Merge pull request #302 from MervinPraison/develop

Develop

.gitignore

@@ -65,4 +65,5 @@ outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
 agents/praisonaiagents/praisonaiagents.egg-info
-.codegpt
+.codegpt
+.praison

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.37 gunicorn markdown
+RUN pip install flask praisonai==2.0.48 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents.yaml

@@ -1,6 +1,7 @@
 framework: praisonai
 process: sequential
 topic: create movie script about cat in mars
+memory: false
 roles:
   researcher:
     backstory: Skilled in research, with a focus on gathering accurate and relevant

agents/.cursorrules

@@ -1,6 +1,8 @@
-Make it minimal change as possible
-
-Firstly try not to make any modification to the existing code as possible
-
-Only if its highly required, without that if it can't be done, then modify the existing. 
-So by default try to add new code section and not modify the existing. 
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/.gitignore

@@ -83,4 +83,5 @@ dist
 build
 *.mp4 
 *.png 
-graph.py
+graph.py
+chroma_db/

agents/.praisonignore

@@ -0,0 +1,32 @@
+.cursorrules
+.gitignore
+.praisoncontext
+.praisonignore
+.windsurfrules
+async_example.py
+async_example_full.py
+async_example_full_multigroups.py
+auto_agents_example.py
+code_agents_example.py
+example_callback.py
+example_custom_tools.py
+example_sequential.py
+image.jpg
+langchain_example.py
+langchain_logs.py
+long_term.db
+mini_agents_example.py
+multimodal.py
+praisonaiagents/tools/*
+praisonaiagents/process/*
+pyproject.toml
+short_term.db
+structured_agents_example.py
+structured_response_example.py
+test.py
+tools_example.py
+uv.lock
+workflow_example_basic.py
+workflow_example_detailed.py
+__pycache__
+__pycache__/*

agents/.windsurfrules

@@ -0,0 +1,8 @@
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/autonomous-agent.py

@@ -0,0 +1,114 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_environment_state():
+    """Simulates getting current environment state"""
+    current_time = int(time.time())
+    states = ["normal", "critical", "optimal"]
+    state = states[current_time % 3]
+    print(f"Environment state: {state}")
+    return state
+
+def perform_action(state: str):
+    """Simulates performing an action based on state"""
+    actions = {
+        "normal": "maintain",
+        "critical": "fix",
+        "optimal": "enhance"
+    }
+    action = actions.get(state, "observe")
+    print(f"Performing action: {action} for state: {state}")
+    return action
+
+def get_feedback():
+    """Simulates environment feedback"""
+    current_time = int(time.time())
+    feedback = "positive" if current_time % 2 == 0 else "negative"
+    print(f"Feedback received: {feedback}")
+    return feedback
+
+# Create specialized agents
+llm_caller = Agent(
+    name="Environment Monitor",
+    role="State analyzer",
+    goal="Monitor environment and analyze state",
+    instructions="Check environment state and provide analysis",
+    tools=[get_environment_state]
+)
+
+action_agent = Agent(
+    name="Action Executor",
+    role="Action performer",
+    goal="Execute appropriate actions based on state",
+    instructions="Determine and perform actions based on environment state",
+    tools=[perform_action]
+)
+
+feedback_agent = Agent(
+    name="Feedback Processor",
+    role="Feedback analyzer",
+    goal="Process environment feedback and adapt strategy",
+    instructions="Analyze feedback and provide adaptation recommendations",
+    tools=[get_feedback]
+)
+
+# Create tasks for autonomous workflow
+monitor_task = Task(
+    name="monitor_environment",
+    description="Monitor and analyze environment state",
+    expected_output="Current environment state analysis",
+    agent=llm_caller,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["execute_action"],
+    condition={
+        "normal": ["execute_action"],
+        "critical": ["execute_action"],
+        "optimal": "exit"
+    }
+)
+
+action_task = Task(
+    name="execute_action",
+    description="Execute appropriate action based on state",
+    expected_output="Action execution result",
+    agent=action_agent,
+    next_tasks=["process_feedback"]
+)
+
+feedback_task = Task(
+    name="process_feedback",
+    description="Process feedback and adapt strategy",
+    expected_output="Strategy adaptation based on feedback",
+    agent=feedback_agent,
+    next_tasks=["monitor_environment"],  # Create feedback loop
+    context=[monitor_task, action_task]  # Access to previous states and actions
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[llm_caller, action_agent, feedback_agent],
+    tasks=[monitor_task, action_task, feedback_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Autonomous Agent Workflow...")
+    print("=" * 50)
+    
+    # Run autonomous workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nAutonomous Agent Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            task_name = result.description
+            print(f"\nTask: {task_name}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/chunking-direct-test.py

@@ -0,0 +1,14 @@
+from praisonaiagents.knowledge import Chunking
+
+# No heavy imports or initialization happens here
+chunking = Chunking(chunker_type='token')
+
+# Chunker is only initialized when first used
+chunks = chunking.chunk("Your text here")
+
+# Embedding model is only loaded if using semantic chunking
+semantic_chunking = Chunking(chunker_type='recursive')
+# Embedding model and chunker are loaded here on first use
+semantic_chunks = semantic_chunking.chunk("Your text here")
+
+print(semantic_chunks)

agents/code_review.py

@@ -0,0 +1,108 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import List, Dict
+
+def analyze_code_changes():
+    """Simulates code analysis"""
+    issues = [
+        {"type": "style", "severity": "low", "file": "main.py"},
+        {"type": "security", "severity": "high", "file": "auth.py"},
+        {"type": "performance", "severity": "medium", "file": "data.py"}
+    ]
+    return issues[int(time.time()) % 3]
+
+def suggest_fixes(issue: Dict):
+    """Simulates fix suggestions"""
+    fixes = {
+        "style": "Apply PEP 8 formatting",
+        "security": "Implement input validation",
+        "performance": "Use list comprehension"
+    }
+    return fixes.get(issue["type"], "Review manually")
+
+def apply_automated_fix(fix: str):
+    """Simulates applying automated fixes"""
+    success = int(time.time()) % 2 == 0
+    return "fixed" if success else "manual_review"
+
+# Create specialized agents
+analyzer = Agent(
+    name="Code Analyzer",
+    role="Code analysis",
+    goal="Analyze code changes and identify issues",
+    instructions="Review code changes and report issues",
+    tools=[analyze_code_changes]
+)
+
+fix_suggester = Agent(
+    name="Fix Suggester",
+    role="Solution provider",
+    goal="Suggest fixes for identified issues",
+    instructions="Provide appropriate fix suggestions",
+    tools=[suggest_fixes]
+)
+
+fix_applier = Agent(
+    name="Fix Applier",
+    role="Fix implementation",
+    goal="Apply suggested fixes automatically when possible",
+    instructions="Implement suggested fixes and report results",
+    tools=[apply_automated_fix]
+)
+
+# Create workflow tasks
+analysis_task = Task(
+    name="analyze_code",
+    description="Analyze code changes for issues",
+    expected_output="Identified code issues",
+    agent=analyzer,
+    is_start=True,
+    next_tasks=["suggest_fixes"]
+)
+
+suggestion_task = Task(
+    name="suggest_fixes",
+    description="Suggest fixes for identified issues",
+    expected_output="Fix suggestions",
+    agent=fix_suggester,
+    next_tasks=["apply_fixes"]
+)
+
+fix_task = Task(
+    name="apply_fixes",
+    description="Apply suggested fixes",
+    expected_output="Fix application status",
+    agent=fix_applier,
+    task_type="decision",
+    condition={
+        "fixed": "",
+        "manual_review": ["suggest_fixes"]  # Loop back for manual review
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[analyzer, fix_suggester, fix_applier],
+    tasks=[analysis_task, suggestion_task, fix_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Code Review Workflow...")
+    print("=" * 50)
+    
+    # Run workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nCode Review Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/data-analyst-agent.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+import os
+
+agent = Agent(instructions="You are a Data Analyst Agent", tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+agent.start(f"""
+    Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+    Analyse the data and give me the insights
+    read_csv to read the file
+""")

agents/document.txt

@@ -0,0 +1,3 @@
+I am Mervin Praison, a software engineer and entrepreneur.
+
+I am the founder of Praison AI, a company that is building AI agents to help people with their daily tasks.

agents/evaluator-optimiser.py

@@ -0,0 +1,70 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create generator and evaluator agents
+generator = Agent(
+    name="Generator",
+    role="Solution generator",
+    goal="Generate initial solutions and incorporate feedback",
+    instructions=(
+        "1. Look at the context from previous tasks.\n"
+        "2. If you see that you have already produced 2 points, then add another 2 new points "
+        "   so that the total becomes 10.\n"
+        "3. Otherwise, just produce the first 2 points.\n"
+        "4. Return only the final list of points, with no extra explanation."
+    )
+)
+
+evaluator = Agent(
+    name="Evaluator",
+    role="Solution evaluator",
+    goal="Evaluate solutions and provide improvement feedback",
+    instructions=(
+        "1. Count how many lines in the response start with a number and a period (like '1. ' or '2. ').\n"
+        "2. If there are 10 or more, respond with 'done'.\n"
+        "3. Otherwise, respond with 'more'.\n"
+        "4. Return only the single word: 'done' or 'more'."
+    )
+)
+
+
+# Create tasks for the feedback loop
+generate_task = Task(
+    name="generate",
+    description="Write 2 points about AI incuding if anything exiting from previous points",
+    expected_output="2 points",
+    agent=generator,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["evaluate"]
+)
+
+evaluate_task = Task(
+    name="evaluate",
+    description="Check if there are 10 points about AI",
+    expected_output="more or done",
+    agent=evaluator,
+    next_tasks=["generate"],
+    context=[generate_task],
+    task_type="decision",
+    condition={
+        "more": ["generate"],  # Continue to generate
+        "done": [""]  # Exit when optimization complete
+    }
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[generator, evaluator],
+    tasks=[generate_task, evaluate_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run optimization workflow
+results = workflow.start()
+
+# Print results
+print("\nEvaluator-Optimizer Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/finance-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+agent = Agent(instructions="You are a Research Agent", tools=[get_stock_price, get_stock_info, get_historical_data])
+agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")

agents/gradio-agents.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+def research(query):
+    agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+    result = agent.start(query)
+    # Format the result with enhanced markdown
+    formatted_result = f"""
+{result}
+----
+*Generated by PraisonAI Research Assistant*
+    """
+    return formatted_result
+
+# Create a simple Gradio interface
+demo = gr.Interface(
+    fn=research,
+    inputs=gr.Textbox(
+        label="Research Query",
+        placeholder="Enter your research topic...",
+        lines=2
+    ),
+    outputs=gr.Markdown(
+        show_copy_button=True,
+        height=500,
+        container=True
+    ),
+    title="AI Research Assistant",
+    description="Enter your research query below to get started!",
+)
+
+if __name__ == "__main__":
+    demo.launch()

agents/image-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/image-to-text-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/knowledge-agents-task.py

