user message that contains toolResult blocks
-    tool_result_messages = [
-        m for m in converse_msgs
-        if m.get("role") == "user"
-        and any("toolResult" in b for b in m.get("content", []))
-    ]
-
-    # There must be exactly ONE user message with tool results (not two)
-    assert len(tool_result_messages) == 1, (
-        f"Expected 1 grouped tool-result message, got {len(tool_result_messages)}. "
-        "Bedrock requires all parallel tool results in a single user message."
-    )
-
-    # That single message must contain both tool results
-    tool_results = tool_result_messages[0]["content"]
-    assert len(tool_results) == 2, (
-        f"Expected 2 toolResult blocks in grouped message, got {len(tool_results)}"
-    )
-
-    # Verify the tool use IDs match
-    tool_use_ids = {
-        block["toolResult"]["toolUseId"] for block in tool_results
-    }
-    assert tool_use_ids == {"call_add", "call_mul"}
-
-
-def test_bedrock_single_tool_result_still_works():
-    """Ensure single tool call still produces a single-block user message."""
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    messages = [
-        {"role": "user", "content": "Add 1 + 2"},
-        {
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [
-                {
-                    "id": "call_single",
-                    "type": "function",
-                    "function": {"name": "add_tool", "arguments": '{"a": 1, "b": 2}'},
-                },
-            ],
-        },
-        {"role": "tool", "tool_call_id": "call_single", "content": "3"},
-    ]
-
-    converse_msgs, _ = llm._format_messages_for_converse(messages)
-
-    tool_result_messages = [
-        m for m in converse_msgs
-        if m.get("role") == "user"
-        and any("toolResult" in b for b in m.get("content", []))
-    ]
-    assert len(tool_result_messages) == 1
-    assert len(tool_result_messages[0]["content"]) == 1
-    assert tool_result_messages[0]["content"][0]["toolResult"]["toolUseId"] == "call_single"
-
-
-def test_bedrock_tool_results_not_merged_across_assistant_messages():
-    """Tool results from different assistant turns must NOT be merged."""
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    messages = [
-        {"role": "user", "content": "First task"},
-        {
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [
-                {
-                    "id": "call_a",
-                    "type": "function",
-                    "function": {"name": "tool_a", "arguments": "{}"},
-                },
-            ],
-        },
-        {"role": "tool", "tool_call_id": "call_a", "content": "result_a"},
-        {"role": "assistant", "content": "Now doing second task"},
-        {"role": "user", "content": "Second task"},
-        {
-            "role": "assistant",
-            "content": "",
-            "tool_calls": [
-                {
-                    "id": "call_b",
-                    "type": "function",
-                    "function": {"name": "tool_b", "arguments": "{}"},
-                },
-            ],
-        },
-        {"role": "tool", "tool_call_id": "call_b", "content": "result_b"},
-    ]
-
-    converse_msgs, _ = llm._format_messages_for_converse(messages)
-
-    tool_result_messages = [
-        m for m in converse_msgs
-        if m.get("role") == "user"
-        and any("toolResult" in b for b in m.get("content", []))
-    ]
-
-    # Two separate tool-result messages (one per assistant turn)
-    assert len(tool_result_messages) == 2, (
-        "Tool results from different assistant turns must remain separate"
-    )
-    assert tool_result_messages[0]["content"][0]["toolResult"]["toolUseId"] == "call_a"
-    assert tool_result_messages[1]["content"][0]["toolResult"]["toolUseId"] == "call_b"

lib/crewai/tests/mcp/test_amp_mcp.py

@@ -268,54 +268,6 @@ class TestGetMCPToolsAmpIntegration:
         assert len(tools) == 1
         assert tools[0].name == "mcp_notion_so_sse_search"
 
-    @patch("crewai.mcp.tool_resolver.MCPClient")
-    @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs")
-    def test_tool_filter_with_hyphenated_hash_syntax(
-        self, mock_fetch, mock_client_class, agent
-    ):
-        """notion#get-page must match the tool whose sanitized name is get_page."""
-        mock_fetch.return_value = {
-            "notion": {
-                "type": "sse",
-                "url": "https://mcp.notion.so/sse",
-                "headers": {"Authorization": "Bearer token"},
-            },
-        }
-
-        hyphenated_tool_definitions = [
-            {
-                "name": "get_page",
-                "original_name": "get-page",
-                "description": "Get a page",
-                "inputSchema": {},
-            },
-            {
-                "name": "search",
-                "original_name": "search",
-                "description": "Search tool",
-                "inputSchema": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string", "description": "Search query"}
-                    },
-                    "required": ["query"],
-                },
-            },
-        ]
-
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=hyphenated_tool_definitions)
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        tools = agent.get_mcp_tools(["notion#get-page"])
-
-        mock_fetch.assert_called_once_with(["notion"])
-        assert len(tools) == 1
-        assert tools[0].name.endswith("_get_page")
-
     @patch("crewai.mcp.tool_resolver.MCPClient")
     @patch.object(MCPToolResolver, "_fetch_amp_mcp_configs")
     def test_deduplicates_slugs(
@@ -419,87 +371,3 @@ class TestGetMCPToolsAmpIntegration:
         mock_external.assert_called_once_with("https://external.mcp.com/api")
         # 2 from notion + 1 from external + 2 from http_config
         assert len(tools) == 5
-
-
-class TestResolveExternalToolFilter:
-    """Tests for _resolve_external with #tool-name filtering."""
-
-    @pytest.fixture
-    def agent(self):
-        return Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-        )
-
-    @pytest.fixture
-    def resolver(self, agent):
-        return MCPToolResolver(agent=agent, logger=agent._logger)
-
-    @patch.object(MCPToolResolver, "_get_mcp_tool_schemas")
-    def test_filters_hyphenated_tool_name(self, mock_schemas, resolver):
-        """https://...#get-page must match the sanitized key get_page in schemas."""
-        mock_schemas.return_value = {
-            "get_page": {
-                "description": "Get a page",
-                "args_schema": None,
-            },
-            "search": {
-                "description": "Search tool",
-                "args_schema": None,
-            },
-        }
-
-        tools = resolver._resolve_external("https://mcp.example.com/api#get-page")
-
-        assert len(tools) == 1
-        assert "get_page" in tools[0].name
-
-    @patch.object(MCPToolResolver, "_get_mcp_tool_schemas")
-    def test_filters_underscored_tool_name(self, mock_schemas, resolver):
-        """https://...#get_page must also match the sanitized key get_page."""
-        mock_schemas.return_value = {
-            "get_page": {
-                "description": "Get a page",
-                "args_schema": None,
-            },
-            "search": {
-                "description": "Search tool",
-                "args_schema": None,
-            },
-        }
-
-        tools = resolver._resolve_external("https://mcp.example.com/api#get_page")
-
-        assert len(tools) == 1
-        assert "get_page" in tools[0].name
-
-    @patch.object(MCPToolResolver, "_get_mcp_tool_schemas")
-    def test_returns_all_tools_without_hash(self, mock_schemas, resolver):
-        mock_schemas.return_value = {
-            "get_page": {
-                "description": "Get a page",
-                "args_schema": None,
-            },
-            "search": {
-                "description": "Search tool",
-                "args_schema": None,
-            },
-        }
-
-        tools = resolver._resolve_external("https://mcp.example.com/api")
-
-        assert len(tools) == 2
-
-    @patch.object(MCPToolResolver, "_get_mcp_tool_schemas")
-    def test_returns_empty_for_nonexistent_tool(self, mock_schemas, resolver):
-        mock_schemas.return_value = {
-            "search": {
-                "description": "Search tool",
-                "args_schema": None,
-            },
-        }
-
-        tools = resolver._resolve_external("https://mcp.example.com/api#nonexistent")
-
-        assert len(tools) == 0

