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

docs/changelog.mdx

@@ -4,7 +4,7 @@ description: View the latest updates and changes to CrewAI
 icon: timeline
 ---
 
-<Update label="2024-03-17" description="v0.108.0">
+<Update label="2025-03-17" description="v0.108.0">
   **Features**
   - Converted tabs to spaces in `crew.py` template
   - Enhanced LLM Streaming Response Handling and Event System
@@ -24,7 +24,7 @@ icon: timeline
   - Added documentation for `ApifyActorsTool`
 </Update>
 
-<Update label="2024-03-10" description="v0.105.0">
+<Update label="2025-03-10" description="v0.105.0">
   **Core Improvements & Fixes**
   - Fixed issues with missing template variables and user memory configuration
   - Improved async flow support and addressed agent response formatting
@@ -45,7 +45,7 @@ icon: timeline
   - Fixed typos in prompts and updated Amazon Bedrock model listings
 </Update>
 
-<Update label="2024-02-12" description="v0.102.0">
+<Update label="2025-02-12" description="v0.102.0">
   **Core Improvements & Fixes**
   - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
   - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
@@ -65,7 +65,7 @@ icon: timeline
   - Fixed Various Typos & Formatting Issues
 </Update>
 
-<Update label="2024-01-28" description="v0.100.0">
+<Update label="2025-01-28" description="v0.100.0">
   **Features**
   - Add Composio docs
   - Add SageMaker as a LLM provider
@@ -80,7 +80,7 @@ icon: timeline
   - Improve formatting and clarity in CLI and Composio Tool docs
 </Update>
 
-<Update label="2024-01-20" description="v0.98.0">
+<Update label="2025-01-20" description="v0.98.0">
   **Features**
   - Conversation crew v1
   - Add unique ID to flow states
@@ -101,7 +101,7 @@ icon: timeline
   - Fixed typos, nested pydantic model issue, and docling issues
 </Update>
 
-<Update label="2024-01-04" description="v0.95.0">
+<Update label="2025-01-04" description="v0.95.0">
   **New Features**
   - Adding Multimodal Abilities to Crew
   - Programatic Guardrails
@@ -131,7 +131,7 @@ icon: timeline
   - Suppressed userWarnings from litellm pydantic issues
 </Update>
 
-<Update label="2023-12-05" description="v0.86.0">
+<Update label="2024-12-05" description="v0.86.0">
   **Changes**
   - Remove all references to pipeline and pipeline router
   - Add Nvidia NIM as provider in Custom LLM
@@ -141,7 +141,7 @@ icon: timeline
   - Simplify template crew
 </Update>
 
-<Update label="2023-12-04" description="v0.85.0">
+<Update label="2024-12-04" description="v0.85.0">
   **Features**
   - Added knowledge to agent level
   - Feat/remove langchain
@@ -161,7 +161,7 @@ icon: timeline
   - Improvements to LLM Configuration and Usage
 </Update>
 
-<Update label="2023-11-25" description="v0.83.0">
+<Update label="2024-11-25" description="v0.83.0">
   **New Features**
   - New before_kickoff and after_kickoff crew callbacks
   - Support to pre-seed agents with Knowledge
@@ -178,7 +178,7 @@ icon: timeline
   - Update Docs
 </Update>
 
-<Update label="2023-11-13" description="v0.80.0">
+<Update label="2024-11-13" description="v0.80.0">
   **Fixes**
   - Fixing Tokens callback replacement bug
   - Fixing Step callback issue

docs/docs.json

@@ -111,6 +111,8 @@
             "pages": [
               "tools/aimindtool",
               "tools/apifyactorstool",
+              "tools/bedrockinvokeagenttool",
+              "tools/bedrockkbretriever",
               "tools/bravesearchtool",
               "tools/browserbaseloadtool",
               "tools/codedocssearchtool",

docs/how-to/kickoff-for-each.mdx

@@ -39,8 +39,7 @@ analysis_crew = Crew(
     agents=[coding_agent],
     tasks=[data_analysis_task],
     verbose=True,
-    memory=False,
+    memory=False
-    respect_context_window=True  # enable by default
 )
 
 datasets = [

docs/tools/bedrockinvokeagenttool.mdx

@@ -0,0 +1,187 @@
+---
+title: Bedrock Invoke Agent Tool
+description: Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows
+icon: aws
+---
+
+# `BedrockInvokeAgentTool`
+
+The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows.
+
+## Installation
+
+```bash
+uv pip install 'crewai[tools]'
+```
+
+## Requirements
+
+- AWS credentials configured (either through environment variables or AWS CLI)
+- `boto3` and `python-dotenv` packages
+- Access to Amazon Bedrock Agents
+
+## Usage
+
+Here's how to use the tool with a CrewAI agent:
+
+```python {2, 4-8}
+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)
+```
+
+## Tool Arguments
+
+| Argument | Type | Required | Default | Description |
+|:---------|:-----|:---------|:--------|:------------|
+| **agent_id** | `str` | Yes | None | The unique identifier of the Bedrock agent |
+| **agent_alias_id** | `str` | Yes | None | The unique identifier of the agent alias |
+| **session_id** | `str` | No | timestamp | The unique identifier of the session |
+| **enable_trace** | `bool` | No | False | Whether to enable trace for debugging |
+| **end_session** | `bool` | No | False | Whether to end the session after invocation |
+| **description** | `str` | No | None | Custom description for the tool |
+
+## Environment Variables
+
+```bash
+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
+```
+
+## Advanced Usage
+
+### Multi-Agent Workflow with Session Management
+
+```python {2, 4-22}
+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()
+```
+
+## Use Cases
+
+### Hybrid Multi-Agent Collaborations
+- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS
+- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally
+- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows
+
+### Data Sovereignty and Compliance
+- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks
+- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account
+- Enable secure multi-agent collaborations where some agents cannot access your organization's private data
+
+### Seamless AWS Service Integration
+- Access any AWS service through Amazon Bedrock Actions without writing complex integration code
+- Enable CrewAI agents to interact with AWS services through natural language requests
+- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more
+
+### Scalable Hybrid Agent Architectures
+- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI
+- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents
+
+### Cross-Organizational Agent Collaboration
+- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents
+- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data
+- Build agent ecosystems that span organizational boundaries while maintaining security and data control

