Bedrock Invoke Agent Tool

`BedrockInvokeAgentTool`

BedrockInvokeAgentTool 让 CrewAI agents 能够调用 Amazon Bedrock Agents，并在你的工作流中利用它们的能力。

安装

uv pip install 'crewai[tools]'

要求

已配置 AWS 凭据（通过环境变量或 AWS CLI）
boto3 和 python-dotenv 包
可访问 Amazon Bedrock Agents

用法

下面展示如何在 CrewAI agent 中使用该工具：

from crewai import Agent, Task, Crew
from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool

# Initialize the tool
agent_tool = BedrockInvokeAgentTool(
    agent_id="your-agent-id",
    agent_alias_id="your-agent-alias-id"
)

# Create a CrewAI agent that uses the tool
aws_expert = Agent(
    role='AWS Service Expert',
    goal='Help users understand AWS services and quotas',
    backstory='I am an expert in AWS services and can provide detailed information about them.',
    tools=[agent_tool],
    verbose=True
)

# Create a task for the agent
quota_task = Task(
    description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
    agent=aws_expert
)

# Create a crew with the agent
crew = Crew(
    agents=[aws_expert],
    tasks=[quota_task],
    verbose=2
)

# Run the crew
result = crew.kickoff()
print(result)

工具参数

Argument	Type	Required	Default	Description
agent_id	`str`	Yes	None	Bedrock agent 的唯一标识符
agent_alias_id	`str`	Yes	None	agent alias 的唯一标识符
session_id	`str`	No	timestamp	session 的唯一标识符
enable_trace	`bool`	No	False	是否启用 trace 用于调试
end_session	`bool`	No	False	是否在调用后结束 session
description	`str`	No	None	工具的自定义描述

环境变量

BEDROCK_AGENT_ID=your-agent-id           # Alternative to passing agent_id
BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
AWS_REGION=your-aws-region               # Defaults to us-west-2
AWS_ACCESS_KEY_ID=your-access-key        # Required for AWS authentication
AWS_SECRET_ACCESS_KEY=your-secret-key    # Required for AWS authentication

高级用法

带 Session 管理的多 Agent 工作流

from crewai import Agent, Task, Crew, Process
from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool

# Initialize tools with session management
initial_tool = BedrockInvokeAgentTool(
    agent_id="your-agent-id",
    agent_alias_id="your-agent-alias-id",
    session_id="custom-session-id"
)

followup_tool = BedrockInvokeAgentTool(
    agent_id="your-agent-id",
    agent_alias_id="your-agent-alias-id",
    session_id="custom-session-id"
)

final_tool = BedrockInvokeAgentTool(
    agent_id="your-agent-id",
    agent_alias_id="your-agent-alias-id",
    session_id="custom-session-id",
    end_session=True
)

# Create agents for different stages
researcher = Agent(
    role='AWS Service Researcher',
    goal='Gather information about AWS services',
    backstory='I am specialized in finding detailed AWS service information.',
    tools=[initial_tool]
)

analyst = Agent(
    role='Service Compatibility Analyst',
    goal='Analyze service compatibility and requirements',
    backstory='I analyze AWS services for compatibility and integration possibilities.',
    tools=[followup_tool]
)

summarizer = Agent(
    role='Technical Documentation Writer',
    goal='Create clear technical summaries',
    backstory='I specialize in creating clear, concise technical documentation.',
    tools=[final_tool]
)

# Create tasks
research_task = Task(
    description="Find all available AWS services in us-west-2 region.",
    agent=researcher
)

analysis_task = Task(
    description="Analyze which services support IPv6 and their implementation requirements.",
    agent=analyst
)

summary_task = Task(
    description="Create a summary of IPv6-compatible services and their key features.",
    agent=summarizer
)

# Create a crew with the agents and tasks
crew = Crew(
    agents=[researcher, analyst, summarizer],
    tasks=[research_task, analysis_task, summary_task],
    process=Process.sequential,
    verbose=2
)

# Run the crew
result = crew.kickoff()

用例

混合多 Agent 协作

创建工作流，让 CrewAI agents 与运行在 AWS 中的托管 Bedrock agents 协作
让敏感数据处理在你的 AWS 环境中进行，同时其他 agents 在外部运行
将本地 CrewAI agents 与基于云的 Bedrock agents 连接起来，实现分布式智能工作流

数据主权与合规

让数据敏感型 agentic 工作流留在你的 AWS 环境中，同时允许外部 CrewAI agents 编排任务
只在你的 AWS account 内处理敏感信息，以满足数据驻留要求
让一些 agents 无法访问组织私有数据的同时，依然实现安全的多 agent 协作

无缝 AWS 服务集成

无需编写复杂的集成代码，即可通过 Amazon Bedrock Actions 访问任意 AWS 服务
让 CrewAI agents 通过自然语言请求与 AWS 服务交互
利用预构建的 Bedrock agent 能力与 Bedrock Knowledge Bases、Lambda 等 AWS 服务交互

可扩展的混合 agent 架构

将计算密集型任务卸载给托管 Bedrock agents，而轻量任务在 CrewAI 中运行
通过在本地 CrewAI agents 与云端 Bedrock agents 之间分配工作负载来扩展 agent 处理能力

跨组织 agent 协作

让你组织内的 CrewAI agents 与合作方组织的 Bedrock agents 安全协作
创建工作流，使来自 Bedrock agents 的外部专业能力能够在不暴露敏感数据的前提下被纳入
构建跨越组织边界的 agent 生态系统，同时保持安全与数据控制