lib/crewai/tests/mcp/test_mcp_config.py

@@ -1,5 +1,4 @@
 import asyncio
-import concurrent.futures
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -31,17 +30,6 @@ def mock_tool_definitions():
     ]
 
 
-def _make_mock_client(tool_definitions):
-    """Create a mock MCPClient that returns *tool_definitions*."""
-    client = AsyncMock()
-    client.list_tools = AsyncMock(return_value=tool_definitions)
-    client.connected = False
-    client.connect = AsyncMock()
-    client.disconnect = AsyncMock()
-    client.call_tool = AsyncMock(return_value="test result")
-    return client
-
-
 def test_agent_with_stdio_mcp_config(mock_tool_definitions):
     """Test agent setup with MCPServerStdio configuration."""
     stdio_config = MCPServerStdio(
@@ -57,8 +45,14 @@ def test_agent_with_stdio_mcp_config(mock_tool_definitions):
         mcps=[stdio_config],
     )
 
+
     with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class:
-        mock_client_class.return_value = _make_mock_client(mock_tool_definitions)
+        mock_client = AsyncMock()
+        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
+        mock_client.connected = False  # Will trigger connect
+        mock_client.connect = AsyncMock()
+        mock_client.disconnect = AsyncMock()
+        mock_client_class.return_value = mock_client
 
         tools = agent.get_mcp_tools([stdio_config])
 
@@ -66,7 +60,8 @@ def test_agent_with_stdio_mcp_config(mock_tool_definitions):
         assert all(isinstance(tool, BaseTool) for tool in tools)
 
         mock_client_class.assert_called_once()
-        transport = mock_client_class.call_args.kwargs["transport"]
+        call_args = mock_client_class.call_args
+        transport = call_args.kwargs["transport"]
         assert transport.command == "python"
         assert transport.args == ["server.py"]
         assert transport.env == {"API_KEY": "test_key"}
@@ -88,7 +83,12 @@ def test_agent_with_http_mcp_config(mock_tool_definitions):
     )
 
     with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class:
-        mock_client_class.return_value = _make_mock_client(mock_tool_definitions)
+        mock_client = AsyncMock()
+        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
+        mock_client.connected = False  # Will trigger connect
+        mock_client.connect = AsyncMock()
+        mock_client.disconnect = AsyncMock()
+        mock_client_class.return_value = mock_client
 
         tools = agent.get_mcp_tools([http_config])
 
@@ -96,7 +96,8 @@ def test_agent_with_http_mcp_config(mock_tool_definitions):
         assert all(isinstance(tool, BaseTool) for tool in tools)
 
         mock_client_class.assert_called_once()
-        transport = mock_client_class.call_args.kwargs["transport"]
+        call_args = mock_client_class.call_args
+        transport = call_args.kwargs["transport"]
         assert transport.url == "https://api.example.com/mcp"
         assert transport.headers == {"Authorization": "Bearer test_token"}
         assert transport.streamable is True
@@ -117,7 +118,12 @@ def test_agent_with_sse_mcp_config(mock_tool_definitions):
     )
 
     with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class:
-        mock_client_class.return_value = _make_mock_client(mock_tool_definitions)
+        mock_client = AsyncMock()
+        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
+        mock_client.connected = False
+        mock_client.connect = AsyncMock()
+        mock_client.disconnect = AsyncMock()
+        mock_client_class.return_value = mock_client
 
         tools = agent.get_mcp_tools([sse_config])
 
@@ -125,7 +131,8 @@ def test_agent_with_sse_mcp_config(mock_tool_definitions):
         assert all(isinstance(tool, BaseTool) for tool in tools)
 
         mock_client_class.assert_called_once()
-        transport = mock_client_class.call_args.kwargs["transport"]
+        call_args = mock_client_class.call_args
+        transport = call_args.kwargs["transport"]
         assert transport.url == "https://api.example.com/mcp/sse"
         assert transport.headers == {"Authorization": "Bearer test_token"}
 
@@ -135,7 +142,13 @@ def test_mcp_tool_execution_in_sync_context(mock_tool_definitions):
     http_config = MCPServerHTTP(url="https://api.example.com/mcp")
 
     with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class:
-        mock_client_class.return_value = _make_mock_client(mock_tool_definitions)
+        mock_client = AsyncMock()
+        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
+        mock_client.connected = False
+        mock_client.connect = AsyncMock()
+        mock_client.disconnect = AsyncMock()
+        mock_client.call_tool = AsyncMock(return_value="test result")
+        mock_client_class.return_value = mock_client
 
         agent = Agent(
             role="Test Agent",
@@ -147,12 +160,12 @@ def test_mcp_tool_execution_in_sync_context(mock_tool_definitions):
         tools = agent.get_mcp_tools([http_config])
         assert len(tools) == 2
 