@@ -0,0 +1,48 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()

agents/knowledge-agents.py

@@ -0,0 +1,20 @@
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["large.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")

agents/knowledge-direct-test.py

@@ -0,0 +1,171 @@
+import logging
+from praisonaiagents.knowledge import Knowledge
+import os
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "test",
+            "path": ".praison",
+        }
+    }
+}
+
+knowledge = Knowledge(config)
+
+# Store memories
+logger.info("Storing memories...")
+cricket_memory = knowledge.store("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+logger.info(f"Stored cricket memory: {cricket_memory}")
+tennis_memory = knowledge.store("Enjoys playing tennis with friends", user_id="alice", metadata={"category": "hobbies"})
+logger.info(f"Stored tennis memory: {tennis_memory}")
+work_memory = knowledge.store("Works as a software engineer", user_id="alice", metadata={"category": "work"})
+logger.info(f"Stored work memory: {work_memory}")
+
+print("Stored memories:")
+print(cricket_memory)
+print(tennis_memory)
+print(work_memory)
+
+# Retrieve all memories for a user
+logger.info("Retrieving all memories for Alice...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories for Alice: {alice_memories}")
+print("\nAll memories for Alice:")
+for memory in alice_memories:
+    print(memory)
+
+# Retrieve a specific memory by ID
+logger.info(f"Retrieving memory with ID: {cricket_memory['results'][0]['id']}")
+retrieved_memory = knowledge.get(cricket_memory['results'][0]["id"])
+logger.info(f"Retrieved memory: {retrieved_memory}")
+print("\nRetrieved memory:")
+print(retrieved_memory)
+
+# Search for memories based on a query
+query = "What are Alice's hobbies?"
+logger.info(f"Searching for memories with query: {query}")
+search_results = knowledge.search(query, user_id="alice")
+logger.info(f"Search results: {search_results}")
+print(f"\nSearch results for '{query}':")
+for memory in search_results:
+    print(memory)
+
+# Update a memory
+logger.info(f"Updating memory with ID: {tennis_memory['results'][0]['id']}")
+updated_memory = knowledge.update(memory_id=tennis_memory['results'][0]["id"], data="Loves playing tennis on weekends")
+logger.info(f"Updated memory: {updated_memory}")
+print("\nUpdated memory:")
+print(updated_memory)
+
+# Get memory history
+logger.info(f"Retrieving memory history for ID: {tennis_memory['results'][0]['id']}")
+memory_history = knowledge.history(memory_id=tennis_memory['results'][0]["id"])
+logger.info(f"Memory history: {memory_history}")
+print("\nMemory history:")
+for entry in memory_history:
+    print(entry)
+
+# Delete a memory
+logger.info(f"Deleting memory with ID: {work_memory['results'][0]['id']}")
+knowledge.delete(memory_id=work_memory['results'][0]["id"])
+logger.info("Memory deleted")
+print("\nDeleted memory")
+
+# Retrieve memories after deletion
+logger.info("Retrieving memories after deletion...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories after deletion: {alice_memories}")
+print("\nMemories after deletion:")
+for memory in alice_memories:
+    print(memory)
+
+# Delete all memories for a user
+logger.info("Deleting all memories for Alice...")
+knowledge.delete_all(user_id="alice")
+logger.info("All memories for Alice deleted")
+print("\nAll memories for Alice deleted")
+
+# Retrieve memories after deleting all
+logger.info("Retrieving memories after deleting all...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories after deleting all: {alice_memories}")
+print("\nMemories after deleting all:")
+print(alice_memories)
+
+# Reset all memories
+logger.info("Resetting all memories...")
+knowledge.reset()
+logger.info("All memories reset")
+print("\nAll memories reset")
+
+# Test file handling
+logger.info("Testing file handling...")
+
+try:
+    # Get absolute path to test files
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    pdf_file = os.path.join(current_dir, "small.pdf")
+    txt_file = os.path.join(current_dir, "sample.txt")
+
+    # Test text file
+    logger.info(f"Testing text file: {txt_file}")
+    # Read and display content
+    with open(txt_file, 'r') as file:
+        txt_content = file.read()
+        print("\nText file content:")
+        print(txt_content)
+    # Store the text file using add
+    txt_memory = knowledge.add(txt_file, user_id="bob", metadata={"type": "text"})
+    logger.info(f"Stored text file: {txt_memory}")
+    print("\nStored text content:")
+    if txt_memory and 'results' in txt_memory and txt_memory['results']:
+        for mem in txt_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the text file")
+
+    # Verify text memories are stored
+    if txt_memory and 'results' in txt_memory and txt_memory['results']:
+        logger.info("Verifying text memories...")
+        for mem in txt_memory['results']:
+            if isinstance(mem, dict) and 'id' in mem:
+                stored = knowledge.get(mem['id'])
+                logger.info(f"Verified text memory: {stored}")
+            else:
+                logger.warning(f"Unexpected memory format: {mem}")
+    else:
+        logger.warning("No valid memories were stored from the text file")
+
+    # Test pdf file
+    logger.info(f"Testing pdf file: {pdf_file}")
+    pdf_memory = knowledge.add(pdf_file, user_id="bob", metadata={"type": "pdf"})
+    logger.info(f"Stored pdf file: {pdf_memory}")
+    print("\nStored pdf content:")
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for mem in pdf_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the PDF file")
+
+    # Verify stored content
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for memory in pdf_memory['results']:
+            if isinstance(memory, dict) and 'id' in memory:
+                stored_memory = knowledge.get(memory['id'])
+                logger.info(f"Verified stored memory: {stored_memory}")
+                print("\nVerified content:")
+                print(stored_memory)
+            else:
+                logger.warning(f"Unexpected memory format: {memory}")
+    else:
+        logger.warning("No valid memories were stored from the PDF file")
+
+except Exception as e:
+    logger.error(f"Error during file handling test: {str(e)}")
+    raise
+

agents/knowledge-large-pdf-test.py

@@ -0,0 +1,40 @@
+import logging
+from praisonaiagents.knowledge import Knowledge
+import os
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+knowledge = Knowledge()
+try:
+    # Get absolute path to test files
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    pdf_file = os.path.join(current_dir, "large.pdf")
+
+    # Test pdf file
+    logger.info(f"Testing pdf file: {pdf_file}")
+    pdf_memory = knowledge.add(pdf_file, user_id="bob", metadata={"type": "pdf"})
+    logger.info(f"Stored pdf file: {pdf_memory}")
+    print("\nStored pdf content:")
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for mem in pdf_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the PDF file")
+
+    # Verify stored content
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for memory in pdf_memory['results']:
+            if isinstance(memory, dict) and 'id' in memory:
+                stored_memory = knowledge.get(memory['id'])
+                logger.info(f"Verified stored memory: {stored_memory}")
+                print("\nVerified content:")
+                print(stored_memory)
+            else:
+                logger.warning(f"Unexpected memory format: {memory}")
+    else:
+        logger.warning("No valid memories were stored from the PDF file")
+
+except Exception as e:
+    logger.error(f"Error during file handling test: {str(e)}")
+    raise

agents/markdown-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

agents/md.py

@@ -0,0 +1,5 @@
+from markitdown import MarkItDown
+
+md = MarkItDown()
+result = md.convert("sample.txt")
+print(result.text_content)

agents/memory_example.py

@@ -0,0 +1,183 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+def main():
+    # Initialize memory config
+    memory_config = {
+        "provider": "rag",
+        "use_embedding": True,
+        "storage": {
+            "type": "sqlite",
+            "path": "./.praison/memory.db"
+        },
+        "rag_db_path": "./.praison/chroma_db"
+    }
+    
+    # Test facts
+    fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+    fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+    fact3 = "The year the first Josinga was released is 2007"
+    
+    # # Check if database exists
+    # if os.path.exists("./memory.db"):
+    #     logger.info("Found existing memory database")
+    # else:
+    #     logger.info("Creating new memory database")
+    
+    # Create task config (without memory config since it's moved to PraisonAIAgents)
+    task_config = {}
+
+    # Create agents with different roles
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and document key information about topics",
+        backstory="Expert at analyzing and storing information in memory",
+        llm="gpt-4o-mini"
+    )
+    
+    retriever = Agent(
+        role="Information Retriever",
+        goal="Retrieve and verify stored information from memory",
+        backstory="Specialist in searching and validating information from memory",
+        llm="gpt-4o-mini"
+    )
+
+    # Task 1: Process the facts
+    store_task = Task(
+        description=f"""
+        Process and analyze this information:
+        1. {fact1}
+        2. {fact2}
+        3. {fact3}
+
+        Provide a clear summary of each fact.
+        """,
+        expected_output="""
+        Clear statements summarizing each fact.
+        Example format:
+        1. [Summary of fact 1]
+        2. [Summary of fact 2]
+        3. [Summary of fact 3]
+        """,
+        agent=researcher
+    )
+
+    # Task 2: Write essay about AI
+    verify_task = Task(
+        description="""
+        write few points about AI
+        """,
+        expected_output="Points about AI",
+        agent=retriever
+    )
+
+    # Task 3: Query memory
+    query_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in memory about Hahanu?
+        2. What ingredients for proloder are recorded in memory?
+        3. What year is stored in memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Task 4: Query both short-term and long-term memory
+    query_both_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in both short-term and long-term memory about Jujuha?
+        2. What ingredients for proloder are recorded in both short-term and long-term memory?
+        3. What year is stored in both short-term and long-term memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Initialize PraisonAIAgents with memory configuration
+    agents = PraisonAIAgents(
+        agents=[researcher, retriever],
+        tasks=[store_task, verify_task, query_task, query_both_task],
+        verbose=True,  # Use same verbose level as memory
+        memory=True,
+        embedder={
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small"
+            }
+        }
+    )
+
+    # agents = PraisonAIAgents(
+    #     agents=[researcher, retriever],
+    #     tasks=[store_task, verify_task, query_task, query_both_task],
+    #     verbose=True,  # Use same verbose level as memory
+    #     memory=True
+    # )
+    
+    # Execute tasks
+    print("\nExecuting Memory Test Tasks...")
+    print("-" * 50)
+    agents.start()
+    
+    # Use shared memory for final checks
+    memory = agents.shared_memory
+    
+    # Test memory retrieval with different quality thresholds
+    if memory:
+        print("\nFinal Memory Check:")
+        print("-" * 50)
+        
+        queries = ["Jujuha", "proloder", "Josinga"]
+        for query in queries:
+            print(f"\nSearching memory for: {query}")
+            
+            # Search in both short-term and long-term memory
+            print("\nShort-term memory results:")
+            stm_results = memory.search_short_term(query)
+            if stm_results:
+                for item in stm_results:
+                    print(f"Content: {item.get('content', '')[:200]}")
+                    if 'meta' in item:
+                        print(f"Metadata: {item['meta']}")
+                    print("-" * 20)
+            else:
+                print("No results found in short-term memory")
+
+            print("\nLong-term memory results:")
+            ltm_results = memory.search_long_term(query)
+            if ltm_results:
+                for item in ltm_results:
+                    print(f"Content: {item.get('text', '')[:200]}")
+                    if 'metadata' in item:
+                        print(f"Metadata: {item['metadata']}")
+                    print("-" * 20)
+            else:
+                print("No results found in long-term memory")
+                
+            # Also check ChromaDB if using RAG
+            if memory.use_rag and hasattr(memory, "chroma_col"):
+                print("\nChromaDB results:")
+                try:
+                    all_items = memory.chroma_col.get()
+                    print(f"Found {len(all_items['ids'])} items in ChromaDB")
+                    for i in range(len(all_items['ids'])):
+                        print(f"ID: {all_items['ids'][i]}")
+                        print(f"Content: {all_items['documents'][i][:200]}")
+                        print(f"Metadata: {all_items['metadatas'][i]}")
+                        print("-" * 20)
+                except Exception as e:
+                    print(f"Error querying ChromaDB: {e}")
+            
+            print("-" * 30)
+    else:
+        print("\nNo memory available for final checks")
+
+if __name__ == "__main__":
+    main()

agents/memory_simple.py

@@ -0,0 +1,63 @@
+from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+
+# Test facts
+fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+fact3 = "The year the first Josinga was released is 2007"
+
+fact_agent = Agent(
+    name="Fact Agent",
+    instructions="You are a fact agent, you store and retrieve facts in memory",
+    llm="gpt-4o-mini"
+)
+
+research_agent = Agent(
+    name="Research Agent",
+    instructions="You are a research analyst, you research and document key points about topics",
+    llm="gpt-4o-mini"
+)
+
+blog_agent = Agent(
+    name="Blog Agent",
+    instructions="You are a blog writer, you write a blog post about the research",
+    llm="gpt-4o-mini"
+)
+
+fact_task = Task(
+    description="Store the following facts in memory: " + fact1 + ", " + fact2 + ", " + fact3,
+    agent=fact_agent
+)
+
+research_task = Task(
+    description="Research and document 2 key points about AI",
+    agent=research_agent
+)
+
+research_task2 = Task(
+    description="Research and document 2 key points about AI in healthcare",
+    agent=research_agent
+)
+
+research_task3 = Task(
+    description="Research and document 2 key points about AI in education",
+    agent=research_agent
+)
+
+research_task4 = Task(
+    description="Research and document 2 key points about AI in finance",
+    agent=research_agent
+)
+
+blog_task = Task(
+    description="Write a blog post about Jujuha",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[fact_agent, research_agent, blog_agent],
+    tasks=[fact_task, research_task, research_task2, research_task3, research_task4, blog_task],
+    memory=True
+)   
+
+agents.start()

agents/multimodal.py

@@ -51,9 +51,4 @@ agents = PraisonAIAgents(
 )
 
