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docs: major docs updates (#2897)
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Tony Kipkemboi
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docs/tools/cloud-storage/bedrockinvokeagenttool.mdx
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docs/tools/cloud-storage/bedrockinvokeagenttool.mdx
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---
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title: Bedrock Invoke Agent Tool
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description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
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icon: aws
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---
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# `BedrockInvokeAgentTool`
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The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
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## Installation
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```bash
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uv pip install 'crewai[tools]'
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```
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## Requirements
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- AWS credentials configured (either through environment variables or AWS CLI)
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- `boto3` and `python-dotenv` packages
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- Access to Amazon Bedrock Agents
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## Usage
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Here's how to use the tool with a CrewAI agent:
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```python {2, 4-8}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize the tool
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agent_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id"
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)
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# Create a CrewAI agent that uses the tool
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aws_expert = Agent(
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role='AWS Service Expert',
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goal='Help users understand AWS services and quotas',
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backstory='I am an expert in AWS services and can provide detailed information about them.',
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tools=[agent_tool],
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verbose=True
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)
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# Create a task for the agent
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quota_task = Task(
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description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
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agent=aws_expert
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)
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# Create a crew with the agent
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crew = Crew(
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agents=[aws_expert],
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tasks=[quota_task],
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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print(result)
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```
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## Tool Arguments
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| Argument | Type | Required | Default | Description |
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|:---------|:-----|:---------|:--------|:------------|
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| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
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| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
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| **session_id** | `str` | No | timestamp | The unique identifier of the session |
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| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
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| **end_session** | `bool` | No | False | Whether to end the session after invocation |
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| **description** | `str` | No | None | Custom description for the tool |
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## Environment Variables
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```bash
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BEDROCK_AGENT_ID=your-agent-id # Alternative to passing agent_id
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BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
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AWS_REGION=your-aws-region # Defaults to us-west-2
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AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
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AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
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```
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## Advanced Usage
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### Multi-Agent Workflow with Session Management
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```python {2, 4-22}
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from crewai import Agent, Task, Crew, Process
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize tools with session management
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initial_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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followup_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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final_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id",
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end_session=True
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)
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# Create agents for different stages
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researcher = Agent(
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role='AWS Service Researcher',
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goal='Gather information about AWS services',
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backstory='I am specialized in finding detailed AWS service information.',
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tools=[initial_tool]
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)
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analyst = Agent(
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role='Service Compatibility Analyst',
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goal='Analyze service compatibility and requirements',
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backstory='I analyze AWS services for compatibility and integration possibilities.',
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tools=[followup_tool]
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)
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summarizer = Agent(
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role='Technical Documentation Writer',
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goal='Create clear technical summaries',
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backstory='I specialize in creating clear, concise technical documentation.',
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tools=[final_tool]
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)
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# Create tasks
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research_task = Task(
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description="Find all available AWS services in us-west-2 region.",
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agent=researcher
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)
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analysis_task = Task(
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description="Analyze which services support IPv6 and their implementation requirements.",
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agent=analyst
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)
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summary_task = Task(
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description="Create a summary of IPv6-compatible services and their key features.",
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agent=summarizer
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)
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# Create a crew with the agents and tasks
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crew = Crew(
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agents=[researcher, analyst, summarizer],
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tasks=[research_task, analysis_task, summary_task],
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process=Process.sequential,
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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```
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## Use Cases
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### Hybrid Multi-Agent Collaborations
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- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
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- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
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- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
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### Data Sovereignty and Compliance
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- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
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- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
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- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
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### Seamless AWS Service Integration
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- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
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- Enable CrewAI agents to interact with AWS services through natural language requests
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- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
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### Scalable Hybrid Agent Architectures
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- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
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- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
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### Cross-Organizational Agent Collaboration
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- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
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- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
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- Build agent ecosystems that span organizational boundaries while maintaining security and data control
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