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docs: update changelog dates (#2437)
* docs: update changelog dates * docs: add aws bedrock tools docs * docs: fix incorrect respect_context_window parameter in Crew example
This commit is contained in:
Tony Kipkemboi
@@ -4,7 +4,7 @@ description: View the latest updates and changes to CrewAI
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icon: timeline
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---
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<Update label="2024-03-17" description="v0.108.0">
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<Update label="2025-03-17" description="v0.108.0">
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**Features**
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- Converted tabs to spaces in `crew.py` template
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- Enhanced LLM Streaming Response Handling and Event System
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@@ -24,7 +24,7 @@ icon: timeline
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- Added documentation for `ApifyActorsTool`
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</Update>
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<Update label="2024-03-10" description="v0.105.0">
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<Update label="2025-03-10" description="v0.105.0">
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**Core Improvements & Fixes**
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- Fixed issues with missing template variables and user memory configuration
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- Improved async flow support and addressed agent response formatting
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@@ -45,7 +45,7 @@ icon: timeline
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- Fixed typos in prompts and updated Amazon Bedrock model listings
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</Update>
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<Update label="2024-02-12" description="v0.102.0">
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<Update label="2025-02-12" description="v0.102.0">
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**Core Improvements & Fixes**
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- Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
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- Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
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@@ -65,7 +65,7 @@ icon: timeline
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- Fixed Various Typos & Formatting Issues
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</Update>
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<Update label="2024-01-28" description="v0.100.0">
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<Update label="2025-01-28" description="v0.100.0">
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**Features**
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- Add Composio docs
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- Add SageMaker as a LLM provider
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@@ -80,7 +80,7 @@ icon: timeline
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- Improve formatting and clarity in CLI and Composio Tool docs
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</Update>
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<Update label="2024-01-20" description="v0.98.0">
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<Update label="2025-01-20" description="v0.98.0">
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**Features**
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- Conversation crew v1
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- Add unique ID to flow states
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@@ -101,7 +101,7 @@ icon: timeline
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- Fixed typos, nested pydantic model issue, and docling issues
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</Update>
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<Update label="2024-01-04" description="v0.95.0">
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<Update label="2025-01-04" description="v0.95.0">
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**New Features**
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- Adding Multimodal Abilities to Crew
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- Programatic Guardrails
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@@ -131,7 +131,7 @@ icon: timeline
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- Suppressed userWarnings from litellm pydantic issues
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</Update>
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<Update label="2023-12-05" description="v0.86.0">
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<Update label="2024-12-05" description="v0.86.0">
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**Changes**
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- Remove all references to pipeline and pipeline router
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- Add Nvidia NIM as provider in Custom LLM
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@@ -141,7 +141,7 @@ icon: timeline
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- Simplify template crew
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</Update>
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<Update label="2023-12-04" description="v0.85.0">
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<Update label="2024-12-04" description="v0.85.0">
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**Features**
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- Added knowledge to agent level
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- Feat/remove langchain
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@@ -161,7 +161,7 @@ icon: timeline
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- Improvements to LLM Configuration and Usage
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</Update>
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<Update label="2023-11-25" description="v0.83.0">
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<Update label="2024-11-25" description="v0.83.0">
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**New Features**
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- New before_kickoff and after_kickoff crew callbacks
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- Support to pre-seed agents with Knowledge
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@@ -178,7 +178,7 @@ icon: timeline
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- Update Docs
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</Update>
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<Update label="2023-11-13" description="v0.80.0">
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<Update label="2024-11-13" description="v0.80.0">
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**Fixes**
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- Fixing Tokens callback replacement bug
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- Fixing Step callback issue
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@@ -111,6 +111,8 @@
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"pages": [
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"tools/aimindtool",
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"tools/apifyactorstool",
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"tools/bedrockinvokeagenttool",
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"tools/bedrockkbretriever",
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"tools/bravesearchtool",
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"tools/browserbaseloadtool",
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"tools/codedocssearchtool",
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@@ -39,8 +39,7 @@ analysis_crew = Crew(
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agents=[coding_agent],
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tasks=[data_analysis_task],
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verbose=True,
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memory=False,
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respect_context_window=True # enable by default
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memory=False
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)
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datasets = [
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187
docs/tools/bedrockinvokeagenttool.mdx
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187
docs/tools/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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165
docs/tools/bedrockkbretriever.mdx
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165
docs/tools/bedrockkbretriever.mdx
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@@ -0,0 +1,165 @@
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---
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title: 'Bedrock Knowledge Base Retriever'
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description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
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icon: aws
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---
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# `BedrockKBRetrieverTool`
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The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
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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 Knowledge Base
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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, 4-17}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
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# Initialize the tool
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kb_tool = BedrockKBRetrieverTool(
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knowledge_base_id="your-kb-id",
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number_of_results=5
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)
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# Create a CrewAI agent that uses the tool
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researcher = Agent(
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role='Knowledge Base Researcher',
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goal='Find information about company policies',
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backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
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tools=[kb_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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research_task = Task(
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description="Find our company's remote work policy and summarize the key points.",
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agent=researcher
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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=[researcher],
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tasks=[research_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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| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
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| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
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| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
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| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
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| **next_token** | `str` | No | None | Token for pagination |
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## Environment Variables
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```bash
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BEDROCK_KB_ID=your-knowledge-base-id # Alternative to passing knowledge_base_id
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AWS_REGION=your-aws-region # Defaults to us-east-1
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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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## Response Format
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The tool returns results in JSON format:
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```json
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{
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"results": [
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{
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"content": "Retrieved text content",
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"content_type": "text",
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"source_type": "S3",
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"source_uri": "s3://bucket/document.pdf",
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"score": 0.95,
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"metadata": {
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"additional": "metadata"
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}
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}
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],
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"nextToken": "pagination-token",
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"guardrailAction": "NONE"
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}
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```
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## Advanced Usage
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### Custom Retrieval Configuration
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```python
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kb_tool = BedrockKBRetrieverTool(
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knowledge_base_id="your-kb-id",
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retrieval_configuration={
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"vectorSearchConfiguration": {
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"numberOfResults": 10,
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"overrideSearchType": "HYBRID"
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}
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}
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)
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policy_expert = Agent(
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role='Policy Expert',
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goal='Analyze company policies in detail',
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backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
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tools=[kb_tool]
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)
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```
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## Supported Data Sources
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- Amazon S3
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- Confluence
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- Salesforce
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- SharePoint
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- Web pages
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- Custom document locations
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- Amazon Kendra
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- SQL databases
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## Use Cases
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|
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### Enterprise Knowledge Integration
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- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
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- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
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- Create agents that can answer questions based on your internal documentation while maintaining data security
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### Specialized Domain Knowledge
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- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
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- Leverage existing knowledge repositories that are already maintained in your AWS environment
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- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
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### Data-Driven Decision Making
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- Ground CrewAI agent responses in your actual company data rather than general knowledge
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- Ensure agents provide recommendations based on your specific business context and documentation
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- Reduce hallucinations by retrieving factual information from your knowledge bases
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### Scalable Information Access
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- Access terabytes of organizational knowledge without embedding it all into your models
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- Dynamically query only the relevant information needed for specific tasks
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- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
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### Compliance and Governance
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- Ensure CrewAI agents provide responses that align with your company's approved documentation
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- Create auditable trails of information sources used by your agents
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- Maintain control over what information sources your agents can access
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