Merge branch 'main' into gl/refactor/a2a-tool-based-delegation

Each remote A2A agent is now exposed to the local LLM as a BaseTool
(delegate_to_<card_name>); the local agent's tool-call loop drives
multi-turn delegation. The Literal-constrained AgentResponse model and
the explicit per-turn re-prompting loop are gone.

Closes #3897. The original failure mode — Pydantic literal_error when
skill.id != endpoint URL, and Gemini flash-lite hallucinating
out-of-enum values — is structurally impossible: provider-side tool-call
validation enforces the tool name, and there's no competing identifier.

docs/ar/concepts/flows.mdx

@@ -29,7 +29,6 @@ from crewai.flow.flow import Flow, listen, start
 from dotenv import load_dotenv
 from litellm import completion
 
-load_dotenv()
 
 class ExampleFlow(Flow):
     model = "gpt-4o-mini"

docs/en/concepts/flows.mdx

@@ -29,7 +29,6 @@ from crewai.flow.flow import Flow, listen, start
 from dotenv import load_dotenv
 from litellm import completion
 
-load_dotenv()
 
 class ExampleFlow(Flow):
     model = "gpt-4o-mini"

docs/en/guides/flows/mastering-flow-state.mdx

@@ -313,9 +313,9 @@ flow1 = PersistentCounterFlow()
 result1 = flow1.kickoff()
 print(f"First run result: {result1}")
 
-# Second run - pass the ID to load the persisted state
+# Second run - state is automatically loaded
 flow2 = PersistentCounterFlow()
-result2 = flow2.kickoff(inputs={"id": flow1.state.id})
+result2 = flow2.kickoff()
 print(f"Second run result: {result2}")  # Will be higher due to persisted state
 ```
 

docs/en/guides/tools/platform-tools-cli.mdx

@@ -1,131 +0,0 @@
----
-title: Platform Tools CLI
-description: Create, publish, and install custom tools on the CrewAI platform using the CLI.
-icon: terminal
-mode: "wide"
----
-
-## Overview
-
-The CrewAI CLI provides commands to manage custom tools on the **CrewAI platform** — a hosted tool registry that lets you share tools within your organization without publishing to PyPI.
-
-| Command | Purpose |
-|---------|---------|
-| `crewai tool create <handle>` | Scaffold a new tool project |
-| `crewai tool publish` | Publish the tool to the CrewAI platform |
-| `crewai tool install <handle>` | Install a platform tool into your crew project |
-
-<Note type="info" title="Platform vs PyPI">
-  These commands manage tools on the **CrewAI platform registry**. If you want to publish a standalone Python package to PyPI instead, see the [Publish Custom Tools to PyPI](/en/guides/tools/publish-custom-tools) guide.
-</Note>
-
-## Prerequisites
-
-- **CrewAI CLI** installed (`pip install crewai`)
-- **Authenticated** with the platform — run `crewai login` first
-
----
-
-## Step 1: Create a Tool Project
-
-Scaffold a new tool project:
-
-```bash
-crewai tool create my_custom_tool
-```
-
-This generates a project structure with the boilerplate you need to start building your tool.
-
-<Tip>
-  The `handle` is the unique identifier for your tool on the platform. Choose something descriptive and specific to what the tool does.
-</Tip>
-
-### Implement Your Tool
-
-Edit the generated tool file to add your logic. The tool follows the standard CrewAI tools contract — you can subclass `BaseTool` or use the `@tool` decorator:
-
-```python
-from crewai.tools import BaseTool
-
-class MyCustomTool(BaseTool):
-    name: str = "My Custom Tool"
-    description: str = "Description of what this tool does — be specific so agents know when to use it."
-
-    def _run(self, argument: str) -> str:
-        # Your tool logic here
-        return "result"
-```
-
-For the full tools API reference (input schemas, caching, async support, error handling), see the [Create Custom Tools](/en/learn/create-custom-tools) guide.
-
----
-
-## Step 2: Publish to the Platform
-
-From your tool project directory, publish it to the CrewAI platform:
-
-```bash
-crewai tool publish
-```
-
-### Options
-
-| Flag | Description |
-|------|-------------|
-| `--force` | Bypass Git remote validations |
-
-Tools are published privately to your organization by default.
-
----
-
-## Step 3: Install a Platform Tool
-
-To install a tool that's been published to the platform:
-
-```bash
-crewai tool install my_custom_tool
-```
-
-Once installed, you can use the tool in your crew like any other tool — assign it to an agent via the `tools` parameter.
-
----
-
-## Full Lifecycle Example
-
-```bash
-# 1. Authenticate with the platform
-crewai login
-
-# 2. Scaffold a new tool
-crewai tool create weather_lookup
-
-# 3. Implement your logic in the generated project
-cd weather_lookup
-# ... edit the tool file ...
-
-# 4. Publish to the platform
-crewai tool publish
-
-# 5. In another project, install and use it
-crewai tool install weather_lookup
-```
-
----
-
-## Platform Tools vs PyPI Packages
-
-| | Platform Tools | PyPI Packages |
-|---|---|---|
-| **Publish** | `crewai tool publish` | `uv build` + `uv publish` |
-| **Registry** | CrewAI platform | PyPI |
-| **Install** | `crewai tool install <handle>` | `pip install <package>` |
-| **Auth** | `crewai login` | PyPI account + token |
-| **Visibility** | Organization-scoped (private) | Always public |
-| **Guide** | This page | [Publish Custom Tools](/en/guides/tools/publish-custom-tools) |
-
----
-
-## Related
-
-- [Create Custom Tools](/en/learn/create-custom-tools) — Python API reference for building tools (BaseTool, @tool decorator)
-- [Publish Custom Tools to PyPI](/en/guides/tools/publish-custom-tools) — package and distribute tools as standalone Python libraries

docs/en/learn/create-custom-tools.mdx

@@ -12,9 +12,7 @@ incorporating the latest functionalities such as tool delegation, error handling
 enabling agents to perform a wide range of actions.
 
 <Tip>
-  **Want to publish your tool to the CrewAI platform?** Use the CLI to scaffold, publish, and share tools directly on the platform — see the [Platform Tools CLI](/en/guides/tools/platform-tools-cli) guide.
-
-  **Prefer publishing to PyPI?** Check out the [Publish Custom Tools](/en/guides/tools/publish-custom-tools) guide to package and distribute your tool as a standalone Python library.
+  **Want to publish your tool for the community?** If you're building a tool that others could benefit from, check out the [Publish Custom Tools](/en/guides/tools/publish-custom-tools) guide to learn how to package and distribute your tool on PyPI.
 </Tip>
 
 ### Subclassing `BaseTool`

docs/ko/concepts/flows.mdx

@@ -29,7 +29,6 @@ from crewai.flow.flow import Flow, listen, start
 from dotenv import load_dotenv
 from litellm import completion
 
-load_dotenv()
 
 class ExampleFlow(Flow):
     model = "gpt-4o-mini"

docs/pt-BR/concepts/flows.mdx

@@ -24,63 +24,7 @@ Os flows permitem que você crie fluxos de trabalho estruturados e orientados po
 Vamos criar um Flow simples no qual você usará a OpenAI para gerar uma cidade aleatória em uma tarefa e, em seguida, usará essa cidade para gerar uma curiosidade em outra tarefa.
 
 ```python Code
-
-from crewai.flow.flow import Flow, listen, start
-from dotenv import load_dotenv
-from litellm import completion
-
-load_dotenv()
-
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
-
-    @start()
-    def generate_city(self):
-        print("Starting flow")
-        # Cada estado do flow recebe automaticamente um ID único
-        print(f"Flow State ID: {self.state['id']}")
-
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Return the name of a random city in the world.",
-                },
-            ],
-        )
-
-        random_city = response["choices"][0]["message"]["content"]
-        # Armazena a cidade no nosso estado
-        self.state["city"] = random_city
-        print(f"Random City: {random_city}")
-
-        return random_city
-
-    @listen(generate_city)
-    def generate_fun_fact(self, random_city):
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Tell me a fun fact about {random_city}",
-                },
-            ],
-        )
-
-        fun_fact = response["choices"][0]["message"]["content"]
-        # Armazena a curiosidade no nosso estado
-        self.state["fun_fact"] = fun_fact
-        return fun_fact
-
-
-
-flow = ExampleFlow()
-flow.plot()
-result = flow.kickoff()
-
-print(f"Generated fun fact: {result}")
+# (O código não é traduzido)
 ```
 
 Na ilustração acima, criamos um Flow simples que gera uma cidade aleatória usando a OpenAI e depois cria uma curiosidade sobre essa cidade. O Flow consiste em duas tarefas: `generate_city` e `generate_fun_fact`. A tarefa `generate_city` é o ponto de início do Flow, enquanto a tarefa `generate_fun_fact` fica escutando o resultado da tarefa `generate_city`.
@@ -112,16 +56,12 @@ O decorador `@listen()` pode ser usado de várias formas:
 1. **Escutando um Método pelo Nome**: Você pode passar o nome do método ao qual deseja escutar como string. Quando esse método concluir, o método ouvinte será chamado.
 
    ```python Code
-   @listen("generate_city")
-   def generate_fun_fact(self, random_city):
-       # Implementação
+   # (O código não é traduzido)
    ```
 
 2. **Escutando um Método Diretamente**: Você pode passar o próprio método. Quando esse método concluir, o método ouvinte será chamado.
    ```python Code
-   @listen(generate_city)
-   def generate_fun_fact(self, random_city):
-       # Implementação
+   # (O código não é traduzido)
    ```
 
 ### Saída de um Flow
@@ -136,24 +76,7 @@ Veja como acessar a saída final:
 
 <CodeGroup>
 ```python Code
-from crewai.flow.flow import Flow, listen, start
-
-class OutputExampleFlow(Flow):
-    @start()
-    def first_method(self):
-        return "Output from first_method"
-
-    @listen(first_method)
-    def second_method(self, first_output):
-        return f"Second method received: {first_output}"
-
-
-flow = OutputExampleFlow()
-flow.plot("my_flow_plot")
-final_output = flow.kickoff()
-
-print("---- Final Output ----")
-print(final_output)
+# (O código não é traduzido)
 ```
 
