diff --git a/docs/swarms/examples/agent_rearrange_example.md b/docs/swarms/examples/agent_rearrange_example.md
index 042e0698..c0c8e430 100644
--- a/docs/swarms/examples/agent_rearrange_example.md
+++ b/docs/swarms/examples/agent_rearrange_example.md
@@ -1,15 +1,12 @@
-# AgentRearrange Example
+# AgentRearrange Examples
 
-!!! abstract "Overview"
-    Learn how to create flexible multi-agent workflows using `AgentRearrange`. Define custom flow patterns with sequential execution (`->`) and concurrent execution (`,`) to orchestrate agents in sophisticated workflows.
+The `AgentRearrange` architecture enables flexible orchestration of multiple agents using custom flow patterns. You can define sequential execution (using `->`) and concurrent execution (using `,`) within the same workflow, allowing for sophisticated multi-agent task processing.
 
 ## Prerequisites
 
-!!! info "Before You Begin"
-    Make sure you have:
-    - Python 3.7+ installed
-    - A valid API key for your model provider
-    - The Swarms package installed
+- Python 3.7+
+- OpenAI API key or other supported LLM provider keys
+- Swarms library
 
 ## Installation
 
@@ -17,159 +14,542 @@
 pip3 install -U swarms
 ```
 
-## Environment Setup
+## Environment Variables
 
-!!! tip "API Key Configuration"
-    Set your API key in the `.env` file:
-    ```bash
-    OPENAI_API_KEY="your-api-key-here"
-    ```
+```plaintext
+WORKSPACE_DIR="agent_workspace"
+OPENAI_API_KEY=""
+ANTHROPIC_API_KEY=""
+GROQ_API_KEY=""
+```
+
+## Understanding Flow Patterns
+
+AgentRearrange uses a simple but powerful syntax to define agent execution:
+
+- **Sequential execution**: Use `->` to chain agents sequentially
+  - Example: `"Researcher -> Writer -> Editor"`
+  
+- **Concurrent execution**: Use `,` to run agents in parallel
+  - Example: `"Researcher1, Researcher2 -> Writer"`
+  
+- **Mixed patterns**: Combine both for complex workflows
+  - Example: `"Researcher -> Writer1, Writer2 -> Editor"`
+
+## Basic Usage
+
+### 1. Simple Sequential Flow
+
+```python
+from swarms import Agent, AgentRearrange
+
+# Create specialized agents
+researcher = Agent(
+    agent_name="Researcher",
+    system_prompt="""You are a research specialist. Your tasks include:
+    1. Gathering information from reliable sources
+    2. Analyzing data and identifying key insights
+    3. Synthesizing findings into clear summaries
+    4. Verifying facts and checking sources
+    5. Organizing information logically""",
+    model_name="gpt-4.1",
+    max_loops=1,
+    temperature=0.7,
+)
+
+writer = Agent(
+    agent_name="Writer",
+    system_prompt="""You are a professional writer. Your responsibilities include:
+    1. Creating clear and engaging content
+    2. Structuring information logically
+    3. Ensuring proper grammar and style
+    4. Adapting tone for the target audience
+    5. Incorporating research findings effectively""",
+    model_name="gpt-4.1",
+    max_loops=1,
+    temperature=0.7,
+)
+
+editor = Agent(
+    agent_name="Editor",
+    system_prompt="""You are an expert editor. Your focus areas include:
+    1. Reviewing content for clarity and accuracy
+    2. Correcting grammar and spelling errors
+    3. Improving sentence structure and flow
+    4. Ensuring consistency in style and tone
+    5. Verifying facts and checking sources""",
+    model_name="gpt-4.1",
+    max_loops=1,
+    temperature=0.7,
+)
+
+# Define sequential flow: Researcher -> Writer -> Editor
+flow = "Researcher -> Writer -> Editor"
+
+# Create the AgentRearrange system
+workflow = AgentRearrange(
+    name="content-creation-workflow",
+    agents=[researcher, writer, editor],
+    flow=flow,
+    max_loops=1,
+    verbose=True,
+)
+
+# Execute the workflow
+result = workflow.run(
+    "Research and write a comprehensive article about the impact of AI on healthcare"
+)
+print(result)
+```
+
+### 2. Parallel Execution
+
+```python
+# Create multiple research agents
+researcher1 = Agent(
+    agent_name="Market-Researcher",
+    system_prompt="Research market trends and competitive landscape",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+researcher2 = Agent(