docs/tools/bedrockkbretriever.mdx

@@ -0,0 +1,165 @@
+---
+title: 'Bedrock Knowledge Base Retriever'
+description: 'Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries'
+icon: aws
+---
+
+# `BedrockKBRetrieverTool`
+
+The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries.
+
+## Installation
+
+```bash
+uv pip install 'crewai[tools]'
+```
+
+## Requirements
+
+- AWS credentials configured (either through environment variables or AWS CLI)
+- `boto3` and `python-dotenv` packages
+- Access to Amazon Bedrock Knowledge Base
+
+## Usage
+
+Here's how to use the tool with a CrewAI agent:
+
+```python {2, 4-17}
+from crewai import Agent, Task, Crew
+from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool
+
+# Initialize the tool
+kb_tool = BedrockKBRetrieverTool(
+    knowledge_base_id="your-kb-id",
+    number_of_results=5
+)
+
+# Create a CrewAI agent that uses the tool
+researcher = Agent(
+    role='Knowledge Base Researcher',
+    goal='Find information about company policies',
+    backstory='I am a researcher specialized in retrieving and analyzing company documentation.',
+    tools=[kb_tool],
+    verbose=True
+)
+
+# Create a task for the agent
+research_task = Task(
+    description="Find our company's remote work policy and summarize the key points.",
+    agent=researcher
+)
+
+# Create a crew with the agent
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task],
+    verbose=2
+)
+
+# Run the crew
+result = crew.kickoff()
+print(result)   
+```
+
+## Tool Arguments
+
+| Argument | Type | Required | Default | Description |
+|:---------|:-----|:---------|:---------|:-------------|
+| **knowledge_base_id** | `str` | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) |
+| **number_of_results** | `int` | No | 5 | Maximum number of results to return |
+| **retrieval_configuration** | `dict` | No | None | Custom configurations for the knowledge base query |
+| **guardrail_configuration** | `dict` | No | None | Content filtering settings |
+| **next_token** | `str` | No | None | Token for pagination |
+
+## Environment Variables
+
+```bash
+BEDROCK_KB_ID=your-knowledge-base-id  # Alternative to passing knowledge_base_id
+AWS_REGION=your-aws-region            # Defaults to us-east-1
+AWS_ACCESS_KEY_ID=your-access-key     # Required for AWS authentication
+AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
+```
+
+## Response Format
+
+The tool returns results in JSON format:
+
+```json
+{
+  "results": [
+    {
+      "content": "Retrieved text content",
+      "content_type": "text",
+      "source_type": "S3",
+      "source_uri": "s3://bucket/document.pdf",
+      "score": 0.95,
+      "metadata": {
+        "additional": "metadata"
+      }
+    }
+  ],
+  "nextToken": "pagination-token",
+  "guardrailAction": "NONE"
+}
+```
+
+## Advanced Usage
+
+### Custom Retrieval Configuration
+
+```python
+kb_tool = BedrockKBRetrieverTool(
+    knowledge_base_id="your-kb-id",
+    retrieval_configuration={
+        "vectorSearchConfiguration": {
+            "numberOfResults": 10,
+            "overrideSearchType": "HYBRID"
+        }
+    }
+)
+
+policy_expert = Agent(
+    role='Policy Expert',
+    goal='Analyze company policies in detail',
+    backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.',
+    tools=[kb_tool]
+)
+```
+
+## Supported Data Sources
+
+- Amazon S3
+- Confluence
+- Salesforce
+- SharePoint
+- Web pages
+- Custom document locations
+- Amazon Kendra
+- SQL databases    
+
+## Use Cases
+
+### Enterprise Knowledge Integration
+- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data
+- Allow agents to make decisions based on your company's specific policies, procedures, and documentation
+- Create agents that can answer questions based on your internal documentation while maintaining data security
+
+### Specialized Domain Knowledge
+- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models
+- Leverage existing knowledge repositories that are already maintained in your AWS environment
+- Combine CrewAI's reasoning with domain-specific information from your knowledge bases
+
+### Data-Driven Decision Making
+- Ground CrewAI agent responses in your actual company data rather than general knowledge
+- Ensure agents provide recommendations based on your specific business context and documentation
+- Reduce hallucinations by retrieving factual information from your knowledge bases
+
+### Scalable Information Access
+- Access terabytes of organizational knowledge without embedding it all into your models
+- Dynamically query only the relevant information needed for specific tasks
+- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently
+
+### Compliance and Governance
+- Ensure CrewAI agents provide responses that align with your company's approved documentation
+- Create auditable trails of information sources used by your agents
+- Maintain control over what information sources your agents can access