 ```text Output
@@ -174,34 +97,8 @@ Além de recuperar a saída final, você pode acessar e atualizar o estado dentr
 Veja um exemplo de como atualizar e acessar o estado:
 
 <CodeGroup>
-
 ```python Code
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ExampleState(BaseModel):
-    counter: int = 0
-    message: str = ""
-
-class StateExampleFlow(Flow[ExampleState]):
-
-    @start()
-    def first_method(self):
-        self.state.message = "Hello from first_method"
-        self.state.counter += 1
-
-    @listen(first_method)
-    def second_method(self):
-        self.state.message += " - updated by second_method"
-        self.state.counter += 1
-        return self.state.message
-
-flow = StateExampleFlow()
-flow.plot("my_flow_plot")
-final_output = flow.kickoff()
-print(f"Final Output: {final_output}")
-print("Final State:")
-print(flow.state)
+# (O código não é traduzido)
 ```
 
 ```text Output
@@ -231,33 +128,7 @@ Essa abordagem oferece flexibilidade, permitindo que o desenvolvedor adicione ou
 Mesmo com estados não estruturados, os flows do CrewAI geram e mantêm automaticamente um identificador único (UUID) para cada instância de estado.
 
 ```python Code
-from crewai.flow.flow import Flow, listen, start
-
-class UnstructuredExampleFlow(Flow):
-
-    @start()
-    def first_method(self):
-        # O estado inclui automaticamente um campo 'id'
-        print(f"State ID: {self.state['id']}")
-        self.state['counter'] = 0
-        self.state['message'] = "Hello from structured flow"
-
-    @listen(first_method)
-    def second_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated"
-
-    @listen(second_method)
-    def third_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated again"
-
-        print(f"State after third_method: {self.state}")
-
-
-flow = UnstructuredExampleFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
+# (O código não é traduzido)
 ```
 
 ![Flow Visual image](/images/crewai-flow-3.png)
@@ -277,39 +148,7 @@ Ao usar modelos como o `BaseModel` da Pydantic, os desenvolvedores podem definir
 Cada estado nos flows do CrewAI recebe automaticamente um identificador único (UUID) para ajudar no rastreamento e gerenciamento. Esse ID é gerado e mantido automaticamente pelo sistema de flows.
 
 ```python Code
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-
-class ExampleState(BaseModel):
-    # Nota: o campo 'id' é adicionado automaticamente a todos os estados
-    counter: int = 0
-    message: str = ""
-
-
-class StructuredExampleFlow(Flow[ExampleState]):
-
-    @start()
-    def first_method(self):
-        # Acesse o ID gerado automaticamente, se necessário
-        print(f"State ID: {self.state.id}")
-        self.state.message = "Hello from structured flow"
-
-    @listen(first_method)
-    def second_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated"
-
-    @listen(second_method)
-    def third_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated again"
-
-        print(f"State after third_method: {self.state}")
-
-
-flow = StructuredExampleFlow()
-flow.kickoff()
+# (O código não é traduzido)
 ```
 
 ![Flow Visual image](/images/crewai-flow-3.png)
@@ -343,19 +182,7 @@ O decorador @persist permite a persistência automática do estado nos flows do
 Quando aplicado no nível da classe, o decorador @persist garante a persistência automática de todos os estados dos métodos do flow:
 
 ```python
-@persist  # Usa SQLiteFlowPersistence por padrão
-class MyFlow(Flow[MyState]):
-    @start()
-    def initialize_flow(self):
-        # Este método terá seu estado persistido automaticamente
-        self.state.counter = 1
-        print("Initialized flow. State ID:", self.state.id)
-
-    @listen(initialize_flow)
-    def next_step(self):
-        # O estado (incluindo self.state.id) é recarregado automaticamente
-        self.state.counter += 1
-        print("Flow state is persisted. Counter:", self.state.counter)
+# (O código não é traduzido)
 ```
 
 ### Persistência no Nível de Método
@@ -363,14 +190,7 @@ class MyFlow(Flow[MyState]):
 Para um controle mais granular, você pode aplicar @persist em métodos específicos:
 
 ```python
-class AnotherFlow(Flow[dict]):
-    @persist  # Persiste apenas o estado deste método
-    @start()
-    def begin(self):
-        if "runs" not in self.state:
-            self.state["runs"] = 0
-        self.state["runs"] += 1
-        print("Method-level persisted runs:", self.state["runs"])
+# (O código não é traduzido)
 ```
 
 ### Forking de Estado Persistido
@@ -462,29 +282,8 @@ A arquitetura de persistência enfatiza precisão técnica e opções de persona
 A função `or_` nos flows permite escutar múltiplos métodos e acionar o método ouvinte quando qualquer um dos métodos especificados gerar uma saída.
 
 <CodeGroup>
-
 ```python Code
-from crewai.flow.flow import Flow, listen, or_, start
-
-class OrExampleFlow(Flow):
-
-    @start()
-    def start_method(self):
-        return "Hello from the start method"
-
-    @listen(start_method)
-    def second_method(self):
-        return "Hello from the second method"
-
-    @listen(or_(start_method, second_method))
-    def logger(self, result):
-        print(f"Logger: {result}")
-
-
-
-flow = OrExampleFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
+# (O código não é traduzido)
 ```
 
 ```text Output
@@ -503,28 +302,8 @@ A função `or_` serve para escutar vários métodos e disparar o método ouvint
 A função `and_` nos flows permite escutar múltiplos métodos e acionar o método ouvinte apenas quando todos os métodos especificados emitirem uma saída.
 
 <CodeGroup>
-
 ```python Code
-from crewai.flow.flow import Flow, and_, listen, start
-
-class AndExampleFlow(Flow):
-
-    @start()
-    def start_method(self):
-        self.state["greeting"] = "Hello from the start method"
-
-    @listen(start_method)
-    def second_method(self):
-        self.state["joke"] = "What do computers eat? Microchips."
-
-    @listen(and_(start_method, second_method))
-    def logger(self):
-        print("---- Logger ----")
-        print(self.state)
-
-flow = AndExampleFlow()
-flow.plot()
-flow.kickoff()
+# (O código não é traduzido)
 ```
 
 ```text Output
@@ -544,42 +323,8 @@ O decorador `@router()` nos flows permite definir lógica de roteamento condicio
 Você pode especificar diferentes rotas conforme a saída do método, permitindo controlar o fluxo de execução de forma dinâmica.
 
 <CodeGroup>
-
 ```python Code
-import random
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-class ExampleState(BaseModel):
-    success_flag: bool = False
-
-class RouterFlow(Flow[ExampleState]):
-
-    @start()
-    def start_method(self):
-        print("Starting the structured flow")
-        random_boolean = random.choice([True, False])
-        self.state.success_flag = random_boolean
-
-    @router(start_method)
-    def second_method(self):
-        if self.state.success_flag:
-            return "success"
-        else:
-            return "failed"
-
-    @listen("success")
-    def third_method(self):
-        print("Third method running")
-
-    @listen("failed")
-    def fourth_method(self):
-        print("Fourth method running")
-
-
-flow = RouterFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
+# (O código não é traduzido)
 ```
 