 # Run all tasks
-result = agents.start()
-
-# Print results
-for task_id, task_result in result["task_results"].items():
-    print(f"\nTask {task_id} Result:")
-    print(task_result.raw)
+agents.start()

agents/orchestrator-workers.py

@@ -0,0 +1,110 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    if current_time % 3 == 0:
+        result = 1
+    elif current_time % 3 == 1:
+        result = 2
+    else:
+        result = 3
+    print(f"Time: {current_time}, Result: {result}")
+    return result
+
+# Create orchestrator and worker agents
+router = Agent(
+    name="Router",
+    role="Task router",
+    goal="Distribute tasks to based on response from get_time_check",
+    tools=[get_time_check]
+)
+
+worker1 = Agent(
+    name="Worker 1",
+    role="Specialized worker",
+    goal="Handle specific subtask type 1",
+)
+
+worker2 = Agent(
+    name="Worker 2",
+    role="Specialized worker",
+    goal="Handle specific subtask type 2",
+)
+
+worker3 = Agent(
+    name="Worker 3",
+    role="Specialized worker",
+    goal="Handle specific subtask type 3",
+)
+
+synthesizer = Agent(
+    name="Synthesizer",
+    role="Result synthesizer",
+    goal="Combine and synthesize worker outputs",
+)
+
+# Create orchestrated workflow tasks
+router_task = Task(
+    name="route_task",
+    description="Analyze input from get_time_check and route to appropriate workers",
+    expected_output="Task routing decision, 1 , 2 or 3",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["worker1_task", "worker2_task", "worker3_task"],
+    condition={
+        "1": ["worker1_task"],
+        "2": ["worker2_task"],
+        "3": ["worker3_task"]
+    }
+)
+
+worker1_task = Task(
+    name="worker1_task",
+    description="Process type 1 operation",
+    expected_output="Worker 1 result",
+    agent=worker1,
+    next_tasks=["synthesize_task"]
+)
+
+worker2_task = Task(
+    name="worker2_task",
+    description="Process type 2 operation",
+    expected_output="Worker 2 result",
+    agent=worker2,
+    next_tasks=["synthesize_task"]
+)
+
+worker3_task = Task(
+    name="worker3_task",
+    description="Process type 3 operation",
+    expected_output="Worker 3 result",
+    agent=worker3,
+    next_tasks=["synthesize_task"]
+)
+
+synthesize_task = Task(
+    name="synthesize_task",
+    description="Synthesize worker results into final output",
+    expected_output="Final synthesized result",
+    agent=synthesizer,
+    context=[worker1_task, worker2_task, worker3_task]
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[router, worker1, worker2, worker3, synthesizer],
+    tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run orchestrated workflow
+results = workflow.start()
+
+# Print results
+print("\nOrchestrator-Workers Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/parallelisation.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from datetime import datetime
+import asyncio
+
+def process_time():
+    """Simulate processing"""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"Processing at: {current_time}")
+    return f"Processed at {current_time}"
+
+# Create parallel processing agents
+agent1 = Agent(
+    name="Processor 1",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent2 = Agent(
+    name="Processor 2",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent3 = Agent(
+    name="Processor 3",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+aggregator = Agent(
+    name="Aggregator",
+    role="Result aggregator",
+    goal="Collect all the processed time from all tasks"
+)
+
+# Create parallel tasks with memory disabled
+task1 = Task(
+    name="process_1",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent1,
+    is_start=True,
+    async_execution=True
+)
+
+task2 = Task(
+    name="process_2",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent2,
+    is_start=True,
+    async_execution=True
+)
+
+task3 = Task(
+    name="process_3",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent3,
+    is_start=True,
+    async_execution=True
+)
+
+aggregate_task = Task(
+    name="aggregate",
+    description="Collect all the processed time from all tasks",
+    expected_output="Output all the processed time from all tasks and just the time",
+    agent=aggregator,
+    context=[task1, task2, task3]
+)
+
+async def main():
+
+    # Create workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3, aggregator],
+        tasks=[task1, task2, task3, aggregate_task],
+        process="workflow"
+    )
+
+    # Run parallel workflow
+    results = await workflow.astart()
+
+    # Print results
+    print("\nParallel Processing Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())

agents/pdf_agents_example.py

@@ -0,0 +1,41 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create PDF Analysis Agent
+pdf_agent = Agent(
+    name="PDFAnalyst",
+    role="PDF Document Specialist",
+    goal="Analyze PDF documents to extract meaningful information",
+    backstory="""You are an expert in PDF document analysis and text extraction.
+    You excel at understanding document structure, extracting content, and analyzing textual information.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with PDF URL
+task1 = Task(
+    name="analyze_pdf_url",
+    description="Extract and analyze content from this PDF document.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["https://example.com/document.pdf"]
+)
+
+# 2. Task with Local PDF File
+task2 = Task(
+    name="analyze_local_pdf",
+    description="What information can you extract from this PDF? Analyze its content.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["document.pdf"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[pdf_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/planning-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")

agents/praisonaiagents/__init__.py

@@ -7,6 +7,9 @@ from .agents.agents import PraisonAIAgents
 from .task.task import Task
 from .tools.tools import Tools
 from .agents.autoagents import AutoAgents
+from .knowledge.knowledge import Knowledge
+from .knowledge.chunking import Chunking
+from .memory.memory import Memory
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -35,6 +38,7 @@ __all__ = [
     'TaskOutput',
     'ReflectionOutput',
     'AutoAgents',
+    'Memory',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -46,4 +50,6 @@ __all__ = [
     'register_display_callback',
     'sync_display_callbacks',
     'async_display_callbacks',
+    'Knowledge',
+    'Chunking'
 ] 

agents/praisonaiagents/agent/agent.py

@@ -16,9 +16,11 @@ from ..main import (
     display_self_reflection,
     ReflectionOutput,
     client,
-    error_logs
+    error_logs,
+    adisplay_instruction
 )
 import inspect
+import uuid
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -162,7 +164,7 @@ class Agent:
         max_iter: int = 20,
         max_rpm: Optional[int] = None,
         max_execution_time: Optional[int] = None,
-        memory: bool = True,
+        memory: Optional[Any] = None,
         verbose: bool = True,
         allow_delegation: bool = False,
         step_callback: Optional[Any] = None,
@@ -175,13 +177,15 @@ class Agent:
         respect_context_window: bool = True,
         code_execution_mode: Literal["safe", "unsafe"] = "safe",
         embedder_config: Optional[Dict[str, Any]] = None,
-        knowledge_sources: Optional[List[Any]] = None,
+        knowledge: Optional[List[str]] = None,
+        knowledge_config: Optional[Dict[str, Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
-        self_reflect: Optional[bool] = None,
+        self_reflect: bool = False,
         max_reflect: int = 3,
         min_reflect: int = 1,
-        reflect_llm: Optional[str] = None
+        reflect_llm: Optional[str] = None,
+        user_id: Optional[str] = None
     ):
         # Handle backward compatibility for required fields
         if all(x is None for x in [name, role, goal, backstory, instructions]):
@@ -225,7 +229,7 @@ class Agent:
         self.respect_context_window = respect_context_window
         self.code_execution_mode = code_execution_mode
         self.embedder_config = embedder_config
-        self.knowledge_sources = knowledge_sources
+        self.knowledge = knowledge
         self.use_system_prompt = use_system_prompt
         self.chat_history = []
         self.markdown = markdown
@@ -241,6 +245,36 @@ Your Role: {self.role}\n
 Your Goal: {self.goal}
         """
 
+        # Generate unique IDs
+        self.agent_id = str(uuid.uuid4())
+
+        # Store user_id
+        self.user_id = user_id
+
+        # Initialize Knowledge with provided or default config
+        from praisonaiagents.knowledge import Knowledge
+        self.knowledge = Knowledge(knowledge_config or None)
+
+        # Handle knowledge
+        if knowledge:
+            for source in knowledge:
+                self._process_knowledge(source)
+
+    def _process_knowledge(self, knowledge_item):
+        """Process and store knowledge from a file path, URL, or string."""
+        try:
+            if os.path.exists(knowledge_item):
+                # It's a file path
+                self.knowledge.add(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+            elif knowledge_item.startswith("http://") or knowledge_item.startswith("https://"):
+                # It's a URL
+                pass
+            else:
+                # It's a string content
+                self.knowledge.store(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+        except Exception as e:
+            logging.error(f"Error processing knowledge item: {knowledge_item}, error: {e}")
+
     def generate_task(self) -> 'Task':
         """Generate a Task object from the agent's instructions"""
         from ..task.task import Task
@@ -381,7 +415,7 @@ Your Goal: {self.goal}
                     display_generating("", start_time),
                     console=self.console,
                     refresh_per_second=4,
-                    transient=False,  # Changed to False to preserve output
+                    transient=True,  # Changed to False to preserve output
                     vertical_overflow="ellipsis",
                     auto_refresh=True
                 ) as live:
@@ -417,6 +451,21 @@ Your Goal: {self.goal}
             return None
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        # Search for existing knowledge if any knowledge is provided
+        if self.knowledge:
+            search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+            if search_results:
+                # Check if search_results is a list of dictionaries or strings
+                if isinstance(search_results, dict) and 'results' in search_results:
+                    # Extract memory content from the results
+                    knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                else:
+                    # If search_results is a list of strings, join them directly
+                    knowledge_content = "\n".join(search_results)
+                
+                # Append found knowledge to the prompt
+                prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
+
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n
@@ -466,7 +515,15 @@ Your Goal: {self.goal}
                         display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
                     
                     if display_text and str(display_text).strip():
-                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
+                        # Pass agent information to display_instruction
+                        agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                        display_instruction(
+                            f"Agent {self.name} is processing prompt: {display_text}", 
+                            console=self.console,
+                            agent_name=self.name,
+                            agent_role=self.role,
+                            agent_tools=agent_tools
+                        )
 
                 response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                 if not response:
@@ -525,7 +582,7 @@ Your Goal: {self.goal}
                     self.chat_history.append({"role": "user", "content": original_prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
                     if self.verbose:
-                        logging.info(f"Agent {self.name} final response: {response_text}")
+                        logging.debug(f"Agent {self.name} final response: {response_text}")
                     display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                     return response_text
 
@@ -630,6 +687,22 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
                             break
 
+            # Display instruction with agent info if verbose
+            if self.verbose:
+                display_text = prompt
+                if isinstance(prompt, list):
+                    display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                
+                if display_text and str(display_text).strip():
+                    agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                    await adisplay_instruction(
+                        f"Agent {self.name} is processing prompt: {display_text}",
+                        console=self.console,
+                        agent_name=self.name,
+                        agent_role=self.role,
+                        agent_tools=agent_tools
+                    )
+
             # Format tools if provided
             formatted_tools = []
             if tools:
@@ -737,4 +810,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
     def run(self):
         """Alias for start() method"""
-        return self.start() 
+        return self.start() 
+
+    def start(self, prompt: str, **kwargs):
+        """Start the agent with a prompt. This is a convenience method that wraps chat()."""
+        return self.chat(prompt, **kwargs) 

agents/praisonaiagents/agents/agents.py

@@ -12,6 +12,10 @@ from ..agent.agent import Agent
 from ..task.task import Task
 from ..process.process import Process, LoopItems
 import asyncio
+import uuid
+
+# Set up logger
+logger = logging.getLogger(__name__)
 
 def encode_file_to_base64(file_path: str) -> str:
     """Base64-encode a file."""
@@ -41,10 +45,17 @@ def process_video(video_path: str, seconds_per_frame=2):
     return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None, user_id=None):
         if not agents:
             raise ValueError("At least one agent must be provided")
-            
+        
+        self.run_id = str(uuid.uuid4())  # Auto-generate run_id
+        self.user_id = user_id  # Optional user_id
+
+        # Pass user_id to each agent
+        for agent in agents:
+            agent.user_id = self.user_id
+
         self.agents = agents
         self.tasks = {}
         if max_retries < 3:
@@ -57,8 +68,21 @@ class PraisonAIAgents:
         