+
         tool = tools[0]
         result = tool.run(query="test query")
 
         assert result == "test result"
-        # 1 discovery + 1 for the run() invocation
-        assert mock_client_class.call_count == 2
+        mock_client.call_tool.assert_called()
 
 
 @pytest.mark.asyncio
@@ -161,7 +174,13 @@ async def test_mcp_tool_execution_in_async_context(mock_tool_definitions):
     http_config = MCPServerHTTP(url="https://api.example.com/mcp")
 
     with patch("crewai.mcp.tool_resolver.MCPClient") as mock_client_class:
-        mock_client_class.return_value = _make_mock_client(mock_tool_definitions)
+        mock_client = AsyncMock()
+        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
+        mock_client.connected = False
+        mock_client.connect = AsyncMock()
+        mock_client.disconnect = AsyncMock()
+        mock_client.call_tool = AsyncMock(return_value="test result")
+        mock_client_class.return_value = mock_client
 
         agent = Agent(
             role="Test Agent",
@@ -173,129 +192,9 @@ async def test_mcp_tool_execution_in_async_context(mock_tool_definitions):
         tools = agent.get_mcp_tools([http_config])
         assert len(tools) == 2
 
+
         tool = tools[0]
         result = tool.run(query="test query")
 
         assert result == "test result"
-        assert mock_client_class.call_count == 2
-
-
-def test_each_invocation_gets_fresh_client(mock_tool_definitions):
-    """Every tool.run() must create its own MCPClient (no shared state)."""
-    http_config = MCPServerHTTP(url="https://api.example.com/mcp")
-
-    clients_created: list = []
-
-    def _make_client(**kwargs):
-        client = _make_mock_client(mock_tool_definitions)
-        clients_created.append(client)
-        return client
-
-    with patch("crewai.mcp.tool_resolver.MCPClient", side_effect=_make_client):
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            mcps=[http_config],
-        )
-
-        tools = agent.get_mcp_tools([http_config])
-        assert len(tools) == 2
-        # 1 discovery client so far
-        assert len(clients_created) == 1
-
-        # Two sequential calls to the same tool must create 2 new clients
-        tools[0].run(query="q1")
-        tools[0].run(query="q2")
-        assert len(clients_created) == 3
-        assert clients_created[1] is not clients_created[2]
-
-
-def test_parallel_mcp_tool_execution_same_tool(mock_tool_definitions):
-    """Parallel calls to the *same* tool must not interfere."""
-    http_config = MCPServerHTTP(url="https://api.example.com/mcp")
-
-    call_log: list[str] = []
-
-    def _make_client(**kwargs):
-        client = AsyncMock()
-        client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        client.connected = False
-        client.connect = AsyncMock()
-        client.disconnect = AsyncMock()
-
-        async def _call_tool(name, args):
-            call_log.append(name)
-            await asyncio.sleep(0.05)
-            return f"result-{name}"
-
-        client.call_tool = AsyncMock(side_effect=_call_tool)
-        return client
-
-    with patch("crewai.mcp.tool_resolver.MCPClient", side_effect=_make_client):
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            mcps=[http_config],
-        )
-
-        tools = agent.get_mcp_tools([http_config])
-        assert len(tools) >= 1
-        tool = tools[0]
-
-        # Call the SAME tool concurrently -- the exact scenario from the bug
-        with concurrent.futures.ThreadPoolExecutor(max_workers=2) as pool:
-            futures = [
-                pool.submit(tool.run, query="q1"),
-                pool.submit(tool.run, query="q2"),
-            ]
-            results = [f.result() for f in concurrent.futures.as_completed(futures)]
-
-    assert len(results) == 2
-    assert all("result-" in r for r in results)
-    assert len(call_log) == 2
-
-
-def test_parallel_mcp_tool_execution_different_tools(mock_tool_definitions):
-    """Parallel calls to different tools from the same server must not interfere."""
-    http_config = MCPServerHTTP(url="https://api.example.com/mcp")
-
-    call_log: list[str] = []
-
-    def _make_client(**kwargs):
-        client = AsyncMock()
-        client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        client.connected = False
-        client.connect = AsyncMock()
-        client.disconnect = AsyncMock()
-
-        async def _call_tool(name, args):
-            call_log.append(name)
-            await asyncio.sleep(0.05)
-            return f"result-{name}"
-
-        client.call_tool = AsyncMock(side_effect=_call_tool)
-        return client
-
-    with patch("crewai.mcp.tool_resolver.MCPClient", side_effect=_make_client):
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            mcps=[http_config],
-        )
-
-        tools = agent.get_mcp_tools([http_config])
-        assert len(tools) == 2
-
-        with concurrent.futures.ThreadPoolExecutor(max_workers=2) as pool:
-            futures = [
-                pool.submit(tools[0].run, query="q1"),
-                pool.submit(tools[1].run, query="q2"),
-            ]
-            results = [f.result() for f in concurrent.futures.as_completed(futures)]
-
-    assert len(results) == 2
-    assert all("result-" in r for r in results)
-    assert len(call_log) == 2
+        mock_client.call_tool.assert_called()

lib/crewai/tests/memory/test_unified_memory.py

@@ -172,8 +172,8 @@ def test_memory_scope_slice(tmp_path: Path, mock_embedder: MagicMock) -> None:
     sc = mem.scope("/agent/1")
     assert sc._root in ("/agent/1", "/agent/1/")
     sl = mem.slice(["/a", "/b"], read_only=True)
-    assert sl.read_only is True
-    assert "/a" in sl.scopes and "/b" in sl.scopes
+    assert sl._read_only is True
+    assert "/a" in sl._scopes and "/b" in sl._scopes
 
 
 def test_memory_list_scopes_info_tree(tmp_path: Path, mock_embedder: MagicMock) -> None:
@@ -198,7 +198,7 @@ def test_memory_scope_remember_recall(tmp_path: Path, mock_embedder: MagicMock)
     from crewai.memory.memory_scope import MemoryScope
 
     mem = Memory(storage=str(tmp_path / "db5"), llm=MagicMock(), embedder=mock_embedder)
-    scope = MemoryScope(memory=mem, root_path="/crew/1")
+    scope = MemoryScope(mem, "/crew/1")
     scope.remember("Scoped note", scope="/", categories=[], importance=0.5, metadata={})
     results = scope.recall("note", limit=5, depth="shallow")
     assert len(results) >= 1
@@ -213,7 +213,7 @@ def test_memory_slice_recall(tmp_path: Path, mock_embedder: MagicMock) -> None:
 
     mem = Memory(storage=str(tmp_path / "db6"), llm=MagicMock(), embedder=mock_embedder)
     mem.remember("In scope A", scope="/a", categories=[], importance=0.5, metadata={})
-    sl = MemorySlice(memory=mem, scopes=["/a"], read_only=True)
+    sl = MemorySlice(mem, ["/a"], read_only=True)
     matches = sl.recall("scope", limit=5, depth="shallow")
     assert isinstance(matches, list)
 