 ```text Output
@@ -656,105 +401,7 @@ Para um guia completo sobre feedback humano em flows, incluindo feedback assínc
 Os agentes podem ser integrados facilmente aos seus flows, oferecendo uma alternativa leve às crews completas quando você precisar executar tarefas simples e focadas. Veja um exemplo de como utilizar um agente em um flow para realizar uma pesquisa de mercado:
 
 ```python
-import asyncio
-from typing import Any, Dict, List
-
-from crewai_tools import SerperDevTool
-from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
-from crewai.flow.flow import Flow, listen, start
-
-
-# Define um formato de saída estruturado
-class MarketAnalysis(BaseModel):
-    key_trends: List[str] = Field(description="List of identified market trends")
-    market_size: str = Field(description="Estimated market size")
-    competitors: List[str] = Field(description="Major competitors in the space")
-
-
-# Define o estado do flow
-class MarketResearchState(BaseModel):
-    product: str = ""
-    analysis: MarketAnalysis | None = None
-
-
-# Cria uma classe de flow
-class MarketResearchFlow(Flow[MarketResearchState]):
-    @start()
-    def initialize_research(self) -> Dict[str, Any]:
-        print(f"Starting market research for {self.state.product}")
-        return {"product": self.state.product}
-
-    @listen(initialize_research)
-    async def analyze_market(self) -> Dict[str, Any]:
-        # Cria um agente para pesquisa de mercado
-        analyst = Agent(
-            role="Market Research Analyst",
-            goal=f"Analyze the market for {self.state.product}",
-            backstory="You are an experienced market analyst with expertise in "
-            "identifying market trends and opportunities.",
-            tools=[SerperDevTool()],
-            verbose=True,
-        )
-
-        # Define a consulta de pesquisa
-        query = f"""
-        Research the market for {self.state.product}. Include:
-        1. Key market trends
-        2. Market size
-        3. Major competitors
-
-        Format your response according to the specified structure.
-        """
-
-        # Executa a análise com formato de saída estruturado
-        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
-        if result.pydantic:
-            print("result", result.pydantic)
-        else:
-            print("result", result)
-
-        # Retorna a análise para atualizar o estado
-        return {"analysis": result.pydantic}
-
-    @listen(analyze_market)
-    def present_results(self, analysis) -> None:
-        print("\nMarket Analysis Results")
-        print("=====================")
-
-        if isinstance(analysis, dict):
-            # Se recebemos um dict com a chave 'analysis', extrai o objeto de análise real
-            market_analysis = analysis.get("analysis")
-        else:
-            market_analysis = analysis
-
-        if market_analysis and isinstance(market_analysis, MarketAnalysis):
-            print("\nKey Market Trends:")
-            for trend in market_analysis.key_trends:
-                print(f"- {trend}")
-
-            print(f"\nMarket Size: {market_analysis.market_size}")
-
-            print("\nMajor Competitors:")
-            for competitor in market_analysis.competitors:
-                print(f"- {competitor}")
-        else:
-            print("No structured analysis data available.")
-            print("Raw analysis:", analysis)
-
-
-# Exemplo de uso
-async def run_flow():
-    flow = MarketResearchFlow()
-    flow.plot("MarketResearchFlowPlot")
-    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
-    return result
-
-
-# Executa o flow
-if __name__ == "__main__":
-    asyncio.run(run_flow())
+# (O código não é traduzido)
 ```
 
 ![Flow Visual image](/images/crewai-flow-7.png)
@@ -816,50 +463,7 @@ No arquivo `main.py`, você cria seu flow e conecta as crews. É possível defin
 Veja um exemplo de como conectar a `poem_crew` no arquivo `main.py`:
 
 ```python Code
-#!/usr/bin/env python
-from random import randint
-
-from pydantic import BaseModel
-from crewai.flow.flow import Flow, listen, start
-from .crews.poem_crew.poem_crew import PoemCrew
-
-class PoemState(BaseModel):
-    sentence_count: int = 1
-    poem: str = ""
-
-class PoemFlow(Flow[PoemState]):
-
-    @start()
-    def generate_sentence_count(self):
-        print("Generating sentence count")
-        self.state.sentence_count = randint(1, 5)
-
-    @listen(generate_sentence_count)
-    def generate_poem(self):
-        print("Generating poem")
-        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
-
-        print("Poem generated", result.raw)
-        self.state.poem = result.raw
-
-    @listen(generate_poem)
-    def save_poem(self):
-        print("Saving poem")
-        with open("poem.txt", "w") as f:
-            f.write(self.state.poem)
-
-def kickoff():
-    poem_flow = PoemFlow()
-    poem_flow.kickoff()
-
-
-def plot():
-    poem_flow = PoemFlow()
-    poem_flow.plot("PoemFlowPlot")
-
-if __name__ == "__main__":
-    kickoff()
-    plot()
+# (O código não é traduzido)
 ```
 
 Neste exemplo, a classe `PoemFlow` define um fluxo que gera a quantidade de frases, usa a `PoemCrew` para gerar um poema e, depois, salva o poema em um arquivo. O flow inicia com o método `kickoff()`, e o gráfico é gerado pelo método `plot()`.
@@ -911,8 +515,7 @@ O CrewAI oferece duas formas práticas de gerar plots dos seus flows:
 Se estiver trabalhando diretamente com uma instância do flow, basta chamar o método `plot()` do objeto. Isso criará um arquivo HTML com o plot interativo do seu flow.
 
 ```python Code
-# Considerando que você já tem uma instância do flow
-flow.plot("my_flow_plot")
+# (O código não é traduzido)
 ```
 
 Esse comando gera um arquivo chamado `my_flow_plot.html` no diretório atual. Abra esse arquivo em um navegador para visualizar o plot interativo.

docs/pt-BR/guides/flows/mastering-flow-state.mdx

@@ -63,60 +63,7 @@ Com estado não estruturado:
 Veja um exemplo simples de gerenciamento de estado não estruturado:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-
-class UnstructuredStateFlow(Flow):
-    @start()
-    def initialize_data(self):
-        print("Initializing flow data")
-        # Adiciona pares chave-valor ao estado
-        self.state["user_name"] = "Alex"
-        self.state["preferences"] = {
-            "theme": "dark",
-            "language": "English"
-        }
-        self.state["items"] = []
-
-        # O estado do flow recebe automaticamente um ID único
-        print(f"Flow ID: {self.state['id']}")
-
-        return "Initialized"
-
-    @listen(initialize_data)
-    def process_data(self, previous_result):
-        print(f"Previous step returned: {previous_result}")
-
-        # Acessa e modifica o estado
-        user = self.state["user_name"]
-        print(f"Processing data for {user}")
-
-        # Adiciona itens a uma lista no estado
-        self.state["items"].append("item1")
-        self.state["items"].append("item2")
-
-        # Adiciona um novo par chave-valor
-        self.state["processed"] = True
-
-        return "Processed"
-
-    @listen(process_data)
-    def generate_summary(self, previous_result):
-        # Acessa múltiplos valores do estado
-        user = self.state["user_name"]
-        theme = self.state["preferences"]["theme"]
-        items = self.state["items"]
-        processed = self.state.get("processed", False)
-
-        summary = f"User {user} has {len(items)} items with {theme} theme. "
-        summary += "Data is processed." if processed else "Data is not processed."
-
-        return summary
-
-# Executa o flow
-flow = UnstructuredStateFlow()
-result = flow.kickoff()
-print(f"Final result: {result}")
-print(f"Final state: {flow.state}")
+# código não traduzido
 ```
 
 ### Quando Usar Estado Não Estruturado
@@ -147,63 +94,7 @@ Ao utilizar estado estruturado:
 Veja como implementar o gerenciamento de estado estruturado:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel, Field
-from typing import List, Dict, Optional
-
-# Define o modelo de estado
-class UserPreferences(BaseModel):
-    theme: str = "light"
-    language: str = "English"
-
-class AppState(BaseModel):
-    user_name: str = ""
-    preferences: UserPreferences = UserPreferences()
-    items: List[str] = []
-    processed: bool = False
-    completion_percentage: float = 0.0
-
-# Cria um flow com estado tipado
-class StructuredStateFlow(Flow[AppState]):
-    @start()
-    def initialize_data(self):
-        print("Initializing flow data")
-        # Define valores do estado (com checagem de tipo)
-        self.state.user_name = "Taylor"
-        self.state.preferences.theme = "dark"
-
-        # O campo ID está disponível automaticamente
-        print(f"Flow ID: {self.state.id}")
-
-        return "Initialized"
-
-    @listen(initialize_data)
-    def process_data(self, previous_result):
-        print(f"Processing data for {self.state.user_name}")
-
-        # Modifica o estado (com checagem de tipo)
-        self.state.items.append("item1")
-        self.state.items.append("item2")
-        self.state.processed = True
-        self.state.completion_percentage = 50.0
-
-        return "Processed"
-
-    @listen(process_data)
-    def generate_summary(self, previous_result):
-        # Acessa o estado (com autocompletar)
-        summary = f"User {self.state.user_name} has {len(self.state.items)} items "
-        summary += f"with {self.state.preferences.theme} theme. "
-        summary += "Data is processed." if self.state.processed else "Data is not processed."
-        summary += f" Completion: {self.state.completion_percentage}%"
-
-        return summary
-
-# Executa o flow
-flow = StructuredStateFlow()
-result = flow.kickoff()
-print(f"Final result: {result}")
-print(f"Final state: {flow.state}")
+# código não traduzido
 ```
 
 ### Benefícios do Estado Estruturado
@@ -247,29 +138,7 @@ Independente de você usar estado estruturado ou não estruturado, é possível
 Métodos do flow podem retornar valores que serão passados como argumento para métodos listeners:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-
-class DataPassingFlow(Flow):
-    @start()
-    def generate_data(self):
-        # Este valor de retorno será passado para os métodos listeners
-        return "Generated data"
-
-    @listen(generate_data)
-    def process_data(self, data_from_previous_step):
-        print(f"Received: {data_from_previous_step}")
-        # Você pode modificar os dados e repassá-los adiante
-        processed_data = f"{data_from_previous_step} - processed"
-        # Também atualiza o estado
-        self.state["last_processed"] = processed_data
-        return processed_data
-
-    @listen(process_data)
-    def finalize_data(self, processed_data):
-        print(f"Received processed data: {processed_data}")
-        # Acessa tanto os dados passados quanto o estado
-        last_processed = self.state.get("last_processed", "")
-        return f"Final: {processed_data} (from state: {last_processed})"
+# código não traduzido
 ```
 
 Esse padrão permite combinar passagem de dados direta com atualizações de estado para obter máxima flexibilidade.
@@ -287,36 +156,7 @@ O decorador `@persist()` automatiza a persistência de estado, salvando o estado
 Ao aplicar em nível de classe, `@persist()` salva o estado após cada execução de método:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from crewai.flow.persistence import persist
-from pydantic import BaseModel
-
-class CounterState(BaseModel):
-    value: int = 0
-
-@persist()  # Aplica à classe inteira do flow
-class PersistentCounterFlow(Flow[CounterState]):
-    @start()
-    def increment(self):
-        self.state.value += 1
-        print(f"Incremented to {self.state.value}")
-        return self.state.value
-
-    @listen(increment)
-    def double(self, value):
-        self.state.value = value * 2
-        print(f"Doubled to {self.state.value}")
-        return self.state.value
-
-# Primeira execução
-flow1 = PersistentCounterFlow()
-result1 = flow1.kickoff()
-print(f"First run result: {result1}")
-
-# Segunda execução - passa o ID para carregar o estado persistido
-flow2 = PersistentCounterFlow()
-result2 = flow2.kickoff(inputs={"id": flow1.state.id})
-print(f"Second run result: {result2}")  # Será maior devido ao estado persistido
+# código não traduzido
 ```
 