         # Check for manager_llm in environment variable if not provided
         self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+
         if self.verbose:
-            logging.info(f"Using model {self.manager_llm} for manager")
+            logger.info(f"Using model {self.manager_llm} for manager")
         
         # If no tasks provided, generate them from agents
         if tasks is None:
@@ -66,12 +90,12 @@ class PraisonAIAgents:
             for agent in self.agents:
                 task = agent.generate_task()
                 tasks.append(task)
-            logging.info(f"Auto-generated {len(tasks)} tasks from agents")
+            logger.info(f"Auto-generated {len(tasks)} tasks from agents")
         else:
             # Validate tasks for backward compatibility
             if not tasks:
                 raise ValueError("If tasks are provided, at least one task must be present")
-            logging.info(f"Using {len(tasks)} provided tasks")
+            logger.info(f"Using {len(tasks)} provided tasks")
         
         # Add tasks and set their status
         for task in tasks:
@@ -87,9 +111,65 @@ class PraisonAIAgents:
                 if tasks[i + 1].context is None:
                     tasks[i + 1].context = []
                 tasks[i + 1].context.append(tasks[i])
-            logging.info("Set up sequential flow with automatic context passing")
+            logger.info("Set up sequential flow with automatic context passing")
         
         self._state = {}  # Add state storage at PraisonAIAgents level
+        
+        # Initialize memory system
+        self.shared_memory = None
+        if memory:
+            try:
+                from ..memory.memory import Memory
+                
+                # Get memory config from parameter or first task
+                mem_cfg = memory_config
+                if not mem_cfg:
+                    mem_cfg = next((t.config.get('memory_config') for t in tasks if hasattr(t, 'config') and t.config), None)
+                    
+                # Set default memory config if none provided
+                if not mem_cfg:
+                    mem_cfg = {
+                        "provider": "rag",
+                        "use_embedding": True,
+                        "storage": {
+                            "type": "sqlite",
+                            "path": "./.praison/memory.db"
+                        },
+                        "rag_db_path": "./.praison/chroma_db"
+                    }
+                
+                # Add embedder config if provided
+                if embedder:
+                    if isinstance(embedder, dict):
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder"] = embedder
+                    else:
+                        # Handle direct embedder function
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder_function"] = embedder
+
+                if mem_cfg:
+                    # Pass verbose level to Memory
+                    self.shared_memory = Memory(config=mem_cfg, verbose=verbose)
+                    if verbose >= 5:
+                        logger.info("Initialized shared memory for PraisonAIAgents")
+                    
+                    # Distribute memory to tasks
+                    for task in tasks:
+                        if not task.memory:
+                            task.memory = self.shared_memory
+                            if verbose >= 5:
+                                logger.info(f"Assigned shared memory to task {task.id}")
+                            
+            except Exception as e:
+                logger.error(f"Failed to initialize shared memory: {e}")
+            
+        # Update tasks with shared memory
+        if self.shared_memory:
+            for task in tasks:
+                if not task.memory:
+                    task.memory = self.shared_memory
+                    logger.info(f"Assigned shared memory to task {task.id}")
 
     def add_task(self, task):
         task_id = self.task_id_counter
@@ -139,10 +219,15 @@ class PraisonAIAgents:
 
         executor_agent = task.agent
 
+        # Ensure tools are available from both task and agent
+        tools = task.tools or []
+        if executor_agent and executor_agent.tools:
+            tools.extend(executor_agent.tools)
+
         task_prompt = f"""
 You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
-        """
+"""
         if task.context:
             context_results = ""
             for context_task in task.context:
@@ -157,8 +242,8 @@ Here are the results of previous tasks that might be useful:\n
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
         if self.verbose >= 2:
-            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
             def _get_multimodal_message(text_prompt, images):
@@ -195,14 +280,14 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = await executor_agent.achat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools,
+                tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
         else:
             agent_output = await executor_agent.achat(
                 task_prompt,
-                tools=task.tools,
+                tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
@@ -223,8 +308,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.json_dict = parsed
                     task_output.output_format = "JSON"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
 
             if task.output_pydantic:
                 cleaned = self.clean_json_output(agent_output)
@@ -234,8 +319,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.pydantic = pyd_obj
                     task_output.output_format = "Pydantic"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
 
             task.result = task_output
             return task_output
@@ -250,37 +335,53 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         if task.status == "completed":
-            logging.info(f"Task with ID {task_id} is already completed")
+            logger.info(f"Task with ID {task_id} is already completed")
             return
 
         retries = 0
         while task.status != "completed" and retries < self.max_retries:
-            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
             if task.status in ["not started", "in progress"]:
                 task_output = await self.aexecute_task(task_id)
                 if task_output and self.completion_checker(task, task_output.raw):
                     task.status = "completed"
-                    if task.callback:
+                    # Run execute_callback for memory operations
+                    try:
                         await task.execute_callback(task_output)
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
+                    if task.callback:
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                await task.callback(task_output)
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
                     self.save_output_to_file(task, task_output)
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} completed successfully.")
+                        logger.info(f"Task {task_id} completed successfully.")
                 else:
                     task.status = "in progress"
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} not completed, retrying")
+                        logger.info(f"Task {task_id} not completed, retrying")
                     await asyncio.sleep(1)
                     retries += 1
             else:
                 if task.status == "failed":
-                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    logger.info("Task is failed, resetting to in-progress for another try...")
                     task.status = "in progress"
                 else:
-                    logging.info("Invalid Task status")
+                    logger.info("Invalid Task status")
                     break
 
         if retries == self.max_retries and task.status != "completed":
-            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     async def arun_all_tasks(self):
         """Async version of run_all_tasks method"""
@@ -292,11 +393,25 @@ Here are the results of previous tasks that might be useful:\n
         )
         
         if self.process == "workflow":
+            # Collect all tasks that should run in parallel
+            parallel_tasks = []
             async for task_id in process.aworkflow():
-                if self.tasks[task_id].async_execution:
-                    await self.arun_task(task_id)
-                else:
-                    self.run_task(task_id)
+                if self.tasks[task_id].async_execution and self.tasks[task_id].is_start:
+                    parallel_tasks.append(task_id)
+                elif parallel_tasks:
+                    # Execute collected parallel tasks
+                    await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                    parallel_tasks = []
+                    # Run the current non-parallel task
+                    if self.tasks[task_id].async_execution:
+                        await self.arun_task(task_id)
+                    else:
+                        self.run_task(task_id)
+            
+            # Execute any remaining parallel tasks
+            if parallel_tasks:
+                await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                
         elif self.process == "sequential":
             async for task_id in process.asequential():
                 if self.tasks[task_id].async_execution:
@@ -328,7 +443,7 @@ Here are the results of previous tasks that might be useful:\n
                 with open(task.output_file, "w") as f:
                     f.write(str(task_output))
                 if self.verbose >= 1:
-                    logging.info(f"Task output saved to {task.output_file}")
+                    logger.info(f"Task output saved to {task.output_file}")
             except Exception as e:
                 display_error(f"Error saving task output to file: {e}")
 
@@ -339,6 +454,15 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         
+        logger.info(f"Starting execution of task {task_id}")
+        logger.info(f"Task config: {task.config}")
+        
+        # Initialize memory before task execution
+        if not task.memory:
+            task.memory = task.initialize_memory()
+        
+        logger.info(f"Task memory status: {'Initialized' if task.memory else 'Not initialized'}")
+        
         # Only import multimodal dependencies if task has images
         if task.images and task.status == "not started":
             try:
@@ -371,11 +495,20 @@ Expected Output: {task.expected_output}.
 Here are the results of previous tasks that might be useful:\n
 {context_results}
 """
+        # Add memory context if available
+        if task.memory:
+            try:
+                memory_context = task.memory.build_context_for_task(task.description)
+                if memory_context:
+                    task_prompt += f"\n\nRelevant memory context:\n{memory_context}"
+            except Exception as e:
+                logger.error(f"Error getting memory context: {e}")
+
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
         if self.verbose >= 2:
-            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
 
         if task.images:
             def _get_multimodal_message(text_prompt, images):
@@ -425,6 +558,17 @@ Here are the results of previous tasks that might be useful:\n
             )
 
         if agent_output:
+            # Store the response in memory
+            if task.memory:
+                try:
+                    task.store_in_memory(
+                        content=agent_output,
+                        agent_name=executor_agent.name,
+                        task_id=task_id
+                    )
+                except Exception as e:
+                    logger.error(f"Failed to store agent output in memory: {e}")
+
             task_output = TaskOutput(
                 description=task.description,
                 summary=task.description[:10],
@@ -440,8 +584,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.json_dict = parsed
                     task_output.output_format = "JSON"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
 
             if task.output_pydantic:
                 cleaned = self.clean_json_output(agent_output)
@@ -451,8 +595,8 @@ Here are the results of previous tasks that might be useful:\n
                     task_output.pydantic = pyd_obj
                     task_output.output_format = "Pydantic"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
 
             task.result = task_output
             return task_output
@@ -467,37 +611,63 @@ Here are the results of previous tasks that might be useful:\n
             return
         task = self.tasks[task_id]
         if task.status == "completed":
-            logging.info(f"Task with ID {task_id} is already completed")
+            logger.info(f"Task with ID {task_id} is already completed")
             return
 
         retries = 0
         while task.status != "completed" and retries < self.max_retries:
-            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
             if task.status in ["not started", "in progress"]:
                 task_output = self.execute_task(task_id)
                 if task_output and self.completion_checker(task, task_output.raw):
                     task.status = "completed"
+                    # Run execute_callback for memory operations
+                    try:
+                        if asyncio.get_event_loop().is_running():
+                            asyncio.create_task(task.execute_callback(task_output))
+                        else:
+                            loop = asyncio.new_event_loop()
+                            asyncio.set_event_loop(loop)
+                            loop.run_until_complete(task.execute_callback(task_output))
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
                     if task.callback:
-                        task.callback(task_output)
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                if asyncio.get_event_loop().is_running():
+                                    asyncio.create_task(task.callback(task_output))
+                                else:
+                                    loop = asyncio.new_event_loop()
+                                    asyncio.set_event_loop(loop)
+                                    loop.run_until_complete(task.callback(task_output))
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
                     self.save_output_to_file(task, task_output)
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} completed successfully.")
+                        logger.info(f"Task {task_id} completed successfully.")
                 else:
                     task.status = "in progress"
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} not completed, retrying")
+                        logger.info(f"Task {task_id} not completed, retrying")
                     time.sleep(1)
                     retries += 1
             else:
                 if task.status == "failed":
-                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    logger.info("Task is failed, resetting to in-progress for another try...")
                     task.status = "in progress"
                 else:
-                    logging.info("Invalid Task status")
+                    logger.info("Invalid Task status")
                     break
 
         if retries == self.max_retries and task.status != "completed":
-            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     def run_all_tasks(self):
         """Synchronous version of run_all_tasks method"""

agents/praisonaiagents/knowledge/__init__.py

@@ -0,0 +1,8 @@
+"""
+PraisonAI Knowledge - Advanced knowledge management system with configurable features
+"""
+
+from praisonaiagents.knowledge.knowledge import Knowledge
+from praisonaiagents.knowledge.chunking import Chunking
+
+__all__ = ["Knowledge", "Chunking"] 