@@ -223,7 +223,7 @@ def test_memory_slice_remember_is_noop_when_read_only(tmp_path: Path, mock_embed
     from crewai.memory.memory_scope import MemorySlice
 
     mem = Memory(storage=str(tmp_path / "db7"), llm=MagicMock(), embedder=mock_embedder)
-    sl = MemorySlice(memory=mem, scopes=["/a"], read_only=True)
+    sl = MemorySlice(mem, ["/a"], read_only=True)
     result = sl.remember("x", scope="/a")
     assert result is None
     assert mem.list_records() == []
@@ -319,7 +319,7 @@ def test_executor_save_to_memory_calls_extract_then_remember_per_item() -> None:
     from crewai.agents.parser import AgentFinish
 
     mock_memory = MagicMock()
-    mock_memory.read_only = False
+    mock_memory._read_only = False
     mock_memory.extract_memories.return_value = ["Fact A.", "Fact B."]
 
     mock_agent = MagicMock()
@@ -360,7 +360,7 @@ def test_executor_save_to_memory_skips_delegation_output() -> None:
     from crewai.utilities.string_utils import sanitize_tool_name
 
     mock_memory = MagicMock()
-    mock_memory.read_only = False
+    mock_memory._read_only = False
     mock_agent = MagicMock()
     mock_agent.memory = mock_memory
     mock_agent._logger = MagicMock()
@@ -393,7 +393,7 @@ def test_memory_scope_extract_memories_delegates() -> None:
 
     mock_memory = MagicMock()
     mock_memory.extract_memories.return_value = ["Scoped fact."]
-    scope = MemoryScope(memory=mock_memory, root_path="/agent/1")
+    scope = MemoryScope(mock_memory, "/agent/1")
     result = scope.extract_memories("Some content")
     mock_memory.extract_memories.assert_called_once_with("Some content")
     assert result == ["Scoped fact."]
@@ -405,7 +405,7 @@ def test_memory_slice_extract_memories_delegates() -> None:
 
     mock_memory = MagicMock()
     mock_memory.extract_memories.return_value = ["Sliced fact."]
-    sl = MemorySlice(memory=mock_memory, scopes=["/a", "/b"], read_only=True)
+    sl = MemorySlice(mock_memory, ["/a", "/b"], read_only=True)
     result = sl.extract_memories("Some content")
     mock_memory.extract_memories.assert_called_once_with("Some content")
     assert result == ["Sliced fact."]
@@ -670,10 +670,10 @@ def test_agent_kickoff_memory_recall_and_save(tmp_path: Path, mock_embedder: Mag
         verbose=False,
     )
 
-    # Patch on the class to avoid Pydantic BaseModel __delattr__ restriction
-    with patch.object(Memory, "recall", wraps=mem.recall) as recall_mock, \
-         patch.object(Memory, "extract_memories", return_value=["PostgreSQL is used."]) as extract_mock, \
-         patch.object(Memory, "remember_many", wraps=mem.remember_many) as remember_many_mock:
+    # Mock recall to verify it's called, but return real results
+    with patch.object(mem, "recall", wraps=mem.recall) as recall_mock, \
+         patch.object(mem, "extract_memories", return_value=["PostgreSQL is used."]) as extract_mock, \
+         patch.object(mem, "remember_many", wraps=mem.remember_many) as remember_many_mock:
         result = agent.kickoff("What database do we use?")
 
     assert result is not None

lib/crewai/tests/telemetry/test_telemetry.py

@@ -121,41 +121,3 @@ def test_telemetry_singleton_pattern():
         thread.join()
 
     assert all(instance is telemetry1 for instance in instances)
-
-
-def test_no_signal_handler_traceback_in_non_main_thread():
-    """Signal handler registration should be silently skipped in non-main threads.
-
-    Regression test for https://github.com/crewAIInc/crewAI/issues/4289
-    """
-    errors: list[Exception] = []
-    mock_holder: dict = {}
-
-    def init_in_thread():
-        try:
-            Telemetry._instance = None
-            with (
-                patch.dict(
-                    os.environ,
-                    {"CREWAI_DISABLE_TELEMETRY": "false", "OTEL_SDK_DISABLED": "false"},
-                ),
-                patch("crewai.telemetry.telemetry.TracerProvider"),
-                patch("signal.signal") as mock_signal,
-                patch("crewai.telemetry.telemetry.logger") as mock_logger,
-            ):
-                Telemetry()
-                mock_holder["signal"] = mock_signal
-                mock_holder["logger"] = mock_logger
-        except Exception as exc:
-            errors.append(exc)
-
-    thread = threading.Thread(target=init_in_thread)
-    thread.start()
-    thread.join()
-
-    assert not errors, f"Unexpected error: {errors}"
-    assert mock_holder, "Thread did not execute"
-    mock_holder["signal"].assert_not_called()
-    mock_holder["logger"].debug.assert_any_call(
-        "Skipping signal handler registration: not running in main thread"
-    )

lib/crewai/tests/test_async_human_feedback.py

@@ -971,128 +971,6 @@ class TestCollapseToOutcomeJsonParsing:
         assert mock_llm.call.call_count == 2
 