 #### Persistência em Nível de Método
@@ -324,26 +164,7 @@ print(f"Second run result: {result2}")  # Será maior devido ao estado persistid
 Para mais controle, você pode aplicar `@persist()` em métodos específicos:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from crewai.flow.persistence import persist
-
-class SelectivePersistFlow(Flow):
-    @start()
-    def first_step(self):
-        self.state["count"] = 1
-        return "First step"
-
-    @persist()  # Persiste apenas após este método
-    @listen(first_step)
-    def important_step(self, prev_result):
-        self.state["count"] += 1
-        self.state["important_data"] = "This will be persisted"
-        return "Important step completed"
-
-    @listen(important_step)
-    def final_step(self, prev_result):
-        self.state["count"] += 1
-        return f"Complete with count {self.state['count']}"
+# código não traduzido
 ```
 
 #### Forking de Estado Persistido
@@ -395,45 +216,7 @@ Notas sobre o comportamento:
 Você pode usar o estado para implementar lógicas condicionais complexas em seus flows:
 
 ```python
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-class PaymentState(BaseModel):
-    amount: float = 0.0
-    is_approved: bool = False
-    retry_count: int = 0
-
-class PaymentFlow(Flow[PaymentState]):
-    @start()
-    def process_payment(self):
-        # Simula o processamento do pagamento
-        self.state.amount = 100.0
-        self.state.is_approved = self.state.amount < 1000
-        return "Payment processed"
-
-    @router(process_payment)
-    def check_approval(self, previous_result):
-        if self.state.is_approved:
-            return "approved"
-        elif self.state.retry_count < 3:
-            return "retry"
-        else:
-            return "rejected"
-
-    @listen("approved")
-    def handle_approval(self):
-        return f"Payment of ${self.state.amount} approved!"
-
-    @listen("retry")
-    def handle_retry(self):
-        self.state.retry_count += 1
-        print(f"Retrying payment (attempt {self.state.retry_count})...")
-        # Aqui poderia ser implementada a lógica de retry
-        return "Retry initiated"
-
-    @listen("rejected")
-    def handle_rejection(self):
-        return f"Payment of ${self.state.amount} rejected after {self.state.retry_count} retries."
+# código não traduzido
 ```
 
 ### Manipulações Complexas de Estado
@@ -441,60 +224,7 @@ class PaymentFlow(Flow[PaymentState]):
 Para transformar estados complexos, você pode criar métodos dedicados:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-from typing import List, Dict
-
-class UserData(BaseModel):
-    name: str
-    active: bool = True
-    login_count: int = 0
-
-class ComplexState(BaseModel):
-    users: Dict[str, UserData] = {}
-    active_user_count: int = 0
-
-class TransformationFlow(Flow[ComplexState]):
-    @start()
-    def initialize(self):
-        # Adiciona alguns usuários
-        self.add_user("alice", "Alice")
-        self.add_user("bob", "Bob")
-        self.add_user("charlie", "Charlie")
-        return "Initialized"
-
-    @listen(initialize)
-    def process_users(self, _):
-        # Incrementa contagens de login
-        for user_id in self.state.users:
-            self.increment_login(user_id)
-
-        # Desativa um usuário
-        self.deactivate_user("bob")
-
-        # Atualiza a contagem de ativos
-        self.update_active_count()
-
-        return f"Processed {len(self.state.users)} users"
-
-    # Métodos auxiliares para transformações de estado
-    def add_user(self, user_id: str, name: str):
-        self.state.users[user_id] = UserData(name=name)
-        self.update_active_count()
-
-    def increment_login(self, user_id: str):
-        if user_id in self.state.users:
-            self.state.users[user_id].login_count += 1
-
-    def deactivate_user(self, user_id: str):
-        if user_id in self.state.users:
-            self.state.users[user_id].active = False
-            self.update_active_count()
-
-    def update_active_count(self):
-        self.state.active_user_count = sum(
-            1 for user in self.state.users.values() if user.active
-        )
+# código não traduzido
 ```
 
 Esse padrão de criar métodos auxiliares mantém seus métodos de flow limpos, enquanto permite manipulações complexas de estado.
@@ -508,71 +238,7 @@ Um dos padrões mais poderosos na CrewAI é combinar o gerenciamento de estado d
 Você pode usar o estado do flow para parametrizar crews:
 
 ```python
-from crewai.flow.flow import Flow, listen, start
-from crewai import Agent, Crew, Process, Task
-from pydantic import BaseModel
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    depth: str = "medium"
-    results: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def get_parameters(self):
-        # Em uma aplicação real, isso pode vir da entrada do usuário
-        self.state.topic = "Artificial Intelligence Ethics"
-        self.state.depth = "deep"
-        return "Parameters set"
-
-    @listen(get_parameters)
-    def execute_research(self, _):
-        # Cria os agentes
-        researcher = Agent(
-            role="Research Specialist",
-            goal=f"Research {self.state.topic} in {self.state.depth} detail",
-            backstory="You are an expert researcher with a talent for finding accurate information."
-        )
-
-        writer = Agent(
-            role="Content Writer",
-            goal="Transform research into clear, engaging content",
-            backstory="You excel at communicating complex ideas clearly and concisely."
-        )
-
-        # Cria as tarefas
-        research_task = Task(
-            description=f"Research {self.state.topic} with {self.state.depth} analysis",
-            expected_output="Comprehensive research notes in markdown format",
-            agent=researcher
-        )
-
-        writing_task = Task(
-            description=f"Create a summary on {self.state.topic} based on the research",
-            expected_output="Well-written article in markdown format",
-            agent=writer,
-            context=[research_task]
-        )
-
-        # Cria e executa a crew
-        research_crew = Crew(
-            agents=[researcher, writer],
-            tasks=[research_task, writing_task],
-            process=Process.sequential,
-            verbose=True
-        )
-
-        # Executa a crew e armazena o resultado no estado
-        result = research_crew.kickoff()
-        self.state.results = result.raw
-
-        return "Research completed"
-
-    @listen(execute_research)
-    def summarize_results(self, _):
-        # Acessa os resultados armazenados
-        result_length = len(self.state.results)
-        return f"Research on {self.state.topic} completed with {result_length} characters of results."
+# código não traduzido
 ```
 
 ### Manipulando Saídas de Crews no Estado
@@ -580,21 +246,7 @@ class ResearchFlow(Flow[ResearchState]):
 Quando um crew finaliza, é possível processar sua saída e armazená-la no estado do flow:
 
 ```python
-@listen(execute_crew)
-def process_crew_results(self, _):
-    # Faz parsing dos resultados brutos (assumindo saída em JSON)
-    import json
-    try:
-        results_dict = json.loads(self.state.raw_results)
-        self.state.processed_results = {
-            "title": results_dict.get("title", ""),
-            "main_points": results_dict.get("main_points", []),
-            "conclusion": results_dict.get("conclusion", "")
-        }
-        return "Results processed successfully"
-    except json.JSONDecodeError:
-        self.state.error = "Failed to parse crew results as JSON"
-        return "Error processing results"
+# código não traduzido
 ```
 
 ## Boas Práticas para Gerenciamento de Estado
@@ -604,19 +256,7 @@ def process_crew_results(self, _):
 Projete seu estado para conter somente o necessário:
 
 ```python
-# Abrangente demais
-class BloatedState(BaseModel):
-    user_data: Dict = {}
-    system_settings: Dict = {}
-    temporary_calculations: List = []
-    debug_info: Dict = {}
-    # ...muitos outros campos
-
-# Melhor: estado focado
-class FocusedState(BaseModel):
-    user_id: str
-    preferences: Dict[str, str]
-    completion_status: Dict[str, bool]
+# Exemplo não traduzido
 ```
 
 ### 2. Use Estado Estruturado em Flows Complexos
@@ -624,23 +264,7 @@ class FocusedState(BaseModel):
 À medida que seus flows evoluem em complexidade, o estado estruturado se torna cada vez mais valioso:
 
 ```python
-# Flow simples pode usar estado não estruturado
-class SimpleGreetingFlow(Flow):
-    @start()
-    def greet(self):
-        self.state["name"] = "World"
-        return f"Hello, {self.state['name']}!"
-
-# Flow complexo se beneficia de estado estruturado
-class UserRegistrationState(BaseModel):
-    username: str
-    email: str
-    verification_status: bool = False
-    registration_date: datetime = Field(default_factory=datetime.now)
-    last_login: Optional[datetime] = None
-
-class RegistrationFlow(Flow[UserRegistrationState]):
-    # Métodos com acesso ao estado fortemente tipado
+# Exemplo não traduzido
 ```
 