+    agent_name="Financial-Researcher",
+    system_prompt="Research financial data and performance metrics",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+analyst = Agent(
+    agent_name="Analyst",
+    system_prompt="Synthesize research findings into actionable insights",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Define flow: Both researchers run in parallel, then analyst processes results
+flow = "Market-Researcher, Financial-Researcher -> Analyst"
+
+workflow = AgentRearrange(
+    name="parallel-research-workflow",
+    agents=[researcher1, researcher2, analyst],
+    flow=flow,
+    max_loops=1,
+)
+
+result = workflow.run("Analyze Tesla (TSLA) from market and financial perspectives")
+```
+
+### 3. Mixed Sequential and Parallel Flow
+
+```python
+# Create agents for a complex workflow
+researcher = Agent(
+    agent_name="Researcher",
+    system_prompt="Conduct comprehensive research on the topic",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+writer1 = Agent(
+    agent_name="Technical-Writer",
+    system_prompt="Write technical content based on research",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+writer2 = Agent(
+    agent_name="Marketing-Writer",
+    system_prompt="Write marketing content based on research",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+editor = Agent(
+    agent_name="Editor",
+    system_prompt="Review and edit all written content",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Complex flow: Sequential -> Parallel -> Sequential
+flow = "Researcher -> Technical-Writer, Marketing-Writer -> Editor"
+
+workflow = AgentRearrange(
+    name="mixed-workflow",
+    agents=[researcher, writer1, writer2, editor],
+    flow=flow,
+    max_loops=1,
+)
+
+result = workflow.run("Create technical and marketing content about cloud computing")
+```
+
+## Sequential Awareness Feature
+
+The `team_awareness` feature provides agents with context about their position in the workflow, enabling better coordination and task understanding.
+
+### How Sequential Awareness Works
+
+When enabled, agents automatically receive information about:
+- **Agent ahead**: The agent that completed their task before them
+- **Agent behind**: The agent that will receive their output next
+
+This helps agents:
+- Reference previous work more effectively
+- Prepare output suitable for the next agent
+- Understand their role in the larger workflow
+
+### Example with Sequential Awareness
+
+```python
+from swarms import Agent, AgentRearrange
+
+# Create agents
+researcher = Agent(
+    agent_name="Researcher",
+    system_prompt="Research the topic thoroughly and provide comprehensive findings",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+writer = Agent(
+    agent_name="Writer",
+    system_prompt="Write content based on the research provided. Consider what the editor will need.",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+editor = Agent(
+    agent_name="Editor",
+    system_prompt="Edit the written content for clarity, accuracy, and style",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Create workflow with team awareness enabled
+workflow = AgentRearrange(
+    name="aware-workflow",
+    agents=[researcher, writer, editor],
+    flow="Researcher -> Writer -> Editor",
+    team_awareness=True,  # Enable sequential awareness
+    time_enabled=True,     # Enable timestamps
+    message_id_on=True,   # Enable message IDs
+    verbose=True,
+)
+
+# Get flow structure information
+flow_structure = workflow.get_sequential_flow_structure()
+print("Flow Structure:", flow_structure)
+
+# Get awareness for specific agent
+writer_awareness = workflow.get_agent_sequential_awareness("Writer")
+print("Writer Awareness:", writer_awareness)
+
+# Execute workflow
+result = workflow.run("Research and write about quantum computing")
+```
+
+**What happens automatically:**
+- **Researcher** runs first (no awareness info needed)
+- **Writer** receives: "Sequential awareness: Agent ahead: Researcher | Agent behind: Editor"