 
-class TestLLMObjectPreservedInContext:
-    """Tests that BaseLLM objects have their model string preserved in PendingFeedbackContext."""
-
-    @patch("crewai.flow.flow.crewai_event_bus.emit")
-    def test_basellm_object_model_string_survives_roundtrip(self, mock_emit: MagicMock) -> None:
-        """Test that when llm is a BaseLLM object, its model string is stored in context
-        so that outcome collapsing works after async pause/resume.
-
-        This is the exact bug: locally the sync path keeps the LLM object in memory,
-        but in production the async path serializes the context and the LLM object was
-        discarded (stored as None), causing resume to skip classification and always
-        fall back to emit[0].
-        """
-        with tempfile.TemporaryDirectory() as tmpdir:
-            db_path = os.path.join(tmpdir, "test_flows.db")
-            persistence = SQLiteFlowPersistence(db_path)
-
-            # Create a mock BaseLLM object (not a string)
-            mock_llm_obj = MagicMock()
-            mock_llm_obj.model = "gemini/gemini-2.0-flash"
-
-            class PausingProvider:
-                def __init__(self, persistence: SQLiteFlowPersistence):
-                    self.persistence = persistence
-                    self.captured_context: PendingFeedbackContext | None = None
-
-                def request_feedback(
-                    self, context: PendingFeedbackContext, flow: Flow
-                ) -> str:
-                    self.captured_context = context
-                    self.persistence.save_pending_feedback(
-                        flow_uuid=context.flow_id,
-                        context=context,
-                        state_data=flow.state if isinstance(flow.state, dict) else flow.state.model_dump(),
-                    )
-                    raise HumanFeedbackPending(context=context)
-
-            provider = PausingProvider(persistence)
-
-            class TestFlow(Flow):
-                result_path: str = ""
-
-                @start()
-                @human_feedback(
-                    message="Approve?",
-                    emit=["needs_changes", "approved"],
-                    llm=mock_llm_obj,
-                    default_outcome="approved",
-                    provider=provider,
-                )
-                def review(self):
-                    return "content for review"
-
-                @listen("approved")
-                def handle_approved(self):
-                    self.result_path = "approved"
-                    return "Approved!"
-
-                @listen("needs_changes")
-                def handle_changes(self):
-                    self.result_path = "needs_changes"
-                    return "Changes needed"
-
-            # Phase 1: Start flow (should pause)
-            flow1 = TestFlow(persistence=persistence)
-            result = flow1.kickoff()
-            assert isinstance(result, HumanFeedbackPending)
-
-            # Verify the context stored the model STRING, not None
-            assert provider.captured_context is not None
-            assert provider.captured_context.llm == "gemini/gemini-2.0-flash"
-
-            # Verify it survives persistence roundtrip
-            flow_id = result.context.flow_id
-            loaded = persistence.load_pending_feedback(flow_id)
-            assert loaded is not None
-            _, loaded_context = loaded
-            assert loaded_context.llm == "gemini/gemini-2.0-flash"
-
-            # Phase 2: Resume with positive feedback - should use LLM to classify
-            flow2 = TestFlow.from_pending(flow_id, persistence)
-            assert flow2._pending_feedback_context is not None
-            assert flow2._pending_feedback_context.llm == "gemini/gemini-2.0-flash"
-
-            # Mock _collapse_to_outcome to verify it gets called (not skipped)
-            with patch.object(flow2, "_collapse_to_outcome", return_value="approved") as mock_collapse:
-                flow2.resume("this looks good, proceed!")
-
-            # The key assertion: _collapse_to_outcome was called (not skipped due to llm=None)
-            mock_collapse.assert_called_once_with(
-                feedback="this looks good, proceed!",
-                outcomes=["needs_changes", "approved"],
-                llm="gemini/gemini-2.0-flash",
-            )
-            assert flow2.last_human_feedback.outcome == "approved"
-            assert flow2.result_path == "approved"
-
-    def test_string_llm_still_works(self) -> None:
-        """Test that passing llm as a string still works correctly."""
-        context = PendingFeedbackContext(
-            flow_id="str-llm-test",
-            flow_class="test.Flow",
-            method_name="review",
-            method_output="output",
-            message="Review:",
-            emit=["approved", "rejected"],
-            llm="gpt-4o-mini",
-        )
-
-        serialized = context.to_dict()
-        restored = PendingFeedbackContext.from_dict(serialized)
-        assert restored.llm == "gpt-4o-mini"
-
-    def test_none_llm_when_no_model_attr(self) -> None:
-        """Test that llm is None when object has no model attribute."""
-        mock_obj = MagicMock(spec=[])  # No attributes
-
-        # Simulate what the decorator does
-        llm_value = mock_obj if isinstance(mock_obj, str) else getattr(mock_obj, "model", None)
-        assert llm_value is None
-
-
 class TestAsyncHumanFeedbackEdgeCases:
     """Edge case tests for async human feedback."""
 

lib/crewai/tests/test_crew.py

@@ -36,7 +36,7 @@ from crewai.flow import Flow, start
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
-from crewai.memory.unified_memory import Memory
+
 from crewai.process import Process
 from crewai.project import CrewBase, agent, before_kickoff, crew, task
 from crewai.task import Task
@@ -2618,9 +2618,9 @@ def test_memory_remember_called_after_task():
     )
 
     with patch.object(
-        Memory, "extract_memories", wraps=crew._memory.extract_memories
+        crew._memory, "extract_memories", wraps=crew._memory.extract_memories
     ) as extract_mock, patch.object(
-        Memory, "remember", wraps=crew._memory.remember
+        crew._memory, "remember", wraps=crew._memory.remember
     ) as remember_mock:
         crew.kickoff()
 
@@ -4773,13 +4773,13 @@ def test_memory_remember_receives_task_content():
         # Mock extract_memories to return fake memories and capture the raw input.
         # No wraps= needed -- the test only checks what args it receives, not the output.
         patch.object(
-            Memory, "extract_memories", return_value=["Fake memory."]
+            crew._memory, "extract_memories", return_value=["Fake memory."]
         ) as extract_mock,
         # Mock recall to avoid LLM calls for query analysis (not in cassette).
-        patch.object(Memory, "recall", return_value=[]),
+        patch.object(crew._memory, "recall", return_value=[]),
         # Mock remember_many to prevent the background save from triggering
         # LLM calls (field resolution) that aren't in the cassette.
-        patch.object(Memory, "remember_many", return_value=[]),
+        patch.object(crew._memory, "remember_many", return_value=[]),
     ):
         crew.kickoff()
 