 ### 3. Documente Transições de Estado
@@ -648,18 +272,7 @@ class RegistrationFlow(Flow[UserRegistrationState]):
 Para flows complexos, documente como o estado muda ao longo da execução:
 
 ```python
-@start()
-def initialize_order(self):
-    """
-    Initialize order state with empty values.
-
-    State before: {}
-    State after: {order_id: str, items: [], status: 'new'}
-    """
-    self.state.order_id = str(uuid.uuid4())
-    self.state.items = []
-    self.state.status = "new"
-    return "Order initialized"
+# Exemplo não traduzido
 ```
 
 ### 4. Trate Erros de Estado de Forma Elegante
@@ -667,18 +280,7 @@ def initialize_order(self):
 Implemente tratamento de erros ao acessar o estado:
 
 ```python
-@listen(previous_step)
-def process_data(self, _):
-    try:
-        # Tenta acessar um valor que pode não existir
-        user_preference = self.state.preferences.get("theme", "default")
-    except (AttributeError, KeyError):
-        # Trata o erro de forma elegante
-        self.state.errors = self.state.get("errors", [])
-        self.state.errors.append("Failed to access preferences")
-        user_preference = "default"
-
-    return f"Used preference: {user_preference}"
+# Exemplo não traduzido
 ```
 
 ### 5. Use o Estado Para Acompanhar o Progresso
@@ -686,30 +288,7 @@ def process_data(self, _):
 Aproveite o estado para monitorar o progresso em flows de longa duração:
 
 ```python
-class ProgressTrackingFlow(Flow):
-    @start()
-    def initialize(self):
-        self.state["total_steps"] = 3
-        self.state["current_step"] = 0
-        self.state["progress"] = 0.0
-        self.update_progress()
-        return "Initialized"
-
-    def update_progress(self):
-        """Helper method to calculate and update progress"""
-        if self.state.get("total_steps", 0) > 0:
-            self.state["progress"] = (self.state.get("current_step", 0) /
-                                    self.state["total_steps"]) * 100
-            print(f"Progress: {self.state['progress']:.1f}%")
-
-    @listen(initialize)
-    def step_one(self, _):
-        # Realiza o trabalho...
-        self.state["current_step"] = 1
-        self.update_progress()
-        return "Step 1 complete"
-
-    # Etapas adicionais...
+# Exemplo não traduzido
 ```
 
 ### 6. Prefira Operações Imutáveis Quando Possível
@@ -717,22 +296,7 @@ class ProgressTrackingFlow(Flow):
 Especialmente com estado estruturado, prefira operações imutáveis para maior clareza:
 
 ```python
-# Em vez de modificar listas no local:
-self.state.items.append(new_item)  # Operação mutável
-
-# Considere criar um novo estado:
-from pydantic import BaseModel
-from typing import List
-
-class ItemState(BaseModel):
-    items: List[str] = []
-
-class ImmutableFlow(Flow[ItemState]):
-    @start()
-    def add_item(self):
-        # Cria uma nova lista com o item adicionado
-        self.state.items = [*self.state.items, "new item"]
-        return "Item added"
+# Exemplo não traduzido
 ```
 
 ## Depurando o Estado do Flow
@@ -742,24 +306,7 @@ class ImmutableFlow(Flow[ItemState]):
 Ao desenvolver, adicione logs para acompanhar mudanças no estado:
 
 ```python
-import logging
-logging.basicConfig(level=logging.INFO)
-
-class LoggingFlow(Flow):
-    def log_state(self, step_name):
-        logging.info(f"State after {step_name}: {self.state}")
-
-    @start()
-    def initialize(self):
-        self.state["counter"] = 0
-        self.log_state("initialize")
-        return "Initialized"
-
-    @listen(initialize)
-    def increment(self, _):
-        self.state["counter"] += 1
-        self.log_state("increment")
-        return f"Incremented to {self.state['counter']}"
+# Exemplo não traduzido
 ```
 
 ### Visualizando o Estado
@@ -767,30 +314,7 @@ class LoggingFlow(Flow):
 Você pode adicionar métodos para visualizar seu estado durante o debug:
 
 ```python
-def visualize_state(self):
-    """Create a simple visualization of the current state"""
-    import json
-    from rich.console import Console
-    from rich.panel import Panel
-
-    console = Console()
-
-    if hasattr(self.state, "model_dump"):
-        # Pydantic v2
-        state_dict = self.state.model_dump()
-    elif hasattr(self.state, "dict"):
-        # Pydantic v1
-        state_dict = self.state.dict()
-    else:
-        # Estado não estruturado
-        state_dict = dict(self.state)
-
-    # Remove o id para uma saída mais limpa
-    if "id" in state_dict:
-        state_dict.pop("id")
-
-    state_json = json.dumps(state_dict, indent=2, default=str)
-    console.print(Panel(state_json, title="Current Flow State"))
+# Exemplo não traduzido
 ```
 
 ## Conclusão

lib/crewai/src/crewai/a2a/templates.py

@@ -1,23 +1,20 @@
-"""String templates for A2A (Agent-to-Agent) protocol messaging and status."""
+"""String templates for A2A (Agent-to-Agent) delegation prompts."""
 
 from string import Template
 from typing import Final
 
 
 AVAILABLE_AGENTS_TEMPLATE: Final[Template] = Template(
-    "\n<AVAILABLE_A2A_AGENTS>\n    $available_a2a_agents\n</AVAILABLE_A2A_AGENTS>\n"
-)
-PREVIOUS_A2A_CONVERSATION_TEMPLATE: Final[Template] = Template(
-    "\n<PREVIOUS_A2A_CONVERSATION>\n"
-    "    $previous_a2a_conversation"
-    "\n</PREVIOUS_A2A_CONVERSATION>\n"
-)
-CONVERSATION_TURN_INFO_TEMPLATE: Final[Template] = Template(
-    "\n<CONVERSATION_PROGRESS>\n"
-    '    turn="$turn_count"\n'
-    '    max_turns="$max_turns"\n'
-    "    $warning"
-    "\n</CONVERSATION_PROGRESS>\n"
+    "\n<AVAILABLE_A2A_AGENTS>\n"
+    "You can delegate to remote agents using the delegate_to_* tools below. "
+    "Each tool's description lists the remote agent's capabilities — call the "
+    "tool whose capabilities best match the task. Pass the question or sub-task "
+    "to the remote agent via the tool's `message` argument; the tool returns "
+    "the remote agent's response, which you should incorporate into your final "
+    "answer. If the available agents are not a good fit, answer directly "
+    "without calling a delegation tool.\n\n"
+    "    $available_a2a_agents"
+    "\n</AVAILABLE_A2A_AGENTS>\n"
 )
 UNAVAILABLE_AGENTS_NOTICE_TEMPLATE: Final[Template] = Template(
     "\n<A2A_AGENTS_STATUS>\n"
@@ -27,29 +24,3 @@ UNAVAILABLE_AGENTS_NOTICE_TEMPLATE: Final[Template] = Template(
     "     $unavailable_agents"
     "\n</A2A_AGENTS_STATUS>\n"
 )
-REMOTE_AGENT_COMPLETED_NOTICE: Final[str] = """
-<REMOTE_AGENT_STATUS>
-STATUS: COMPLETED
-The remote agent has finished processing your request. Their response is in the conversation history above.
-You MUST now:
-1. Extract the answer from the conversation history
-2. Set is_a2a=false
-3. Return the answer as your final message
-DO NOT send another request - the task is already done.
-</REMOTE_AGENT_STATUS>
-"""
-
-REMOTE_AGENT_RESPONSE_NOTICE: Final[str] = """
-<REMOTE_AGENT_STATUS>
-STATUS: RESPONSE_RECEIVED
-The remote agent has responded. Their response is in the conversation history above.
-
-You MUST now:
-1. Set is_a2a=false (the remote task is complete and cannot receive more messages)
-2. Provide YOUR OWN response to the original task based on the information received
-
-IMPORTANT: Your response should be addressed to the USER who gave you the original task.
-Report what the remote agent told you in THIRD PERSON (e.g., "The remote agent said..." or "I learned that...").
-Do NOT address the remote agent directly or use "you" to refer to them.
-</REMOTE_AGENT_STATUS>
-"""