+- **Editor** receives: "Sequential awareness: Agent ahead: Writer"
+
+## Advanced Features
+
+### Output Formatting Options
+
+The workflow supports multiple output formats:
+
+- **"all"**: Complete conversation history (default)
+- **"final"**: Only the final agent's output
+- **"list"**: List of individual agent outputs
+- **"dict"**: Dictionary with agent names as keys
+
+```python
+workflow = AgentRearrange(
+    agents=[researcher, writer, editor],
+    flow="Researcher -> Writer -> Editor",
+    output_type="dict",  # Get structured dictionary output
+)
+```
+
+### Human-in-the-Loop
+
+Add human review points in your workflow:
+
+```python
+def human_reviewer(input_text: str) -> str:
+    """Custom human-in-the-loop function"""
+    print(f"\nReview this content:\n{input_text}\n")
+    response = input("Enter your feedback or 'approve' to continue: ")
+    return response if response else "approved"
+
+workflow = AgentRearrange(
+    agents=[researcher, writer, editor],
+    flow="Researcher -> Writer -> H -> Editor",  # H = Human review point
+    human_in_the_loop=True,
+    custom_human_in_the_loop=human_reviewer,
+)
+```
 
-## Code Implementation
+### Memory System Integration
 
-### Import Required Modules
+Integrate with memory systems for persistent storage:
+
+```python
+from swarms.memory import QdrantVectorDatabase
+
+# Initialize memory system
+memory = QdrantVectorDatabase(
+    collection_name="agent_workflow_memory",
+    path="./memory"
+)
+
+workflow = AgentRearrange(
+    agents=[researcher, writer, editor],
+    flow="Researcher -> Writer -> Editor",
+    memory_system=memory,
+    autosave=True,
+)
+```
+
+### Custom Rules
+
+Add system-wide rules that apply to all agents:
+
+```python
+rules = """
+1. Always cite sources for factual claims
+2. Maintain professional tone throughout
+3. Check for accuracy before proceeding
+4. Highlight uncertainties when present
+"""
+
+workflow = AgentRearrange(
+    agents=[researcher, writer, editor],
+    flow="Researcher -> Writer -> Editor",
+    rules=rules,
+)
+```
+
+### Batch Processing
+
+Process multiple tasks efficiently:
+
+```python
+tasks = [
+    "Research and write about renewable energy",
+    "Research and write about electric vehicles",
+    "Research and write about battery technology",
+]
+
+# batch_size: how many tasks to process at the same time (2 = process 2 tasks, then next 2, etc.)
+results = workflow.batch_run(tasks, batch_size=2)
+for i, result in enumerate(results):
+    print(f"Task {i+1} Result:", result)
+```
+
+### Concurrent Task Processing
+
+Process multiple tasks concurrently:
+
+```python
+tasks = [
+    "Analyze Q1 financial performance",
+    "Analyze Q2 financial performance",
+    "Analyze Q3 financial performance",
+]
+
+# max_workers: how many tasks to run at the same time (3 = process 3 tasks simultaneously)
+results = workflow.concurrent_run(tasks, max_workers=3)
+```
+
+## Example Implementations
+
+### Content Creation Pipeline
 
 ```python
 from swarms import Agent, AgentRearrange
+
+# Create content creation agents
+researcher = Agent(
+    agent_name="Researcher",
+    system_prompt="""You are a research specialist. Conduct thorough research on the given topic,
+    gather information from multiple sources, and provide comprehensive findings with citations.""",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+outline_creator = Agent(
+    agent_name="Outline-Creator",
+    system_prompt="""You are an outline specialist. Create detailed outlines based on research findings.
+    Structure content logically with clear sections and subsections.""",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+writer = Agent(
+    agent_name="Writer",
+    system_prompt="""You are a professional writer. Write engaging content based on the outline and research.
+    Ensure clarity, proper grammar, and engaging style.""",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+editor = Agent(
+    agent_name="Editor",
+    system_prompt="""You are an expert editor. Review content for accuracy, clarity, grammar, and style.