lib/crewai/tests/test_flow.py

@@ -1843,213 +1843,3 @@ def test_cyclic_flow_works_with_persist_and_id_input():
             f"'{method}' should fire 3 times, "
             f"got {len(events)}: {execution_order}"
         )
-
-
-@pytest.mark.timeout(5)
-def test_self_listening_method_does_not_loop():
-    """A method whose @listen label matches its own name must not loop forever.
-
-    Without the guard, 'process' re-triggers itself on every completion,
-    running indefinitely (timeout → FAIL).  The fix caps method calls
-    and raises RecursionError (PASS).
-    """
-
-    class SelfListenFlow(Flow):
-        @start()
-        def begin(self):
-            return "process"
-
-        @router(begin)
-        def route(self):
-            return "process"
-
-        @listen("process")
-        def process(self):
-            pass
-
-    flow = SelfListenFlow()
-    with pytest.raises(RecursionError, match="infinite loop"):
-        flow.kickoff()
-
-
-def test_or_condition_self_listen_fires_once():
-    """or_() with a self-referencing label only fires once due to or_() guard."""
-    call_count = 0
-
-    class OrSelfListenFlow(Flow):
-        @start()
-        def begin(self):
-            return "process"
-
-        @router(begin)
-        def route(self):
-            return "process"
-
-        @listen(or_("other_trigger", "process"))
-        def process(self):
-            nonlocal call_count
-            call_count += 1
-
-    flow = OrSelfListenFlow()
-    flow.kickoff()
-    assert call_count == 1
-
-class ListState(BaseModel):
-    items: list = []
-
-
-class DictState(BaseModel):
-    data: dict = {}
-
-
-class _ListFlow(Flow[ListState]):
-    @start()
-    def populate(self):
-        self.state.items = [3, 1, 4, 1, 5, 9, 2, 6]
-
-
-class _DictFlow(Flow[DictState]):
-    @start()
-    def populate(self):
-        self.state.data = {"a": 1, "b": 2, "c": 3}
-
-
-def _make_list_flow():
-    flow = _ListFlow()
-    flow.kickoff()
-    return flow
-
-
-def _make_dict_flow():
-    flow = _DictFlow()
-    flow.kickoff()
-    return flow
-
-
-def test_locked_list_proxy_index():
-    flow = _make_list_flow()
-    assert flow.state.items.index(4) == 2
-    assert flow.state.items.index(1, 2) == 3
-
-
-def test_locked_list_proxy_index_missing_raises():
-    flow = _make_list_flow()
-    with pytest.raises(ValueError):
-        flow.state.items.index(999)
-
-
-def test_locked_list_proxy_count():
-    flow = _make_list_flow()
-    assert flow.state.items.count(1) == 2
-    assert flow.state.items.count(999) == 0
-
-
-def test_locked_list_proxy_sort():
-    flow = _make_list_flow()
-    flow.state.items.sort()
-    assert list(flow.state.items) == [1, 1, 2, 3, 4, 5, 6, 9]
-
-
-def test_locked_list_proxy_sort_reverse():
-    flow = _make_list_flow()
-    flow.state.items.sort(reverse=True)
-    assert list(flow.state.items) == [9, 6, 5, 4, 3, 2, 1, 1]
-
-
-def test_locked_list_proxy_sort_key():
-    flow = _make_list_flow()
-    flow.state.items.sort(key=lambda x: -x)
-    assert list(flow.state.items) == [9, 6, 5, 4, 3, 2, 1, 1]
-
-
-def test_locked_list_proxy_reverse():
-    flow = _make_list_flow()
-    original = list(flow.state.items)
-    flow.state.items.reverse()
-    assert list(flow.state.items) == list(reversed(original))
-
-
-def test_locked_list_proxy_copy():
-    flow = _make_list_flow()
-    copied = flow.state.items.copy()
-    assert copied == [3, 1, 4, 1, 5, 9, 2, 6]
-    assert isinstance(copied, list)
-    copied.append(999)
-    assert 999 not in flow.state.items
-
-
-def test_locked_list_proxy_add():
-    flow = _make_list_flow()
-    result = flow.state.items + [10, 11]
-    assert result == [3, 1, 4, 1, 5, 9, 2, 6, 10, 11]
-    assert len(flow.state.items) == 8
-
-
-def test_locked_list_proxy_radd():
-    flow = _make_list_flow()
-    result = [0] + flow.state.items
-    assert result[0] == 0
-    assert len(result) == 9
-
-
-def test_locked_list_proxy_iadd():
-    flow = _make_list_flow()
-    flow.state.items += [10]
-    assert 10 in flow.state.items
-    # Verify no deadlock: mutations must still work after +=
-    flow.state.items.append(99)
-    assert 99 in flow.state.items
-
-
-def test_locked_list_proxy_mul():
-    flow = _make_list_flow()
-    result = flow.state.items * 2
-    assert len(result) == 16
-
-
-def test_locked_list_proxy_rmul():
-    flow = _make_list_flow()
-    result = 2 * flow.state.items
-    assert len(result) == 16
-
-
-def test_locked_list_proxy_reversed():
-    flow = _make_list_flow()
-    original = list(flow.state.items)
-    assert list(reversed(flow.state.items)) == list(reversed(original))
-
-
-def test_locked_dict_proxy_copy():
-    flow = _make_dict_flow()
-    copied = flow.state.data.copy()
-    assert copied == {"a": 1, "b": 2, "c": 3}
-    assert isinstance(copied, dict)
-    copied["z"] = 99
-    assert "z" not in flow.state.data
-
-
-def test_locked_dict_proxy_or():
-    flow = _make_dict_flow()
-    result = flow.state.data | {"d": 4}
-    assert result == {"a": 1, "b": 2, "c": 3, "d": 4}
-    assert "d" not in flow.state.data
-
-
-def test_locked_dict_proxy_ror():
-    flow = _make_dict_flow()
-    result = {"z": 0} | flow.state.data
-    assert result == {"z": 0, "a": 1, "b": 2, "c": 3}
-
-
-def test_locked_dict_proxy_ior():
-    flow = _make_dict_flow()
-    flow.state.data |= {"d": 4}
-    assert flow.state.data["d"] == 4
-    # Verify no deadlock: mutations must still work after |=
-    flow.state.data["e"] = 5
-    assert flow.state.data["e"] == 5
-
-
-def test_locked_dict_proxy_reversed():
-    flow = _make_dict_flow()
-    assert list(reversed(flow.state.data)) == ["c", "b", "a"]