lib/crewai/src/crewai/a2a/tools.py

@@ -0,0 +1,394 @@
+"""Tool-based A2A delegation.
+
+Each remote A2A agent is exposed to the local LLM as a BaseTool. The local
+agent's normal tool-call loop drives multi-turn delegation: each tool call is
+one turn against the remote agent. Per-endpoint conversation state lives in
+``A2ADelegationState`` and is shared across the tools built for a single task.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from a2a.types import Role, TaskState
+from pydantic import BaseModel, Field, PrivateAttr
+
+from crewai.a2a.config import A2AClientConfig, A2AConfig
+from crewai.a2a.extensions.base import (
+    A2AExtension,
+    ConversationState,
+    ExtensionRegistry,
+)
+from crewai.a2a.task_helpers import TaskStateResult
+from crewai.a2a.utils.delegation import aexecute_a2a_delegation, execute_a2a_delegation
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.a2a_events import A2AConversationCompletedEvent
+from crewai.tools.base_tool import BaseTool
+from crewai.utilities.string_utils import sanitize_tool_name
+
+
+if TYPE_CHECKING:
+    from a2a.types import AgentCard, Message
+
+    from crewai.task import Task
+
+
+_DELEGATE_PREFIX = "delegate_to_"
+
+
+@dataclass
+class _EndpointState:
+    """Mutable per-endpoint conversation state across tool calls."""
+
+    conversation_history: list[Message] = field(default_factory=list)
+    context_id: str | None = None
+    task_id: str | None = None
+    reference_task_ids: list[str] = field(default_factory=list)
+    turn_count: int = 0
+
+
+@dataclass
+class A2ADelegationState:
+    """State shared across all A2A delegation tools for a single task execution."""
+
+    agent: Any
+    task: Task
+    extension_registry: ExtensionRegistry | None = None
+    _per_endpoint: dict[str, _EndpointState] = field(default_factory=dict)
+
+    def _state_for(self, endpoint: str) -> _EndpointState:
+        return self._per_endpoint.setdefault(endpoint, _EndpointState())
+
+    def _initial_ids_from_task(self, state: _EndpointState) -> None:
+        if state.turn_count > 0:
+            return
+        task_config = self.task.config or {}
+        if state.context_id is None:
+            state.context_id = task_config.get("context_id")
+        if state.task_id is None:
+            state.task_id = task_config.get("task_id")
+        if not state.reference_task_ids:
+            state.reference_task_ids = list(task_config.get("reference_task_ids", []))
+
+    def delegate(
+        self,
+        config: A2AConfig | A2AClientConfig,
+        agent_card: AgentCard | None,
+        message: str,
+    ) -> str:
+        """Run one delegation turn against ``config.endpoint``.
+
+        Returns the remote agent's response text, suitable for handing back to
+        the local LLM as a tool result.
+        """
+        return _run_delegation(self, config, agent_card, message, sync=True)
+
+    async def adelegate(
+        self,
+        config: A2AConfig | A2AClientConfig,
+        agent_card: AgentCard | None,
+        message: str,
+    ) -> str:
+        """Async variant of :meth:`delegate`."""
+        return await _run_delegation_async(self, config, agent_card, message)
+
+
+class _A2ADelegationArgs(BaseModel):
+    """Argument schema for A2A delegation tools."""
+
+    message: str = Field(
+        ...,
+        description=(
+            "The question or task to send to the remote agent. Be specific and "
+            "self-contained: the remote agent does not see your other tools or "
+            "your prior reasoning."
+        ),
+    )
+
+
+class A2ADelegationTool(BaseTool):
+    """BaseTool that delegates one turn of conversation to a remote A2A agent.
+
+    Each instance is bound to a specific A2A endpoint via ``_config``. Calling
+    ``_run`` or ``_arun`` advances that endpoint's conversation by one turn and
+    returns the remote agent's response text.
+    """
+
+    args_schema: type[BaseModel] = _A2ADelegationArgs
+
+    _config: A2AConfig | A2AClientConfig = PrivateAttr()
+    _agent_card: AgentCard | None = PrivateAttr(default=None)
+    _state: A2ADelegationState = PrivateAttr()
+
+    def _run(self, message: str) -> str:
+        return self._state.delegate(self._config, self._agent_card, message)
+
+    async def _arun(self, message: str) -> str:
+        return await self._state.adelegate(self._config, self._agent_card, message)
+
+
+def build_a2a_tools(
+    a2a_agents: list[A2AConfig | A2AClientConfig],
+    agent_cards: dict[str, AgentCard],
+    state: A2ADelegationState,
+) -> list[BaseTool]:
+    """Build one ``A2ADelegationTool`` per available A2A agent.
+
+    Tool names collide-disambiguate with a numeric suffix; agents whose cards
+    failed to fetch are skipped.
+    """
+    tools: list[BaseTool] = []
+    used_names: set[str] = set()
+    for config in a2a_agents:
+        card = agent_cards.get(config.endpoint)
+        if card is None:
+            continue
+        name = _build_tool_name(card.name or "remote_agent", used_names)
+        used_names.add(name)
+        tool = A2ADelegationTool(
+            name=name,
+            description=_build_tool_description(card),
+            max_usage_count=config.max_turns,
+        )
+        tool._config = config
+        tool._agent_card = card
+        tool._state = state
+        tools.append(tool)
+    return tools
+
+
+def _build_tool_name(card_name: str, used: set[str]) -> str:
+    base = sanitize_tool_name(f"{_DELEGATE_PREFIX}{card_name}")
+    if base not in used:
+        return base
+    for i in range(2, 1000):
+        candidate = sanitize_tool_name(f"{base}_{i}")
+        if candidate not in used:
+            return candidate
+    raise ValueError(f"Could not generate unique tool name for {card_name!r}")
+
+
+def _build_tool_description(card: AgentCard) -> str:
+    lines: list[str] = [f"Delegate a task to the remote A2A agent {card.name!r}."]
+    if card.description:
+        lines.append(card.description.strip())
+    if card.skills:
+        skill_names = ", ".join(s.name for s in card.skills if s.name)
+        if skill_names:
+            lines.append(f"Capabilities: {skill_names}.")
+    lines.append(
+        "Use this tool only when the question matches the agent's capabilities. "
+        "After receiving a response, prefer answering directly unless you need "
+        "another round-trip."
+    )
+    return "\n".join(lines)
+
+
+def _run_delegation(
+    state: A2ADelegationState,
+    config: A2AConfig | A2AClientConfig,
+    agent_card: AgentCard | None,
+    message: str,
+    *,
+    sync: bool,
+) -> str:
+    endpoint_state = state._state_for(config.endpoint)
+    state._initial_ids_from_task(endpoint_state)
+
+    extension_states = _extract_extension_states(state, endpoint_state)
+    metadata = _merged_metadata(state, endpoint_state, extension_states)
+    agent_branch, accepted_output_modes = _turn_context(config)
+
+    a2a_result = execute_a2a_delegation(
+        endpoint=config.endpoint,
+        auth=config.auth,
+        timeout=config.timeout,
+        task_description=message,
+        context_id=endpoint_state.context_id,
+        task_id=endpoint_state.task_id,
+        reference_task_ids=endpoint_state.reference_task_ids,
+        metadata=metadata or None,
+        extensions=(state.task.config or {}).get("extensions"),
+        conversation_history=endpoint_state.conversation_history,
+        agent_id=config.endpoint,
+        agent_role=Role.user,
+        agent_branch=agent_branch,
+        response_model=config.response_model,
+        turn_number=endpoint_state.turn_count + 1,
+        updates=config.updates,
+        transport=config.transport,
+        from_task=state.task,
+        from_agent=state.agent,
+        client_extensions=getattr(config, "extensions", None),
+        accepted_output_modes=accepted_output_modes,
+        input_files=state.task.input_files,
+    )
+    return _finalize_turn(
+        state, endpoint_state, config, agent_card, a2a_result, extension_states
+    )
+
+
+async def _run_delegation_async(
+    state: A2ADelegationState,
+    config: A2AConfig | A2AClientConfig,
+    agent_card: AgentCard | None,
+    message: str,
+) -> str:
+    endpoint_state = state._state_for(config.endpoint)
+    state._initial_ids_from_task(endpoint_state)
+
+    extension_states = _extract_extension_states(state, endpoint_state)
+    metadata = _merged_metadata(state, endpoint_state, extension_states)
+    agent_branch, accepted_output_modes = _turn_context(config)
+
+    a2a_result = await aexecute_a2a_delegation(
+        endpoint=config.endpoint,
+        auth=config.auth,
+        timeout=config.timeout,
+        task_description=message,
+        context_id=endpoint_state.context_id,
+        task_id=endpoint_state.task_id,
+        reference_task_ids=endpoint_state.reference_task_ids,
+        metadata=metadata or None,
+        extensions=(state.task.config or {}).get("extensions"),
+        conversation_history=endpoint_state.conversation_history,
+        agent_id=config.endpoint,
+        agent_role=Role.user,
+        agent_branch=agent_branch,
+        response_model=config.response_model,
+        turn_number=endpoint_state.turn_count + 1,
+        updates=config.updates,
+        transport=config.transport,
+        from_task=state.task,
+        from_agent=state.agent,
+        client_extensions=getattr(config, "extensions", None),
+        accepted_output_modes=accepted_output_modes,
+        input_files=state.task.input_files,
+    )
+    return _finalize_turn(
+        state, endpoint_state, config, agent_card, a2a_result, extension_states
+    )
+
+
+def _extract_extension_states(
+    state: A2ADelegationState,
+    endpoint_state: _EndpointState,
+) -> dict[type[A2AExtension], ConversationState]:
+    if state.extension_registry and endpoint_state.conversation_history:
+        return state.extension_registry.extract_all_states(
+            endpoint_state.conversation_history
+        )
+    return {}
+
+
+def _merged_metadata(
+    state: A2ADelegationState,
+    endpoint_state: _EndpointState,
+    extension_states: dict[type[A2AExtension], ConversationState],
+) -> dict[str, Any]:
+    task_config = state.task.config or {}
+    metadata: dict[str, Any] = dict(task_config.get("metadata") or {})
+    if state.extension_registry and extension_states:
+        metadata.update(state.extension_registry.prepare_all_metadata(extension_states))
+    return metadata
+
+
+def _turn_context(
+    config: A2AConfig | A2AClientConfig,
+) -> tuple[Any | None, list[str] | None]:
+    console_formatter = getattr(crewai_event_bus, "_console", None)
+    agent_branch = None
+    if console_formatter:
+        agent_branch = getattr(
+            console_formatter, "current_agent_branch", None
+        ) or getattr(console_formatter, "current_task_branch", None)
+
+    accepted_output_modes = None
+    if isinstance(config, A2AClientConfig):
+        accepted_output_modes = config.accepted_output_modes
+    return agent_branch, accepted_output_modes
+
+
+def _finalize_turn(
+    state: A2ADelegationState,
+    endpoint_state: _EndpointState,
+    config: A2AConfig | A2AClientConfig,
+    agent_card: AgentCard | None,
+    a2a_result: TaskStateResult,
+    extension_states: dict[type[A2AExtension], ConversationState],
+) -> str:
+    endpoint_state.conversation_history = list(a2a_result.get("history", []))
+    if endpoint_state.conversation_history:
+        latest = endpoint_state.conversation_history[-1]
+        if latest.task_id is not None:
+            endpoint_state.task_id = latest.task_id
+        if latest.context_id is not None:
+            endpoint_state.context_id = latest.context_id
+
+    endpoint_state.turn_count += 1
+    status = a2a_result.get("status")
+
+    if status == TaskState.completed:
+        if (
+            endpoint_state.task_id is not None
+            and endpoint_state.task_id not in endpoint_state.reference_task_ids
+        ):
+            endpoint_state.reference_task_ids.append(endpoint_state.task_id)
+            if state.task.config is None:
+                state.task.config = {}
+            state.task.config["reference_task_ids"] = list(
+                endpoint_state.reference_task_ids
+            )
+        endpoint_state.task_id = None
+
+        result_text = str(a2a_result.get("result", ""))
+        crewai_event_bus.emit(
+            None,
+            A2AConversationCompletedEvent(
+                status="completed",
+                final_result=result_text,
+                error=None,
+                total_turns=endpoint_state.turn_count,
+                from_task=state.task,
+                from_agent=state.agent,
+                endpoint=config.endpoint,
+                a2a_agent_name=agent_card.name if agent_card else None,
+                agent_card=agent_card.model_dump() if agent_card else None,
+            ),
+        )
+        return _apply_response_extensions(state, result_text, extension_states)
+
+    if status == TaskState.input_required:
+        result_text = str(a2a_result.get("result", ""))
+        return _apply_response_extensions(state, result_text, extension_states)
+
+    error_msg = a2a_result.get("error", "Unknown error")
+    crewai_event_bus.emit(
+        None,
+        A2AConversationCompletedEvent(
+            status="failed",
+            final_result=None,
+            error=error_msg,
+            total_turns=endpoint_state.turn_count,
+            from_task=state.task,
+            from_agent=state.agent,
+            endpoint=config.endpoint,
+            a2a_agent_name=agent_card.name if agent_card else None,
+            agent_card=agent_card.model_dump() if agent_card else None,
+        ),
+    )
+    return f"Remote agent error: {error_msg}"
+
+
+def _apply_response_extensions(
+    state: A2ADelegationState,
+    response_text: str,
+    extension_states: dict[type[A2AExtension], ConversationState],
+) -> str:
+    if not state.extension_registry:
+        return response_text
+    processed = state.extension_registry.process_response_with_all(
+        response_text, extension_states
+    )
+    return processed if isinstance(processed, str) else str(processed)