+    Ensure consistency and professional quality.""",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Create workflow with sequential awareness
+workflow = AgentRearrange(
+    name="content-pipeline",
+    agents=[researcher, outline_creator, writer, editor],
+    flow="Researcher -> Outline-Creator -> Writer -> Editor",
+    team_awareness=True,
+    output_type="dict",
+    verbose=True,
+)
+
+# Execute content creation
+result = workflow.run(
+    "Create a comprehensive guide on implementing machine learning in production systems"
+)
+
+# Display results
+for agent_name, output in result.items():
+    print(f"\n{agent_name} Output:")
+    print("-" * 50)
+    print(output)
+```
+
+### Multi-Perspective Analysis
+
+```python
+# Create analysis agents with different perspectives
+technical_analyst = Agent(
+    agent_name="Technical-Analyst",
+    system_prompt="Analyze from a technical perspective focusing on implementation details",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+business_analyst = Agent(
+    agent_name="Business-Analyst",
+    system_prompt="Analyze from a business perspective focusing on ROI and market impact",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+risk_analyst = Agent(
+    agent_name="Risk-Analyst",
+    system_prompt="Analyze from a risk perspective focusing on potential issues and mitigation",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+synthesizer = Agent(
+    agent_name="Synthesizer",
+    system_prompt="Synthesize all analysis perspectives into a comprehensive report",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Parallel analysis followed by synthesis
+workflow = AgentRearrange(
+    name="multi-perspective-analysis",
+    agents=[technical_analyst, business_analyst, risk_analyst, synthesizer],
+    flow="Technical-Analyst, Business-Analyst, Risk-Analyst -> Synthesizer",
+    output_type="dict",
+)
+
+result = workflow.run(
+    "Analyze the adoption of blockchain technology in financial services"
+)
+```
+
+### Document Processing Workflow
+
+```python
+# Create document processing agents
+extractor = Agent(
+    agent_name="Data-Extractor",
+    system_prompt="Extract key data points and information from documents",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+validator = Agent(
+    agent_name="Data-Validator",
+    system_prompt="Validate extracted data for accuracy and completeness",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+formatter = Agent(
+    agent_name="Data-Formatter",
+    system_prompt="Format validated data into structured output",
+    model_name="gpt-4.1",
+    max_loops=1,
+)
+
+# Sequential document processing
+workflow = AgentRearrange(
+    name="document-processor",
+    agents=[extractor, validator, formatter],
+    flow="Data-Extractor -> Data-Validator -> Data-Formatter",
+    team_awareness=True,
+    output_type="final",
+)
+
+result = workflow.run(
+    "Extract, validate, and format financial data from the quarterly report"
+)
 ```
 
-### Configure Agents
-
-!!! example "Agent Configuration"
-    Here's how to set up your specialized agents:
-
-    ```python
-    # Research Agent
-    researcher = Agent(
-        agent_name="Researcher",
-        system_prompt="You are a research specialist. Gather information, analyze data, and provide comprehensive findings.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    # Writer Agent
-    writer = Agent(
-        agent_name="Writer",
-        system_prompt="You are a professional writer. Create clear and engaging content based on research findings.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    # Editor Agent
-    editor = Agent(
-        agent_name="Editor",
-        system_prompt="You are an expert editor. Review content for clarity, accuracy, and style.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-    ```
-
-### Initialize AgentRearrange
-
-!!! example "Workflow Setup"
-    Configure AgentRearrange with your agents and flow pattern:
-
-    ```python
-    # Sequential flow: Researcher -> Writer -> Editor
-    flow = "Researcher -> Writer -> Editor"
-
-    workflow = AgentRearrange(
-        name="content-creation-workflow",
-        agents=[researcher, writer, editor],