lib/crewai/tests/tracing/test_tracing.py

@@ -840,87 +840,3 @@ class TestTraceListenerSetup:
                 mock_mark_failed.assert_called_once_with(
                     "test_batch_id_12345", "Internal Server Error"
                 )
-
-    def test_ephemeral_batch_includes_anon_id(self):
-        """Test that ephemeral batch initialization sends anon_id from get_user_id()"""
-        fake_user_id = "abc123def456"
-
-        with (
-            patch(
-                "crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
-                return_value=True,
-            ),
-            patch(
-                "crewai.events.listeners.tracing.trace_batch_manager.get_user_id",
-                return_value=fake_user_id,
-            ),
-            patch(
-                "crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
-                return_value=False,
-            ),
-        ):
-            batch_manager = TraceBatchManager()
-
-            mock_response = MagicMock(
-                status_code=201,
-                json=MagicMock(return_value={
-                    "ephemeral_trace_id": "test-trace-id",
-                    "access_code": "TRACE-abc123",
-                }),
-            )
-
-            with patch.object(
-                batch_manager.plus_api,
-                "initialize_ephemeral_trace_batch",
-                return_value=mock_response,
-            ) as mock_init:
-                batch_manager.initialize_batch(
-                    user_context={"privacy_level": "standard"},
-                    execution_metadata={
-                        "execution_type": "crew",
-                        "crew_name": "test_crew",
-                    },
-                    use_ephemeral=True,
-                )
-
-                mock_init.assert_called_once()
-                payload = mock_init.call_args[0][0]
-                assert payload["user_identifier"] == fake_user_id
-                assert "ephemeral_trace_id" in payload
-
-    def test_non_ephemeral_batch_does_not_include_anon_id(self):
-        """Test that non-ephemeral batch initialization does not send anon_id"""
-        with (
-            patch(
-                "crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
-                return_value=True,
-            ),
-            patch(
-                "crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
-                return_value=False,
-            ),
-        ):
-            batch_manager = TraceBatchManager()
-
-            mock_response = MagicMock(
-                status_code=201,
-                json=MagicMock(return_value={"trace_id": "test-trace-id"}),
-            )
-
-            with patch.object(
-                batch_manager.plus_api,
-                "initialize_trace_batch",
-                return_value=mock_response,
-            ) as mock_init:
-                batch_manager.initialize_batch(
-                    user_context={"privacy_level": "standard"},
-                    execution_metadata={
-                        "execution_type": "crew",
-                        "crew_name": "test_crew",
-                    },
-                    use_ephemeral=False,
-                )
-
-                mock_init.assert_called_once()
-                payload = mock_init.call_args[0][0]
-                assert "user_identifier" not in payload

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.10.1"
+__version__ = "1.10.1a1"

pyproject.toml

@@ -8,9 +8,9 @@ authors = [
 
 [dependency-groups]
 dev = [
-    "ruff==0.15.1",
-    "mypy==1.19.1",
-    "pre-commit==4.5.1",
+    "ruff==0.14.7",
+    "mypy==1.19.0",
+    "pre-commit==4.5.0",
     "bandit==1.9.2",
     "pytest==8.4.2",
     "pytest-asyncio==1.3.0",
@@ -23,9 +23,9 @@ dev = [
     "pytest-split==0.10.0",
     "types-requests~=2.31.0.6",
     "types-pyyaml==6.0.*",
-    "types-regex==2026.1.15.*",
+    "types-regex==2024.11.6.*",
     "types-appdirs==1.4.*",
-    "boto3-stubs[bedrock-runtime]==1.42.40",
+    "boto3-stubs[bedrock-runtime]==1.40.54",
     "types-psycopg2==2.9.21.20251012",
     "types-pymysql==1.1.0.20250916",
     "types-aiofiles~=25.1.0",
@@ -153,7 +153,6 @@ override-dependencies = [
     "onnxruntime<1.24; python_version < '3.11'",
     "pillow>=12.1.1",
     "langchain-core>=0.3.80,<1",
-    "urllib3>=2.6.3",
 ]
 
 [tool.uv.workspace]

uv.lock

@@ -24,15 +24,14 @@ overrides = [
     { name = "onnxruntime", marker = "python_full_version < '3.11'", specifier = "<1.24" },
     { name = "pillow", specifier = ">=12.1.1" },
     { name = "rich", specifier = ">=13.7.1" },
-    { name = "urllib3", specifier = ">=2.6.3" },
 ]
 
 [manifest.dependency-groups]
 dev = [
     { name = "bandit", specifier = "==1.9.2" },
-    { name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.42.40" },
-    { name = "mypy", specifier = "==1.19.1" },
-    { name = "pre-commit", specifier = "==4.5.1" },
+    { name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.40.54" },
+    { name = "mypy", specifier = "==1.19.0" },
+    { name = "pre-commit", specifier = "==4.5.0" },
     { name = "pytest", specifier = "==8.4.2" },
     { name = "pytest-asyncio", specifier = "==1.3.0" },
     { name = "pytest-randomly", specifier = "==4.0.1" },
@@ -41,13 +40,13 @@ dev = [
     { name = "pytest-subprocess", specifier = "==1.5.3" },
     { name = "pytest-timeout", specifier = "==2.4.0" },
     { name = "pytest-xdist", specifier = "==3.8.0" },
-    { name = "ruff", specifier = "==0.15.1" },
+    { name = "ruff", specifier = "==0.14.7" },
     { name = "types-aiofiles", specifier = "~=25.1.0" },
     { name = "types-appdirs", specifier = "==1.4.*" },
     { name = "types-psycopg2", specifier = "==2.9.21.20251012" },
     { name = "types-pymysql", specifier = "==1.1.0.20250916" },
     { name = "types-pyyaml", specifier = "==6.0.*" },
-    { name = "types-regex", specifier = "==2026.1.15.*" },
+    { name = "types-regex", specifier = "==2024.11.6.*" },
     { name = "types-requests", specifier = "~=2.31.0.6" },
     { name = "vcrpy", specifier = "==7.0.0" },
 ]
@@ -596,7 +595,8 @@ dependencies = [
     { name = "pydantic" },
     { name = "starlette" },
     { name = "typing-extensions" },
-    { name = "urllib3" },
+    { name = "urllib3", version = "1.26.20", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation == 'PyPy'" },
+    { name = "urllib3", version = "2.6.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation != 'PyPy'" },
     { name = "uvicorn" },
     { name = "websockets" },
 ]
@@ -621,16 +621,16 @@ wheels = [
 