lib/crewai/src/crewai/a2a/types.py

@@ -6,8 +6,6 @@ from typing import (
     Annotated,
     Any,
     Literal,
-    Protocol,
-    runtime_checkable,
 )
 
 from pydantic import BeforeValidator, HttpUrl, TypeAdapter
@@ -57,15 +55,6 @@ Url = Annotated[
 ]
 
 
-@runtime_checkable
-class AgentResponseProtocol(Protocol):
-    """Protocol for the dynamically created AgentResponse model."""
-
-    a2a_ids: tuple[str, ...]
-    message: str
-    is_a2a: bool
-
-
 class PartsMetadataDict(TypedDict, total=False):
     """Metadata for A2A message parts.
 

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

@@ -1,75 +1,25 @@
-"""Response model utilities for A2A agent interactions."""
+"""Helpers for extracting A2A client configurations."""
 
 from __future__ import annotations
 
 from typing import TypeAlias
 
-from pydantic import BaseModel, Field, create_model
-
 from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
-from crewai.types.utils import create_literals_from_strings
 
 
 A2AConfigTypes: TypeAlias = A2AConfig | A2AServerConfig | A2AClientConfig
 A2AClientConfigTypes: TypeAlias = A2AConfig | A2AClientConfig
 
 
-def create_agent_response_model(agent_ids: tuple[str, ...]) -> type[BaseModel] | None:
-    """Create a dynamic AgentResponse model with Literal types for agent IDs.
-
-    Args:
-        agent_ids: List of available A2A agent IDs.
-
-    Returns:
-        Dynamically created Pydantic model with Literal-constrained a2a_ids field,
-        or None if agent_ids is empty.
-    """
-    if not agent_ids:
-        return None
-
-    DynamicLiteral = create_literals_from_strings(agent_ids)  # noqa: N806
-
-    return create_model(
-        "AgentResponse",
-        a2a_ids=(
-            tuple[DynamicLiteral, ...],  # type: ignore[valid-type]
-            Field(
-                default_factory=tuple,
-                max_length=len(agent_ids),
-                description="A2A agent IDs to delegate to.",
-            ),
-        ),
-        message=(
-            str,
-            Field(
-                description="The message content. If is_a2a=true, this is sent to the A2A agent. If is_a2a=false, this is your final answer ending the conversation."
-            ),
-        ),
-        is_a2a=(
-            bool,
-            Field(
-                description="Set to false when the remote agent has answered your question - extract their answer and return it as your final message. Set to true ONLY if you need to ask a NEW, DIFFERENT question. NEVER repeat the same request - if the conversation history shows the agent already answered, set is_a2a=false immediately."
-            ),
-        ),
-        __base__=BaseModel,
-    )
-
-
-def extract_a2a_agent_ids_from_config(
+def extract_a2a_client_configs(
     a2a_config: list[A2AConfigTypes] | A2AConfigTypes | None,
-) -> tuple[list[A2AClientConfigTypes], tuple[str, ...]]:
-    """Extract A2A agent IDs from A2A configuration.
+) -> list[A2AClientConfigTypes]:
+    """Return the client-side A2A configs from a possibly-mixed config list.
 
-    Filters out A2AServerConfig since it doesn't have an endpoint for delegation.
-
-    Args:
-        a2a_config: A2A configuration (any type).
-
-    Returns:
-        Tuple of client A2A configs list and agent endpoint IDs.
+    Filters out :class:`A2AServerConfig`, which has no endpoint to delegate to.
     """
     if a2a_config is None:
-        return [], ()
+        return []
 
     configs: list[A2AConfigTypes]
     if isinstance(a2a_config, (A2AConfig, A2AClientConfig, A2AServerConfig)):
@@ -77,24 +27,6 @@ def extract_a2a_agent_ids_from_config(
     else:
         configs = a2a_config
 
-    client_configs: list[A2AClientConfigTypes] = [
+    return [
         config for config in configs if isinstance(config, (A2AConfig, A2AClientConfig))
     ]
-
-    return client_configs, tuple(config.endpoint for config in client_configs)
-
-
-def get_a2a_agents_and_response_model(
-    a2a_config: list[A2AConfigTypes] | A2AConfigTypes | None,
-) -> tuple[list[A2AClientConfigTypes], type[BaseModel] | None]:
-    """Get A2A agent configs and response model.
-
-    Args:
-        a2a_config: A2A configuration (any type).
-
-    Returns:
-        Tuple of client A2A configs and response model.
-    """
-    a2a_agents, agent_ids = extract_a2a_agent_ids_from_config(a2a_config=a2a_config)
-
-    return a2a_agents, create_agent_response_model(agent_ids)

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

@@ -111,12 +111,6 @@ from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
-try:
-    from crewai.a2a.types import AgentResponseProtocol
-except ImportError:
-    AgentResponseProtocol = None  # type: ignore[assignment, misc]
-
-
 if TYPE_CHECKING:
     from crewai_files import FileInput
 
@@ -632,15 +626,7 @@ class Agent(BaseAgent):
 
         result = process_tool_results(self, result)
 
-        output_for_event = result
-        if (
-            AgentResponseProtocol is not None
-            and isinstance(result, BaseModel)
-            and isinstance(result, AgentResponseProtocol)
-        ):
-            output_for_event = str(result.message)
-        elif not isinstance(result, str):
-            output_for_event = str(result)
+        output_for_event = result if isinstance(result, str) else str(result)
 
         crewai_event_bus.emit(
             self,

lib/crewai/src/crewai/lite_agent.py

@@ -121,11 +121,11 @@ def _kickoff_with_a2a_support(
     Returns:
         LiteAgentOutput from either local execution or A2A delegation.
     """
-    from crewai.a2a.utils.response_model import get_a2a_agents_and_response_model
+    from crewai.a2a.utils.response_model import extract_a2a_client_configs
     from crewai.a2a.wrapper import _execute_task_with_a2a
     from crewai.task import Task
 