-        flow=flow,
-        max_loops=1,
-    )
-    ```
-
-### Run the Workflow
-
-!!! example "Execute the Workflow"
-    Start the workflow:
-
-    ```python
-    result = workflow.run(
-        "Research and write a comprehensive article about the impact of AI on healthcare"
-    )
-    print(result)
-    ```
-
-## Complete Example
-
-!!! success "Full Implementation"
-    Here's the complete code combined:
-
-    ```python
-    from swarms import Agent, AgentRearrange
-
-    # Create agents
-    researcher = Agent(
-        agent_name="Researcher",
-        system_prompt="You are a research specialist. Gather information, analyze data, and provide comprehensive findings.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    writer = Agent(
-        agent_name="Writer",
-        system_prompt="You are a professional writer. Create clear and engaging content based on research findings.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    editor = Agent(
-        agent_name="Editor",
-        system_prompt="You are an expert editor. Review content for clarity, accuracy, and style.",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    # Define flow pattern
-    flow = "Researcher -> Writer -> Editor"
-
-    # Create workflow
-    workflow = AgentRearrange(
-        name="content-creation-workflow",
-        agents=[researcher, writer, editor],
-        flow=flow,
-        max_loops=1,
-    )
-
-    # Execute workflow
-    result = workflow.run(
-        "Research and write a comprehensive article about the impact of AI on healthcare"
-    )
-    print(result)
-    ```
-
-## Flow Pattern Examples
-
-!!! info "Flow Pattern Syntax"
-    - **Sequential**: `"Agent1 -> Agent2 -> Agent3"` - Agents run one after another
-    - **Parallel**: `"Agent1, Agent2 -> Agent3"` - Agent1 and Agent2 run simultaneously, then Agent3
-    - **Mixed**: `"Agent1 -> Agent2, Agent3 -> Agent4"` - Combine sequential and parallel execution
-
-## Configuration Options
-
-!!! info "Key Parameters"
-    | Parameter | Description | Default |
-    |-----------|-------------|---------|
-    | `agents` | List of Agent objects | Required |
-    | `flow` | Flow pattern string defining execution order | Required |
-    | `max_loops` | Maximum number of execution loops | 1 |
-    | `team_awareness` | Enable sequential awareness for agents | False |
-
-## Next Steps
-
-!!! tip "What to Try Next"
-    1. Experiment with parallel execution: `"Agent1, Agent2 -> Agent3"`
-    2. Enable `team_awareness=True` for better agent coordination
-    3. Try more complex flows combining sequential and parallel patterns
-    4. Use SwarmRouter with `swarm_type="AgentRearrange"` for unified interface
-
-## Troubleshooting
-
-!!! warning "Common Issues"
-    - Ensure agent names in flow match `agent_name` exactly
-    - Check for typos in agent names
-    - Verify all agents in flow are included in agents list
-    - Enable verbose mode for debugging: `verbose=True`
+## Using AgentRearrange with SwarmRouter
+
+You can also use AgentRearrange through SwarmRouter for a unified interface:
+
+```python
+from swarms import Agent, SwarmRouter
+
+# Create agents
+researcher = Agent(agent_name="Researcher", ...)
+writer = Agent(agent_name="Writer", ...)
+editor = Agent(agent_name="Editor", ...)
+
+# Use SwarmRouter with AgentRearrange
+router = SwarmRouter(
+    name="content-router",
+    agents=[researcher, writer, editor],
+    swarm_type="AgentRearrange",
+    rearrange_flow="Researcher -> Writer -> Editor",  # Required for AgentRearrange
+    max_loops=1,
+)
+
+result = router.run("Create content about AI ethics")
+```
+
+
+## Common Issues
+
+**Flow validation errors:**
+- Ensure agent names in flow match `agent_name` exactly
+- Check for typos in agent names
+- Verify all agents in flow are included in agents list
+
+**Agent execution failures:**
+- Check agent configurations (model names, API keys)
+- Verify system prompts are clear and appropriate
+- Enable verbose mode to see detailed error messages
+
+**Memory issues:**
+- Clear conversation history for large batch jobs
+- Use `output_type="final"` if you don't need full history
+- Consider disabling autosave for very large workflows
+