agents/praisonaiagents/knowledge/chunking.py

@@ -0,0 +1,182 @@
+from typing import List, Union, Optional, Dict, Any
+from functools import cached_property
+import importlib
+
+class Chunking:
+    """A unified class for text chunking with various chunking strategies."""
+    
+    CHUNKER_PARAMS = {
+        'token': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'word': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'sentence': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'semantic': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'sdpm': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'late': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'recursive': ['chunk_size', 'tokenizer']
+    }
+    
+    @cached_property
+    def SUPPORTED_CHUNKERS(self) -> Dict[str, Any]:
+        """Lazy load chunker classes."""
+        try:
+            from chonkie.chunker import (
+                TokenChunker,
+                WordChunker,
+                SentenceChunker,
+                SemanticChunker,
+                SDPMChunker,
+                LateChunker,
+                RecursiveChunker
+            )
+        except ImportError:
+            raise ImportError(
+                "chonkie package not found. Please install it using: pip install 'praisonaiagents[knowledge]'"
+            )
+            
+        return {
+            'token': TokenChunker,
+            'word': WordChunker,
+            'sentence': SentenceChunker,
+            'semantic': SemanticChunker,
+            'sdpm': SDPMChunker,
+            'late': LateChunker,
+            'recursive': RecursiveChunker
+        }
+
+    def __init__(
+        self,
+        chunker_type: str = 'token',
+        chunk_size: int = 512,
+        chunk_overlap: int = 128,
+        tokenizer: str = "gpt2",
+        embedding_model: Optional[Union[str, Any]] = None,
+        **kwargs
+    ):
+        """Initialize the Chunking class."""
+        if chunker_type not in self.CHUNKER_PARAMS:
+            raise ValueError(
+                f"Unsupported chunker type: {chunker_type}. "
+                f"Must be one of: {list(self.CHUNKER_PARAMS.keys())}"
+            )
+        
+        self.chunker_type = chunker_type
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.tokenizer = tokenizer
+        self._embedding_model = embedding_model
+        self.kwargs = kwargs
+        
+        # Initialize these as None for lazy loading
+        self._chunker = None
+        self._embeddings = None
+        
+    @cached_property
+    def embedding_model(self):
+        """Lazy load the embedding model."""
+        if self._embedding_model is None and self.chunker_type in ['semantic', 'sdpm', 'late']:
+            from chonkie.embeddings import AutoEmbeddings
+            return AutoEmbeddings.get_embeddings("all-MiniLM-L6-v2")
+        elif isinstance(self._embedding_model, str):
+            from chonkie.embeddings import AutoEmbeddings
+            return AutoEmbeddings.get_embeddings(self._embedding_model)
+        return self._embedding_model
+
+    def _get_chunker_params(self) -> Dict[str, Any]:
+        """Get the appropriate parameters for the current chunker type."""
+        allowed_params = self.CHUNKER_PARAMS[self.chunker_type]
+        params = {'chunk_size': self.chunk_size}
+        
+        if 'chunk_overlap' in allowed_params:
+            params['chunk_overlap'] = self.chunk_overlap
+            
+        if 'tokenizer' in allowed_params:
+            if self.chunker_type in ['semantic', 'sdpm', 'late']:
+                params['tokenizer'] = self.embedding_model.get_tokenizer_or_token_counter()
+            else:
+                params['tokenizer'] = self.tokenizer
+                
+        if 'embedding_model' in allowed_params:
+            params['embedding_model'] = self.embedding_model
+            
+        # Add any additional kwargs that are in allowed_params
+        for key, value in self.kwargs.items():
+            if key in allowed_params:
+                params[key] = value
+                
+        return params
+
+    @cached_property
+    def chunker(self):
+        """Lazy load the chunker instance."""
+        if self._chunker is None:
+            chunker_cls = self.SUPPORTED_CHUNKERS[self.chunker_type]
+            common_params = self._get_chunker_params()
+            self._chunker = chunker_cls(**common_params)
+        
+        return self._chunker
+    
+    def _get_overlap_refinery(self, context_size: Optional[int] = None, **kwargs):
+        """Lazy load the overlap refinery."""
+        try:
+            from chonkie.refinery import OverlapRefinery
+        except ImportError:
+            raise ImportError("Failed to import OverlapRefinery from chonkie.refinery")
+            
+        if context_size is None:
+            context_size = self.chunk_overlap
+            
+        return OverlapRefinery(
+            context_size=context_size,
+            tokenizer=self.chunker.tokenizer,
+            **kwargs
+        )
+    
+    def add_overlap_context(
+        self,
+        chunks: List[Any],
+        context_size: int = None,
+        mode: str = "suffix",
+        merge_context: bool = True
+    ) -> List[Any]:
+        """Add overlap context to chunks using OverlapRefinery."""
+        refinery = self._get_overlap_refinery(
+            context_size=context_size,
+            mode=mode,
+            merge_context=merge_context
+        )
+        return refinery.refine(chunks)
+    
+    def chunk(
+        self,
+        text: Union[str, List[str]],
+        add_context: bool = False,
+        context_params: Optional[Dict[str, Any]] = None
+    ) -> Union[List[Any], List[List[Any]]]:
+        """Chunk text using the configured chunking strategy."""
+        chunks = self.chunker(text)
+        
+        if add_context:
+            context_params = context_params or {}
+            if isinstance(text, str):
+                chunks = self.add_overlap_context(chunks, **context_params)
+            else:
+                chunks = [self.add_overlap_context(c, **context_params) for c in chunks]
+                
+        return chunks
+    
+    def __call__(
+        self,
+        text: Union[str, List[str]],
+        add_context: bool = False,
+        context_params: Optional[Dict[str, Any]] = None
+    ) -> Union[List[Any], List[List[Any]]]:
+        """Make the Chunking instance callable."""
+        return self.chunk(text, add_context, context_params)
+    
+    def __repr__(self) -> str:
+        """String representation of the Chunking instance."""
+        return (
+            f"Chunking(chunker_type='{self.chunker_type}', "
+            f"chunk_size={self.chunk_size}, "
+            f"chunk_overlap={self.chunk_overlap})"
+        )

agents/praisonaiagents/knowledge/knowledge.py

@@ -0,0 +1,303 @@
+import os
+import logging
+import uuid
+import time
+from .chunking import Chunking
+from functools import cached_property
+from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn
+
+logger = logging.getLogger(__name__)
+
+class CustomMemory:
+    @classmethod
+    def from_config(cls, config):
+        from mem0 import Memory
+        return type('CustomMemory', (Memory,), {
+            '_add_to_vector_store': cls._add_to_vector_store
+        }).from_config(config)
+
+    @staticmethod
+    def _add_to_vector_store(self, messages, metadata, filters):
+        # Custom implementation that doesn't use LLM
+        parsed_messages = "\n".join([msg["content"] for msg in messages])
+        
+        # Create a simple fact without using LLM
+        new_retrieved_facts = [parsed_messages]
+        
+        # Process embeddings and continue with vector store operations
+        new_message_embeddings = {}
+        for new_mem in new_retrieved_facts:
+            messages_embeddings = self.embedding_model.embed(new_mem)
+            new_message_embeddings[new_mem] = messages_embeddings
+            
+        # Create the memory
+        memory_id = self._create_memory(
+            data=parsed_messages,
+            existing_embeddings=new_message_embeddings,
+            metadata=metadata
+        )
+        
+        return [{
+            "id": memory_id,
+            "memory": parsed_messages,
+            "event": "ADD"
+        }]
+
+class Knowledge:
+    def __init__(self, config=None, verbose=None):
+        self._config = config
+        self._verbose = verbose or 0
+        os.environ['ANONYMIZED_TELEMETRY'] = 'False'  # Chromadb
+        
+        # Configure logging levels based on verbose setting
+        if not self._verbose:
+            # Suppress logs from all relevant dependencies
+            for logger_name in [
+                'mem0', 
+                'chromadb', 
+                'local_persistent_hnsw',
+                '_client',
+                'main'
+            ]:
+                logging.getLogger(logger_name).setLevel(logging.WARNING)
+            
+            # Disable OpenAI API request logging
+            logging.getLogger('openai').setLevel(logging.WARNING)
+            
+            # Set root logger to warning to catch any uncategorized logs
+            logging.getLogger().setLevel(logging.WARNING)
+
+    @cached_property
+    def _deps(self):
+        try:
+            from markitdown import MarkItDown
+            import chromadb
+            return {
+                'chromadb': chromadb,
+                'markdown': MarkItDown()
+            }
+        except ImportError:
+            raise ImportError(
+                "Required packages not installed. Please install using: "
+                'pip install "praisonaiagents[knowledge]"'
+            )
+
+    @cached_property
+    def config(self):
+        # Generate unique collection name for each instance
+        collection_name = f"test_{int(time.time())}_{str(uuid.uuid4())[:8]}"
+        persist_dir = ".praison"
+
+        # Create persistent client config
+        base_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": collection_name,
+                    "path": persist_dir,
+                    "client": self._deps['chromadb'].PersistentClient(path=persist_dir)
+                }
+            },
+            "version": "v1.1",
+            "custom_prompt": "Return {{\"facts\": [text]}} where text is the exact input provided and json response"
+        }
+
+        # If config is provided, merge it with base config
+        if self._config:
+            if "vector_store" in self._config and "config" in self._config["vector_store"]:
+                config_copy = self._config["vector_store"]["config"].copy()
+                for key in ["collection_name", "client"]:
+                    if key in config_copy:
+                        del config_copy[key]
+                base_config["vector_store"]["config"].update(config_copy)
+
+        return base_config
+
+    @cached_property
+    def memory(self):
+        try:
+            return CustomMemory.from_config(self.config)
+        except (NotImplementedError, ValueError) as e:
+            if "list_collections" in str(e) or "Extra fields not allowed" in str(e):
+                # Keep only allowed fields
+                vector_store_config = {
+                    "collection_name": self.config["vector_store"]["config"]["collection_name"],
+                    "path": self.config["vector_store"]["config"]["path"]
+                }
+                self.config["vector_store"]["config"] = vector_store_config
+                from mem0 import Memory
+                return Memory.from_config(self.config)
+            raise
+
+    @cached_property
+    def markdown(self):
+        return self._deps['markdown']
+
+    @cached_property
+    def chunker(self):
+        return Chunking(
+            chunker_type='recursive',
+            chunk_size=512,
+            chunk_overlap=50
+        )
+
+    def _log(self, message, level=2):
+        """Internal logging helper"""
+        if self._verbose and self._verbose >= level:
+            logger.info(message)
+
+    def store(self, content, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Store a memory."""
+        try:
+            if isinstance(content, str):
+                if any(content.lower().endswith(ext) for ext in ['.pdf', '.doc', '.docx', '.txt']):
+                    self._log(f"Content appears to be a file path, processing file: {content}")
+                    return self.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+                
+                content = content.strip()
+                if not content:
+                    return []
+                
+            result = self.memory.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+            self._log(f"Store operation result: {result}")
+            return result
+        except Exception as e:
+            logger.error(f"Error storing content: {str(e)}")
+            return []
+
+    def get_all(self, user_id=None, agent_id=None, run_id=None):
+        """Retrieve all memories."""
+        return self.memory.get_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def get(self, memory_id):
+        """Retrieve a specific memory by ID."""
+        return self.memory.get(memory_id)
+
+    def search(self, query, user_id=None, agent_id=None, run_id=None):
+        """Search for memories related to a query."""
+        return self.memory.search(query, user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def update(self, memory_id, data):
+        """Update a memory."""
+        return self.memory.update(memory_id, data)
+
+    def history(self, memory_id):
+        """Get the history of changes for a memory."""
+        return self.memory.history(memory_id)
+
+    def delete(self, memory_id):
+        """Delete a memory."""
+        self.memory.delete(memory_id)
+
+    def delete_all(self, user_id=None, agent_id=None, run_id=None):
+        """Delete all memories."""
+        self.memory.delete_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def reset(self):
+        """Reset all memories."""
+        self.memory.reset()
+
+    def normalize_content(self, content):
+        """Normalize content for consistent storage."""
+        # Example normalization: strip whitespace, convert to lowercase
+        return content.strip().lower()
+
+    def add(self, file_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Read file content and store it in memory.
+        
+        Args:
+            file_path: Can be:
+                - A string path to local file
+                - A URL string
+                - A list containing file paths and/or URLs
+        """
+        if isinstance(file_path, (list, tuple)):
+            results = []
+            for path in file_path:
+                result = self._process_single_input(path, user_id, agent_id, run_id, metadata)
+                results.extend(result.get('results', []))
+            return {'results': results, 'relations': []}
+        
+        return self._process_single_input(file_path, user_id, agent_id, run_id, metadata)
+
+    def _process_single_input(self, input_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Process a single input which can be a file path or URL."""
+        try:
+            # Define supported file extensions
+            DOCUMENT_EXTENSIONS = {
+                'document': ('.pdf', '.ppt', '.pptx', '.doc', '.docx', '.xls', '.xlsx'),
+                'media': ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.mp3', '.wav', '.ogg', '.m4a'),
+                'text': ('.txt', '.csv', '.json', '.xml', '.md', '.html', '.htm'),
+                'archive': '.zip'
+            }
+
+            # Check if input is URL
+            if isinstance(input_path, str) and (input_path.startswith('http://') or input_path.startswith('https://')):
+                self._log(f"Processing URL: {input_path}")
+                raise NotImplementedError("URL processing not yet implemented")
+
+            # Check if input ends with any supported extension
+            is_supported_file = any(input_path.lower().endswith(ext) 
+                                  for exts in DOCUMENT_EXTENSIONS.values()
+                                  for ext in (exts if isinstance(exts, tuple) else (exts,)))
+            
+            if is_supported_file:
+                self._log(f"Processing as file path: {input_path}")
+                if not os.path.exists(input_path):
+                    logger.error(f"File not found: {input_path}")
+                    raise FileNotFoundError(f"File not found: {input_path}")
+                
+                file_ext = '.' + input_path.lower().split('.')[-1]  # Get extension reliably
+                
+                # Process file based on type
+                if file_ext in DOCUMENT_EXTENSIONS['text']:
+                    with open(input_path, 'r', encoding='utf-8') as file:
+                        content = file.read().strip()
+                    if not content:
+                        raise ValueError("Empty text file")
+                    memories = [self.normalize_content(content)]
+                else:
+                    # Use MarkItDown for documents and media
+                    result = self.markdown.convert(input_path)
+                    content = result.text_content
+                    if not content:
+                        raise ValueError("No content could be extracted from file")
+                    chunks = self.chunker.chunk(content)
+                    memories = [chunk.text.strip() if hasattr(chunk, 'text') else str(chunk).strip() 
+                              for chunk in chunks if chunk]
+
+                # Set metadata for file
+                if not metadata:
+                    metadata = {}
+                metadata['file_type'] = file_ext.lstrip('.')
+                metadata['filename'] = os.path.basename(input_path)
+            else:
+                # Treat as raw text content only if no file extension
+                memories = [self.normalize_content(input_path)]
+
+            # Create progress display
+            progress = Progress(
+                SpinnerColumn(),
+                TextColumn("[progress.description]{task.description}"),
+                BarColumn(),
+                TaskProgressColumn(),
+                transient=True
+            )
+
+            # Store memories with progress bar
+            all_results = []
+            with progress:
+                store_task = progress.add_task(f"Adding to Knowledge from {os.path.basename(input_path)}", total=len(memories))
+                for memory in memories:
+                    if memory:
+                        memory_result = self.store(memory, user_id=user_id, agent_id=agent_id, 
+                                                 run_id=run_id, metadata=metadata)
+                        if memory_result:
+                            all_results.extend(memory_result.get('results', []))
+                        progress.advance(store_task)
+
+            return {'results': all_results, 'relations': []}
+
+        except Exception as e:
+            logger.error(f"Error processing input {input_path}: {str(e)}", exc_info=True)
+            raise