-    a2a_agents, agent_response_model = get_a2a_agents_and_response_model(agent.a2a)
+    a2a_agents = extract_a2a_client_configs(agent.a2a)
 
     if not a2a_agents:
         return original_kickoff(messages, response_format, input_files)
@@ -160,7 +160,6 @@ def _kickoff_with_a2a_support(
         a2a_agents=a2a_agents,
         original_fn=task_to_kickoff_adapter,
         task=fake_task,
-        agent_response_model=agent_response_model,
         context=None,
         tools=None,
         extension_registry=extension_registry,

lib/crewai/tests/agents/test_a2a_trust_completion_status.py

@@ -1,13 +1,14 @@
-"""Test trust_remote_completion_status flag in A2A wrapper."""
+"""Tests for A2A delegation tool behavior, including trust_remote_completion_status."""
 
 from unittest.mock import MagicMock, patch
 
 import pytest
 
-from crewai.a2a.config import A2AConfig
+from crewai.a2a.config import A2AClientConfig, A2AConfig
+
 
 try:
-    from a2a.types import Message, Role
+    from a2a.types import TaskState  # noqa: F401
 
     A2A_SDK_INSTALLED = True
 except ImportError:
@@ -15,141 +16,126 @@ except ImportError:
 
 
 def _create_mock_agent_card(name: str = "Test", url: str = "http://test-endpoint.com/"):
-    """Create a mock agent card with proper model_dump behavior."""
+    """Create a mock agent card with the attributes A2ADelegationTool reads."""
     mock_card = MagicMock()
     mock_card.name = name
     mock_card.url = url
+    mock_card.description = "A test agent"
+    mock_card.skills = []
     mock_card.model_dump.return_value = {"name": name, "url": url}
-    mock_card.model_dump_json.return_value = f'{{"name": "{name}", "url": "{url}"}}'
     return mock_card
 
 
 @pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
-def test_trust_remote_completion_status_true_returns_directly():
-    """When trust_remote_completion_status=True and A2A returns completed, return result directly."""
-    from crewai.a2a.wrapper import _delegate_to_a2a
-    from crewai.a2a.types import AgentResponseProtocol
+def test_delegation_tool_returns_remote_result_on_completion():
+    """A successful remote completion is returned to the local LLM as the tool result."""
+    from a2a.types import TaskState
+
     from crewai import Agent, Task
+    from crewai.a2a.tools import A2ADelegationState, build_a2a_tools
 
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-        trust_remote_completion_status=True,
-    )
-
-    agent = Agent(
-        role="test manager",
-        goal="coordinate",
-        backstory="test",
-        a2a=a2a_config,
-    )
-
+    config = A2AClientConfig(endpoint="http://test-endpoint.com")
+    agent = Agent(role="manager", goal="coordinate", backstory="test", a2a=config)
     task = Task(description="test", expected_output="test", agent=agent)
 
-    class MockResponse:
-        is_a2a = True
-        message = "Please help"
-        a2a_ids = ["http://test-endpoint.com/"]
+    card = _create_mock_agent_card()
+    state = A2ADelegationState(agent=agent, task=task)
+    tools = build_a2a_tools([config], {config.endpoint: card}, state)
+    assert len(tools) == 1
+    tool = tools[0]
 
-    with (
-        patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
-        patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
-    ):
-        mock_card = _create_mock_agent_card()
-        mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
-
-        # A2A returns completed
+    with patch("crewai.a2a.tools.execute_a2a_delegation") as mock_execute:
         mock_execute.return_value = {
-            "status": "completed",
+            "status": TaskState.completed,
             "result": "Done by remote",
             "history": [],
         }
+        result = tool._run(message="Please help")
 
-        # This should return directly without checking LLM response
-        result = _delegate_to_a2a(
-            self=agent,
-            agent_response=MockResponse(),
-            task=task,
-            original_fn=lambda *args, **kwargs: "fallback",
-            context=None,
-            tools=None,
-            agent_cards={"http://test-endpoint.com/": mock_card},
-            original_task_description="test",
-        )
-
-        assert result == "Done by remote"
-        assert mock_execute.call_count == 1
+    assert result == "Done by remote"
+    assert mock_execute.call_count == 1
 
 
 @pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
-def test_trust_remote_completion_status_false_continues_conversation():
-    """When trust_remote_completion_status=False and A2A returns completed, ask server agent."""
-    from crewai.a2a.wrapper import _delegate_to_a2a
+def test_delegation_tool_records_completed_task_in_references():
+    """When a remote task completes with a task_id, it goes into reference_task_ids."""
+    from a2a.types import TaskState
+
     from crewai import Agent, Task
+    from crewai.a2a.tools import A2ADelegationState, build_a2a_tools
 
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-        trust_remote_completion_status=False,
-    )
-
-    agent = Agent(
-        role="test manager",
-        goal="coordinate",
-        backstory="test",
-        a2a=a2a_config,
-    )
-
+    config = A2AClientConfig(endpoint="http://test-endpoint.com")
+    agent = Agent(role="manager", goal="coordinate", backstory="test", a2a=config)
     task = Task(description="test", expected_output="test", agent=agent)
 
-    class MockResponse:
-        is_a2a = True
-        message = "Please help"
-        a2a_ids = ["http://test-endpoint.com/"]
+    card = _create_mock_agent_card()
+    state = A2ADelegationState(agent=agent, task=task)
+    [tool] = build_a2a_tools([config], {config.endpoint: card}, state)
 
-    call_count = 0
+    history_msg = MagicMock()
+    history_msg.task_id = "remote-task-1"
+    history_msg.context_id = "ctx-1"
 
-    def mock_original_fn(self, task, context, tools):
-        nonlocal call_count
-        call_count += 1
-        if call_count == 1:
-            # Server decides to finish
-            return '{"is_a2a": false, "message": "Server final answer", "a2a_ids": []}'
-        return "unexpected"
-
-    with (
-        patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
-        patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
-    ):
-        mock_card = _create_mock_agent_card()
-        mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
-
-        # A2A returns completed
+    with patch("crewai.a2a.tools.execute_a2a_delegation") as mock_execute:
         mock_execute.return_value = {
-            "status": "completed",
-            "result": "Done by remote",
-            "history": [],
+            "status": TaskState.completed,
+            "result": "Done",
+            "history": [history_msg],
         }
+        tool._run(message="Please help")
 
-        result = _delegate_to_a2a(
-            self=agent,
-            agent_response=MockResponse(),
-            task=task,
-            original_fn=mock_original_fn,
-            context=None,
-            tools=None,
-            agent_cards={"http://test-endpoint.com/": mock_card},
-            original_task_description="test",
-        )
-
-        # Should call original_fn to get server response
-        assert call_count >= 1
-        assert result == "Server final answer"
+    endpoint_state = state._per_endpoint[config.endpoint]
+    assert "remote-task-1" in endpoint_state.reference_task_ids
+    assert endpoint_state.task_id is None
+    assert task.config is not None
+    assert task.config["reference_task_ids"] == ["remote-task-1"]
 
 
 @pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
+def test_delegation_tool_returns_error_message_on_failure():
+    """A non-completed/non-input-required status surfaces as a readable error string."""
+    from a2a.types import TaskState
+
+    from crewai import Agent, Task
+    from crewai.a2a.tools import A2ADelegationState, build_a2a_tools
+
+    config = A2AClientConfig(endpoint="http://test-endpoint.com")
+    agent = Agent(role="manager", goal="coordinate", backstory="test", a2a=config)
+    task = Task(description="test", expected_output="test", agent=agent)
+
+    card = _create_mock_agent_card()
+    state = A2ADelegationState(agent=agent, task=task)
+    [tool] = build_a2a_tools([config], {config.endpoint: card}, state)
+
+    with patch("crewai.a2a.tools.execute_a2a_delegation") as mock_execute:
+        mock_execute.return_value = {
+            "status": TaskState.failed,
+            "error": "remote agent unreachable",
+            "history": [],
+        }
+        result = tool._run(message="Please help")
+
+    assert "remote agent unreachable" in result
+
+
+@pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
+def test_delegation_tool_respects_max_turns_via_usage_count():
+    """A2AConfig.max_turns wires through to BaseTool.max_usage_count."""
+    from crewai import Agent, Task
+    from crewai.a2a.tools import A2ADelegationState, build_a2a_tools
+
+    config = A2AClientConfig(endpoint="http://test-endpoint.com", max_turns=2)
+    agent = Agent(role="manager", goal="coordinate", backstory="test", a2a=config)
+    task = Task(description="test", expected_output="test", agent=agent)
+
+    card = _create_mock_agent_card()
+    state = A2ADelegationState(agent=agent, task=task)
+    [tool] = build_a2a_tools([config], {config.endpoint: card}, state)
+
+    assert tool.max_usage_count == 2
+
+
 def test_default_trust_remote_completion_status_is_false():
     """Verify that default value of trust_remote_completion_status is False."""
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-    )
-
+    a2a_config = A2AConfig(endpoint="http://test-endpoint.com")
     assert a2a_config.trust_remote_completion_status is False