 [[package]]
 name = "boto3-stubs"
-version = "1.42.40"
+version = "1.40.54"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore-stubs" },
     { name = "types-s3transfer" },
     { name = "typing-extensions", marker = "python_full_version < '3.12'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/89/87/190df0854bcacc31d58dab28721f855d928ddd1d20c0ca2c201731d4622b/boto3_stubs-1.42.40.tar.gz", hash = "sha256:2689e235ae0deb6878fced175f7c2701fd8c088e6764de65e8c14085c1fc1914", size = 100886, upload-time = "2026-02-02T23:19:28.917Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e2/70/245477b7f07c9e1533c47fa69e611b172814423a6fd4637004f0d2a13b73/boto3_stubs-1.40.54.tar.gz", hash = "sha256:e21a9eda979a451935eb3196de3efbe15b9470e6bf9027406d1f6d0ac08b339e", size = 100919, upload-time = "2025-10-16T19:49:17.079Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e7/09/e1d031ceae85688c13dd16d84a0e6e416def62c6b23e04f7d318837ee355/boto3_stubs-1.42.40-py3-none-any.whl", hash = "sha256:66679f1075e094b15b2032d8cfc4f070a472e066b04ee1edf61aa44884a6d2cd", size = 69782, upload-time = "2026-02-02T23:19:20.16Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/52/ee9dadd1cc8911e16f18ca9fa036a10328e0a0d3fddd54fadcc1ca0f9143/boto3_stubs-1.40.54-py3-none-any.whl", hash = "sha256:548a4786785ba7b43ef4ef1a2a764bebbb0301525f3201091fcf412e4c8ce323", size = 69712, upload-time = "2025-10-16T19:49:12.847Z" },
 ]
 
 [package.optional-dependencies]
@@ -645,7 +645,8 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jmespath" },
     { name = "python-dateutil" },
-    { name = "urllib3" },
+    { name = "urllib3", version = "1.26.20", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation == 'PyPy'" },
+    { name = "urllib3", version = "2.6.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/35/c1/8c4c199ae1663feee579a15861e34f10b29da11ae6ea0ad7b6a847ef3823/botocore-1.40.70.tar.gz", hash = "sha256:61b1f2cecd54d1b28a081116fa113b97bf4e17da57c62ae2c2751fe4c528af1f", size = 14444592, upload-time = "2025-11-10T20:29:04.046Z" }
 wheels = [
@@ -1196,7 +1197,7 @@ requires-dist = [
     { name = "crewai-files", marker = "extra == 'file-processing'", editable = "lib/crewai-files" },
     { name = "crewai-tools", marker = "extra == 'tools'", editable = "lib/crewai-tools" },
     { name = "docling", marker = "extra == 'docling'", specifier = "~=2.75.0" },
-    { name = "google-genai", marker = "extra == 'google-genai'", specifier = "~=1.65.0" },
+    { name = "google-genai", marker = "extra == 'google-genai'", specifier = "~=1.49.0" },
     { name = "httpx", specifier = "~=0.28.1" },
     { name = "httpx-auth", marker = "extra == 'a2a'", specifier = "~=0.23.1" },
     { name = "httpx-sse", marker = "extra == 'a2a'", specifier = "~=0.4.0" },
@@ -1226,7 +1227,7 @@ requires-dist = [
     { name = "regex", specifier = "~=2026.1.15" },
     { name = "textual", specifier = ">=7.5.0" },
     { name = "tiktoken", marker = "extra == 'embeddings'", specifier = "~=0.8.0" },
-    { name = "tokenizers", specifier = ">=0.21,<1" },
+    { name = "tokenizers", specifier = "~=0.20.3" },
     { name = "tomli", specifier = "~=2.0.2" },
     { name = "tomli-w", specifier = "~=1.1.0" },
     { name = "uv", specifier = "~=0.9.13" },
@@ -1275,7 +1276,7 @@ requires-dist = [
     { name = "aiofiles", specifier = "~=24.1.0" },
     { name = "av", specifier = "~=13.0.0" },
     { name = "pillow", specifier = "~=12.1.1" },
-    { name = "pypdf", specifier = "~=6.7.5" },
+    { name = "pypdf", specifier = "~=6.7.4" },
     { name = "python-magic", specifier = ">=0.4.27" },
     { name = "tinytag", specifier = "~=1.10.0" },
 ]
@@ -1426,7 +1427,7 @@ requires-dist = [
     { name = "docker", specifier = "~=7.1.0" },
     { name = "exa-py", marker = "extra == 'exa-py'", specifier = ">=1.8.7" },
     { name = "firecrawl-py", marker = "extra == 'firecrawl-py'", specifier = ">=1.8.0" },
-    { name = "gitpython", marker = "extra == 'github'", specifier = ">=3.1.41,<4" },
+    { name = "gitpython", marker = "extra == 'github'", specifier = "==3.1.38" },
     { name = "hyperbrowser", marker = "extra == 'hyperbrowser'", specifier = ">=0.18.0" },
     { name = "langchain-apify", marker = "extra == 'apify'", specifier = ">=0.1.2,<1.0.0" },
     { name = "linkup-sdk", marker = "extra == 'linkup-sdk'", specifier = ">=0.2.2" },
@@ -1667,7 +1668,8 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pywin32", marker = "sys_platform == 'win32'" },
     { name = "requests" },
-    { name = "urllib3" },
+    { name = "urllib3", version = "1.26.20", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation == 'PyPy'" },
+    { name = "urllib3", version = "2.6.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/91/9b/4a2ea29aeba62471211598dac5d96825bb49348fa07e906ea930394a83ce/docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c", size = 117834, upload-time = "2024-05-23T11:13:57.216Z" }
 wheels = [
@@ -2201,14 +2203,14 @@ wheels = [
 
 [[package]]
 name = "gitpython"
-version = "3.1.46"
+version = "3.1.38"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "gitdb" },
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