agents/praisonaiagents/main.py

@@ -116,10 +116,10 @@ def display_interaction(message, response, markdown=True, generation_time=None,
         console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
 
     if markdown:
-        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
         console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
     else:
-        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
         console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
 def display_self_reflection(message: str, console=None):
@@ -135,7 +135,7 @@ def display_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-def display_instruction(message: str, console=None):
+def display_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     if not message or not message.strip():
         return
     if console is None:
@@ -146,7 +146,19 @@ def display_instruction(message: str, console=None):
     if 'instruction' in sync_display_callbacks:
         sync_display_callbacks['instruction'](message=message)
     
-    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 def display_tool_call(message: str, console=None):
     if not message or not message.strip():
@@ -222,10 +234,10 @@ async def adisplay_interaction(message, response, markdown=True, generation_time
         console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
 
     if markdown:
-        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(message), title="Task", border_style="cyan"))
         console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
     else:
-        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(message, style="bold green"), title="Task", border_style="cyan"))
         console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
 async def adisplay_self_reflection(message: str, console=None):
@@ -241,7 +253,7 @@ async def adisplay_self_reflection(message: str, console=None):
     
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-async def adisplay_instruction(message: str, console=None):
+async def adisplay_instruction(message: str, console=None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None):
     """Async version of display_instruction."""
     if not message or not message.strip():
         return
@@ -252,7 +264,19 @@ async def adisplay_instruction(message: str, console=None):
     if 'instruction' in async_display_callbacks:
         await async_display_callbacks['instruction'](message=message)
     
-    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+    # Display agent info if available
+    if agent_name:
+        agent_info = f"[bold #FF9B9B]👤 Agent:[/] [#FFE5E5]{agent_name}[/]"
+        if agent_role:
+            agent_info += f"\n[bold #B4B4B3]Role:[/] [#FFE5E5]{agent_role}[/]"
+        if agent_tools:
+            tools_str = ", ".join(f"[italic #B4D4FF]{tool}[/]" for tool in agent_tools)
+            agent_info += f"\n[bold #86A789]Tools:[/] {tools_str}"
+        console.print(Panel(agent_info, border_style="#D2E3C8", title="[bold]Agent Info[/]", title_align="left", padding=(1, 2)))
+    
+    # Only print if log level is DEBUG
+    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+        console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 async def adisplay_tool_call(message: str, console=None):
     """Async version of display_tool_call."""

agents/praisonaiagents/memory/memory.py

@@ -0,0 +1,931 @@
+import os
+import sqlite3
+import json
+import time
+import shutil
+from typing import Any, Dict, List, Optional, Union, Literal
+import logging
+
+# Set up logger
+logger = logging.getLogger(__name__)
+
+try:
+    import chromadb
+    from chromadb.config import Settings as ChromaSettings
+    CHROMADB_AVAILABLE = True
+except ImportError:
+    CHROMADB_AVAILABLE = False
+    logger.warning("To use memory features, please run: pip install \"praisonaiagents[memory]\"")
+
+try:
+    import mem0
+    MEM0_AVAILABLE = True
+except ImportError:
+    MEM0_AVAILABLE = False
+
+try:
+    import openai
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
+
+
+
+
+class Memory:
+    """
+    A single-file memory manager covering:
+    - Short-term memory (STM) for ephemeral context
+    - Long-term memory (LTM) for persistent knowledge
+    - Entity memory (structured data about named entities)
+    - User memory (preferences/history for each user)
+    - Quality score logic for deciding which data to store in LTM
+    - Context building from multiple memory sources
+
+    Config example:
+    {
+      "provider": "rag" or "mem0" or "none",
+      "use_embedding": True,
+      "short_db": "short_term.db",
+      "long_db": "long_term.db",
+      "rag_db_path": "rag_db",   # optional path for local embedding store
+      "config": {
+        "api_key": "...",       # if mem0 usage
+        "org_id": "...",
+        "project_id": "...",
+        ...
+      }
+    }
+    """
+
+    def __init__(self, config: Dict[str, Any], verbose: int = 0):
+        self.cfg = config or {}
+        self.verbose = verbose
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set ChromaDB and OpenAI client loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+        logging.getLogger('chromadb.segment.impl.vector.local_persistent_hnsw').setLevel(logging.ERROR)
+            
+        self.provider = self.cfg.get("provider", "rag")
+        self.use_mem0 = (self.provider.lower() == "mem0") and MEM0_AVAILABLE
+        self.use_rag = (self.provider.lower() == "rag") and CHROMADB_AVAILABLE and self.cfg.get("use_embedding", False)
+
+        # Create .praison directory if it doesn't exist
+        os.makedirs(".praison", exist_ok=True)
+
+        # Short-term DB
+        self.short_db = self.cfg.get("short_db", ".praison/short_term.db")
+        self._init_stm()
+
+        # Long-term DB
+        self.long_db = self.cfg.get("long_db", ".praison/long_term.db")
+        self._init_ltm()
+
+        # Conditionally init Mem0 or local RAG
+        if self.use_mem0:
+            self._init_mem0()
+        elif self.use_rag:
+            self._init_chroma()
+
+    def _log_verbose(self, msg: str, level: int = logging.INFO):
+        """Only log if verbose >= 5"""
+        if self.verbose >= 5:
+            logger.log(level, msg)
+
+    # -------------------------------------------------------------------------
+    #                          Initialization
+    # -------------------------------------------------------------------------
+    def _init_stm(self):
+        """Creates or verifies short-term memory table."""
+        os.makedirs(os.path.dirname(self.short_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.short_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS short_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_ltm(self):
+        """Creates or verifies long-term memory table."""
+        os.makedirs(os.path.dirname(self.long_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS long_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_mem0(self):
+        """Initialize Mem0 client for agent or user memory."""
+        from mem0 import MemoryClient
+        mem_cfg = self.cfg.get("config", {})
+        api_key = mem_cfg.get("api_key", os.getenv("MEM0_API_KEY"))
+        org_id = mem_cfg.get("org_id")
+        proj_id = mem_cfg.get("project_id")
+        if org_id and proj_id:
+            self.mem0_client = MemoryClient(api_key=api_key, org_id=org_id, project_id=proj_id)
+        else:
+            self.mem0_client = MemoryClient(api_key=api_key)
+
+    def _init_chroma(self):
+        """Initialize a local Chroma client for embedding-based search."""
+        try:
+            # Create directory if it doesn't exist
+            rag_path = self.cfg.get("rag_db_path", "chroma_db")
+            os.makedirs(rag_path, exist_ok=True)
+
+            # Initialize ChromaDB with persistent storage
+            self.chroma_client = chromadb.PersistentClient(
+                path=rag_path,
+                settings=ChromaSettings(
+                    anonymized_telemetry=False,
+                    allow_reset=True
+                )
+            )
+
+            collection_name = "memory_store"
+            try:
+                self.chroma_col = self.chroma_client.get_collection(name=collection_name)
+                self._log_verbose("Using existing ChromaDB collection")
+            except Exception as e:
+                self._log_verbose(f"Collection '{collection_name}' not found. Creating new collection. Error: {e}")
+                self.chroma_col = self.chroma_client.create_collection(
+                    name=collection_name,
+                    metadata={"hnsw:space": "cosine"}
+                )
+                self._log_verbose("Created new ChromaDB collection")
+
+        except Exception as e:
+            self._log_verbose(f"Failed to initialize ChromaDB: {e}", logging.ERROR)
+            self.use_rag = False
+
+    # -------------------------------------------------------------------------
+    #                      Basic Quality Score Computation
+    # -------------------------------------------------------------------------
+    def compute_quality_score(
+        self,
+        completeness: float,
+        relevance: float,
+        clarity: float,
+        accuracy: float,
+        weights: Dict[str, float] = None
+    ) -> float:
+        """
+        Combine multiple sub-metrics into one final score, as an example.
+
+        Args:
+            completeness (float): 0-1
+            relevance (float): 0-1
+            clarity (float): 0-1
+            accuracy (float): 0-1
+            weights (Dict[str, float]): optional weighting like {"completeness": 0.25, "relevance": 0.3, ...}
+
+        Returns:
+            float: Weighted average 0-1
+        """
+        if not weights:
+            weights = {
+                "completeness": 0.25,
+                "relevance": 0.25,
+                "clarity": 0.25,
+                "accuracy": 0.25
+            }
+        total = (completeness * weights["completeness"]
+                 + relevance   * weights["relevance"]
+                 + clarity     * weights["clarity"]
+                 + accuracy    * weights["accuracy"]
+                )
+        return round(total, 3)  # e.g. round to 3 decimal places
+
+    # -------------------------------------------------------------------------
+    #                           Short-Term Methods
+    # -------------------------------------------------------------------------
+    def store_short_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in short-term memory with optional quality metrics"""
+        logger.info(f"Storing in short-term memory: {text[:100]}...")
+        logger.info(f"Metadata: {metadata}")
+        
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity, 
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Existing store logic
+        try:
+            conn = sqlite3.connect(self.short_db)
+            ident = str(time.time_ns())
+            conn.execute(
+                "INSERT INTO short_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), time.time())
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in short-term memory with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+            raise
+
+    def search_short_term(
+        self, 
+        query: str, 
+        limit: int = 5,
+        min_quality: float = 0.0,
+        relevance_cutoff: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search short-term memory with optional quality filter"""
+        self._log_verbose(f"Searching short memory for: {query}")
+        
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            filtered = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            return filtered
+            
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"
+                )
+                query_embedding = response.data[0].embedding
+                
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        quality = metadata.get("quality", 0.0)
+                        score = 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        if quality >= min_quality and score >= relevance_cutoff:
+                            results.append({
+                                "id": resp["ids"][0][i],
+                                "text": resp["documents"][0][i],
+                                "metadata": metadata,
+                                "score": score
+                            })
+                return results
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+                return []
+        
+        else:
+            # Local fallback
+            conn = sqlite3.connect(self.short_db)
+            c = conn.cursor()
+            rows = c.execute(
+                "SELECT id, content, meta FROM short_mem WHERE content LIKE ? LIMIT ?",
+                (f"%{query}%", limit)
+            ).fetchall()
+            conn.close()
+
+            results = []
+            for row in rows:
+                meta = json.loads(row[2] or "{}")
+                quality = meta.get("quality", 0.0)
+                if quality >= min_quality:
+                    results.append({
+                        "id": row[0],
+                        "text": row[1],
+                        "metadata": meta
+                    })
+            return results
+
+    def reset_short_term(self):
+        """Completely clears short-term memory."""
+        conn = sqlite3.connect(self.short_db)
+        conn.execute("DELETE FROM short_mem")
+        conn.commit()
+        conn.close()
+
+    # -------------------------------------------------------------------------
+    #                           Long-Term Methods
+    # -------------------------------------------------------------------------
+    def _sanitize_metadata(self, metadata: Dict) -> Dict:
+        """Sanitize metadata for ChromaDB - convert to acceptable types"""
+        sanitized = {}
+        for k, v in metadata.items():
+            if v is None:
+                continue
+            if isinstance(v, (str, int, float, bool)):
+                sanitized[k] = v
+            elif isinstance(v, dict):
+                # Convert dict to string representation
+                sanitized[k] = str(v)
+            else:
+                # Convert other types to string
+                sanitized[k] = str(v)
+        return sanitized
+
+    def store_long_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in long-term memory with optional quality metrics"""
+        logger.info(f"Storing in long-term memory: {text[:100]}...")
+        logger.info(f"Initial metadata: {metadata}")
+        
+        # Process metadata
+        metadata = metadata or {}
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity,
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Generate unique ID
+        ident = str(time.time_ns())
+        created = time.time()
+
+        # Store in SQLite
+        try:
+            conn = sqlite3.connect(self.long_db)
+            conn.execute(
+                "INSERT INTO long_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), created)
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in SQLite with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Error storing in SQLite: {e}")
+            return
+
+        # Store in vector database if enabled
+        if self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
+                
+                logger.info("Getting embeddings from OpenAI...")
+                logger.debug(f"Embedding input text: {text}")  # Log the input text
+                
+                response = client.embeddings.create(
+                    input=text,
+                    model="text-embedding-3-small"
+                )
+                embedding = response.data[0].embedding
+                logger.info("Successfully got embeddings")
+                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                
+                # Sanitize metadata for ChromaDB
+                sanitized_metadata = self._sanitize_metadata(metadata)
+                
+                # Store in ChromaDB with embedding
+                self.chroma_col.add(
+                    documents=[text],
+                    metadatas=[sanitized_metadata],
+                    ids=[ident],
+                    embeddings=[embedding]
+                )
+                logger.info(f"Successfully stored in ChromaDB with ID: {ident}")
+            except Exception as e:
+                logger.error(f"Error storing in ChromaDB: {e}")
+        
+        elif self.use_mem0 and hasattr(self, "mem0_client"):
+            try:
+                self.mem0_client.add(text, metadata=metadata)
+                logger.info("Successfully stored in Mem0")
+            except Exception as e:
+                logger.error(f"Error storing in Mem0: {e}")
+
+
+    def search_long_term(
+        self, 
+        query: str, 
+        limit: int = 5, 
+        relevance_cutoff: float = 0.0,
+        min_quality: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search long-term memory with optional quality filter"""
+        self._log_verbose(f"Searching long memory for: {query}")
+        self._log_verbose(f"Min quality: {min_quality}")
+
+        found = []
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            # Filter by quality
+            filtered = [r for r in results if r.get("metadata", {}).get("quality", 0.0) >= min_quality]
+            logger.info(f"Found {len(filtered)} results in Mem0")
+            return filtered
+
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                # Get query embedding
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"  # Using consistent model
+                )
+                query_embedding = response.data[0].embedding
+                
+                # Search ChromaDB with embedding
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit,
+                    include=["documents", "metadatas", "distances"]
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        text = resp["documents"][0][i]
+                        # Add memory record citation
+                        text = f"{text} (Memory record: {text})"
+                        found.append({
+                            "id": resp["ids"][0][i],
+                            "text": text,
+                            "metadata": metadata,
+                            "score": 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        })
+                logger.info(f"Found {len(found)} results in ChromaDB")
+
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+
+        # Always try SQLite as fallback or additional source
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        rows = c.execute(
+            "SELECT id, content, meta, created_at FROM long_mem WHERE content LIKE ? LIMIT ?",
+            (f"%{query}%", limit)
+        ).fetchall()
+        conn.close()
+
+        for row in rows:
+            meta = json.loads(row[2] or "{}")
+            text = row[1]
+            # Add memory record citation if not already present
+            if "(Memory record:" not in text:
+                text = f"{text} (Memory record: {text})"
+            # Only add if not already found by ChromaDB/Mem0
+            if not any(f["id"] == row[0] for f in found):
+                found.append({
+                    "id": row[0],
+                    "text": text,
+                    "metadata": meta,
+                    "created_at": row[3]
+                })
+        logger.info(f"Found {len(found)} total results after SQLite")
+
+        results = found
+
+        # Filter by quality if needed
+        if min_quality > 0:
+            self._log_verbose(f"Found {len(results)} initial results")
+            results = [
+                r for r in results 
+                if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+            ]
+            self._log_verbose(f"After quality filter: {len(results)} results")
+
+        # Apply relevance cutoff if specified
+        if relevance_cutoff > 0:
+            results = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            logger.info(f"After relevance filter: {len(results)} results")
+        
+        return results[:limit]
+
+    def reset_long_term(self):
+        """Clear local LTM DB, plus Chroma or mem0 if in use."""
+        conn = sqlite3.connect(self.long_db)
+        conn.execute("DELETE FROM long_mem")
+        conn.commit()
+        conn.close()
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            # Mem0 has no universal reset API. Could implement partial or no-op.
+            pass
+        if self.use_rag and hasattr(self, "chroma_client"):
+            self.chroma_client.reset()  # entire DB
+            self._init_chroma()         # re-init fresh
+
+    # -------------------------------------------------------------------------
+    #                       Entity Memory Methods
+    # -------------------------------------------------------------------------
+    def store_entity(self, name: str, type_: str, desc: str, relations: str):
+        """
+        Save entity info in LTM (or mem0/rag). 
+        We'll label the metadata type = entity for easy filtering.
+        """
+        data = f"Entity {name}({type_}): {desc} | relationships: {relations}"
+        self.store_long_term(data, metadata={"category": "entity"})
+
+    def search_entity(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        Filter to items that have metadata 'category=entity'.
+        """
+        all_hits = self.search_long_term(query, limit=20)  # gather more
+        ents = []
+        for h in all_hits:
+            meta = h.get("metadata") or {}
+            if meta.get("category") == "entity":
+                ents.append(h)
+        return ents[:limit]
+
+    def reset_entity_only(self):
+        """
+        If you only want to drop entity items from LTM, you'd do a custom 
+        delete from local DB where meta LIKE '%category=entity%'. 
+        For brevity, we do a full LTM reset here.
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                       User Memory Methods
+    # -------------------------------------------------------------------------
+    def store_user_memory(self, user_id: str, text: str, extra: Dict[str, Any] = None):
+        """
+        If mem0 is used, do user-based addition. Otherwise store in LTM with user in metadata.
+        """
+        meta = {"user_id": user_id}
+        if extra:
+            meta.update(extra)
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            self.mem0_client.add(text, user_id=user_id, metadata=meta)
+        else:
+            self.store_long_term(text, metadata=meta)
+
+    def search_user_memory(self, user_id: str, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        If mem0 is used, pass user_id in. Otherwise fallback to local filter on user in metadata.
+        """
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            return self.mem0_client.search(query=query, limit=limit, user_id=user_id)
+        else:
+            hits = self.search_long_term(query, limit=20)
+            filtered = []
+            for h in hits:
+                meta = h.get("metadata", {})
+                if meta.get("user_id") == user_id:
+                    filtered.append(h)
+            return filtered[:limit]
+
+    def reset_user_memory(self):
+        """
+        Clear all user-based info. For simplicity, we do a full LTM reset. 
+        Real usage might filter only metadata "user_id".
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                 Putting it all Together: Task Finalization
+    # -------------------------------------------------------------------------
+    def finalize_task_output(
+        self,
+        content: str,
+        agent_name: str,
+        quality_score: float,
+        threshold: float = 0.7,
+        metrics: Dict[str, Any] = None,
+        task_id: str = None
+    ):
+        """Store task output in memory with appropriate metadata"""
+        logger.info(f"Finalizing task output: {content[:100]}...")
+        logger.info(f"Agent: {agent_name}, Quality: {quality_score}, Threshold: {threshold}")
+        
+        metadata = {
+            "task_id": task_id,
+            "agent": agent_name,
+            "quality": quality_score,
+            "metrics": metrics,
+            "task_type": "output",
+            "stored_at": time.time()
+        }
+        logger.info(f"Prepared metadata: {metadata}")
+        
+        # Always store in short-term memory
+        try:
+            logger.info("Storing in short-term memory...")
+            self.store_short_term(
+                text=content,
+                metadata=metadata
+            )
+            logger.info("Successfully stored in short-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+        
+        # Store in long-term memory if quality meets threshold
+        if quality_score >= threshold:
+            try:
+                logger.info(f"Quality score {quality_score} >= {threshold}, storing in long-term memory...")
+                self.store_long_term(
+                    text=content,
+                    metadata=metadata
+                )
+                logger.info("Successfully stored in long-term memory")
+            except Exception as e:
+                logger.error(f"Failed to store in long-term memory: {e}")
+        else:
+            logger.info(f"Quality score {quality_score} < {threshold}, skipping long-term storage")
+
+    # -------------------------------------------------------------------------
+    #                 Building Context (Short, Long, Entities, User)
+    # -------------------------------------------------------------------------
+    def build_context_for_task(
+        self,
+        task_descr: str,
+        user_id: Optional[str] = None,
+        additional: str = "",
+        max_items: int = 3
+    ) -> str:
+        """
+        Merges relevant short-term, long-term, entity, user memories
+        into a single text block with deduplication and clean formatting.
+        """
+        q = (task_descr + " " + additional).strip()
+        lines = []
+        seen_contents = set()  # Track unique contents
+
+        def normalize_content(content: str) -> str:
+            """Normalize content for deduplication"""
+            # Extract just the main content without citations for comparison
+            normalized = content.split("(Memory record:")[0].strip()
+            # Keep more characters to reduce false duplicates
+            normalized = ''.join(c.lower() for c in normalized if not c.isspace())
+            return normalized
+
+        def format_content(content: str, max_len: int = 150) -> str:
+            """Format content with clean truncation at word boundaries"""
+            if not content:
+                return ""
+            
+            # Clean up content by removing extra whitespace and newlines
+            content = ' '.join(content.split())
+            
+            # If content contains a memory citation, preserve it
+            if "(Memory record:" in content:
+                return content  # Keep original citation format
+            
+            # Regular content truncation
+            if len(content) <= max_len:
+                return content
+            
+            truncate_at = content.rfind(' ', 0, max_len - 3)
+            if truncate_at == -1:
+                truncate_at = max_len - 3
+            return content[:truncate_at] + "..."
+
+        def add_section(title: str, hits: List[Any]) -> None:
+            """Add a section of memory hits with deduplication"""
+            if not hits:
+                return
+                
+            formatted_hits = []
+            for h in hits:
+                content = h.get('text', '') if isinstance(h, dict) else str(h)
+                if not content:
+                    continue
+                    
+                # Keep original format if it has a citation
+                if "(Memory record:" in content:
+                    formatted = content
+                else:
+                    formatted = format_content(content)
+                
+                # Only add if we haven't seen this normalized content before
+                normalized = normalize_content(formatted)
+                if normalized not in seen_contents:
+                    seen_contents.add(normalized)
+                    formatted_hits.append(formatted)
+            
+            if formatted_hits:
+                # Add section header
+                if lines:
+                    lines.append("")  # Space before new section
+                lines.append(title)
+                lines.append("=" * len(title))  # Underline the title
+                lines.append("")  # Space after title
+                
+                # Add formatted content with bullet points
+                for content in formatted_hits:
+                    lines.append(f" • {content}")
+
+        # Add each section
+        # First get all results
+        short_term = self.search_short_term(q, limit=max_items)
+        long_term = self.search_long_term(q, limit=max_items)
+        entities = self.search_entity(q, limit=max_items)
+        user_mem = self.search_user_memory(user_id, q, limit=max_items) if user_id else []
+
+        # Add sections in order of priority
+        add_section("Short-term Memory Context", short_term)
+        add_section("Long-term Memory Context", long_term)
+        add_section("Entity Context", entities)
+        if user_id:
+            add_section("User Context", user_mem)
+
+        return "\n".join(lines) if lines else ""
+
+    # -------------------------------------------------------------------------
+    #                      Master Reset (Everything)
+    # -------------------------------------------------------------------------
+    def reset_all(self):
+        """
+        Fully wipes short-term, long-term, and any memory in mem0 or rag.
+        """
+        self.reset_short_term()
+        self.reset_long_term()
+        # Entities & user memory are stored in LTM or mem0, so no separate step needed.
+
+    def _process_quality_metrics(
+        self,
+        metadata: Dict[str, Any],
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None, 
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ) -> Dict[str, Any]:
+        """Process and store quality metrics in metadata"""
+        metadata = metadata or {}
+        
+        # Handle sub-metrics if provided
+        if None not in [completeness, relevance, clarity, accuracy]:
+            metadata.update({
+                "completeness": completeness,
+                "relevance": relevance,
+                "clarity": clarity,
+                "accuracy": accuracy,
+                "quality": self.compute_quality_score(
+                    completeness, relevance, clarity, accuracy, weights
+                )
+            })
+        # Handle external evaluator quality if provided
+        elif evaluator_quality is not None:
+            metadata["quality"] = evaluator_quality
+        
+        return metadata
+
+    def calculate_quality_metrics(
+        self,
+        output: str,
+        expected_output: str,
+        llm: Optional[str] = None,
+        custom_prompt: Optional[str] = None
+    ) -> Dict[str, float]:
+        """Calculate quality metrics using LLM"""
+        logger.info("Calculating quality metrics for output")
+        logger.info(f"Output: {output[:100]}...")
+        logger.info(f"Expected: {expected_output[:100]}...")
+        
+        # Default evaluation prompt
+        default_prompt = f"""
+        Evaluate the following output against expected output.
+        Score each metric from 0.0 to 1.0:
+        - Completeness: Does it address all requirements?
+        - Relevance: Does it match expected output?
+        - Clarity: Is it clear and well-structured?
+        - Accuracy: Is it factually correct?
+
+        Expected: {expected_output}
+        Actual: {output}
+
+        Return ONLY a JSON with these keys: completeness, relevance, clarity, accuracy
+        Example: {{"completeness": 0.95, "relevance": 0.8, "clarity": 0.9, "accuracy": 0.85}}
+        """
+
+        try:
+            # Use OpenAI client from main.py
+            from ..main import client
+            
+            response = client.chat.completions.create(
+                model=llm or "gpt-4o",
+                messages=[{
+                    "role": "user", 
+                    "content": custom_prompt or default_prompt
+                }],
+                response_format={"type": "json_object"},
+                temperature=0.3
+            )
+            
+            metrics = json.loads(response.choices[0].message.content)
+            
+            # Validate metrics
+            required = ["completeness", "relevance", "clarity", "accuracy"]
+            if not all(k in metrics for k in required):
+                raise ValueError("Missing required metrics in LLM response")
+            
+            logger.info(f"Calculated metrics: {metrics}")
+            return metrics
+            
+        except Exception as e:
+            logger.error(f"Error calculating metrics: {e}")
+            return {
+                "completeness": 0.0,
+                "relevance": 0.0,
+                "clarity": 0.0,
+                "accuracy": 0.0
+            }
+
+    def store_quality(
+        self,
+        text: str,
+        quality_score: float,
+        task_id: Optional[str] = None,
+        iteration: Optional[int] = None,
+        metrics: Optional[Dict[str, float]] = None,
+        memory_type: Literal["short", "long"] = "long"
+    ) -> None:
+        """Store quality metrics in memory"""
+        logger.info(f"Attempting to store in {memory_type} memory: {text[:100]}...")
+        
+        metadata = {
+            "quality": quality_score,
+            "task_id": task_id,
+            "iteration": iteration
+        }
+        
+        if metrics:
+            metadata.update({
+                k: v for k, v in metrics.items()  # Remove metric_ prefix
+            })
+            
+        logger.info(f"With metadata: {metadata}")
+        
+        try:
+            if memory_type == "short":
+                self.store_short_term(text, metadata=metadata)
+                logger.info("Successfully stored in short-term memory")
+            else:
+                self.store_long_term(text, metadata=metadata)
+                logger.info("Successfully stored in long-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in memory: {e}")
+
+    def search_with_quality(
+        self,
+        query: str,
+        min_quality: float = 0.0,
+        memory_type: Literal["short", "long"] = "long",
+        limit: int = 5
+    ) -> List[Dict[str, Any]]:
+        """Search with quality filter"""
+        logger.info(f"Searching {memory_type} memory for: {query}")
+        logger.info(f"Min quality: {min_quality}")
+        
+        search_func = (
+            self.search_short_term if memory_type == "short" 
+            else self.search_long_term
+        )
+        
+        results = search_func(query, limit=limit)
+        logger.info(f"Found {len(results)} initial results")
+        
+        filtered = [
+            r for r in results 
+            if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+        ]
+        logger.info(f"After quality filter: {len(filtered)} results")
+        
+        return filtered

agents/praisonaiagents/process/process.py

@@ -10,14 +10,16 @@ class LoopItems(BaseModel):
     items: List[Any]
 
 class Process:
-    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False):
+    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False, max_iter: int = 10):
         self.tasks = tasks
         self.agents = agents
         self.manager_llm = manager_llm
         self.verbose = verbose
+        self.max_iter = max_iter
 
     async def aworkflow(self) -> AsyncGenerator[str, None]:
         """Async version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -41,7 +43,12 @@ class Process:
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -111,6 +118,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -130,22 +142,28 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     result = current_task.result.raw.lower()
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() in result:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":  # If empty or explicit exit
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:
@@ -285,6 +303,7 @@ Provide a JSON with the structure:
 
     def workflow(self):
         """Synchronous version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -308,7 +327,12 @@ Provide a JSON with the structure:
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -372,6 +396,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -391,22 +420,28 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
                     result = current_task.result.raw.lower()
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() in result:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":  # If empty or explicit exit
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:

agents/praisonaiagents/task/task.py

@@ -5,12 +5,17 @@ from pydantic import BaseModel
 from ..main import TaskOutput
 from ..agent.agent import Agent
 import uuid
+import os
+import time
+
+# Set up logger
+logger = logging.getLogger(__name__)
 
 class Task:
     def __init__(
         self,
         description: str,
-        expected_output: str,
+        expected_output: Optional[str] = None,
         agent: Optional[Agent] = None,
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
@@ -30,12 +35,14 @@ class Task:
         task_type: str = "task",
         condition: Optional[Dict[str, List[str]]] = None,
         is_start: bool = False,
-        loop_state: Optional[Dict[str, Union[str, int]]] = None
+        loop_state: Optional[Dict[str, Union[str, int]]] = None,
+        memory=None,
+        quality_check=True
     ):
         self.id = str(uuid.uuid4()) if id is None else str(id)
         self.name = name
         self.description = description
-        self.expected_output = expected_output
+        self.expected_output = expected_output if expected_output is not None else "Complete the task successfully"
         self.agent = agent
         self.tools = tools if tools else []
         self.context = context if context else []
@@ -54,6 +61,21 @@ class Task:
         self.condition = condition if condition else {}
         self.is_start = is_start
         self.loop_state = loop_state if loop_state else {}
+        self.memory = memory
+        self.quality_check = quality_check
+
+        # Set logger level based on config verbose level
+        verbose = self.config.get("verbose", 0)
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
 
         if self.output_json and self.output_pydantic:
             raise ValueError("Only one output type can be defined")
@@ -64,11 +86,137 @@ class Task:
     def __str__(self):
         return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
 
+    def initialize_memory(self):
+        """Initialize memory if config exists but memory doesn't"""
+        if not self.memory and self.config.get('memory_config'):
+            try:
+                from ..memory.memory import Memory
+                logger.info(f"Task {self.id}: Initializing memory from config: {self.config['memory_config']}")
+                self.memory = Memory(config=self.config['memory_config'])
+                logger.info(f"Task {self.id}: Memory initialized successfully")
+                
+                # Verify database was created
+                if os.path.exists(self.config['memory_config']['storage']['path']):
+                    logger.info(f"Task {self.id}: Memory database exists after initialization")
+                else:
+                    logger.error(f"Task {self.id}: Failed to create memory database!")
+                return self.memory
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to initialize memory: {e}")
+                logger.exception(e)
+        return None
+
+    def store_in_memory(self, content: str, agent_name: str = None, task_id: str = None):
+        """Store content in memory with metadata"""
+        if self.memory:
+            try:
+                logger.info(f"Task {self.id}: Storing content in memory...")
+                self.memory.store_long_term(
+                    text=content,
+                    metadata={
+                        "agent_name": agent_name or "Agent",
+                        "task_id": task_id or self.id,
+                        "timestamp": time.time()
+                    }
+                )
+                logger.info(f"Task {self.id}: Content stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store content in memory: {e}")
+                logger.exception(e)
+
     async def execute_callback(self, task_output: TaskOutput) -> None:
-        """Execute the callback function, handling both sync and async callbacks"""
+        """Execute callback and store quality metrics if enabled"""
+        logger.info(f"Task {self.id}: execute_callback called")
+        logger.info(f"Quality check enabled: {self.quality_check}")
+        
+        # Initialize memory if not already initialized
+        if not self.memory:
+            self.memory = self.initialize_memory()
+        
+        logger.info(f"Memory object exists: {self.memory is not None}")
+        if self.memory:
+            logger.info(f"Memory config: {self.memory.cfg}")
+            # Store task output in memory
+            try:
+                logger.info(f"Task {self.id}: Storing task output in memory...")
+                self.store_in_memory(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Task output stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store task output in memory: {e}")
+                logger.exception(e)
+        
+        logger.info(f"Task output: {task_output.raw[:100]}...")
+        
+        if self.quality_check and self.memory:
+            try:
+                logger.info(f"Task {self.id}: Starting memory operations")
+                logger.info(f"Task {self.id}: Calculating quality metrics for output: {task_output.raw[:100]}...")
+                
+                # Get quality metrics from LLM
+                metrics = self.memory.calculate_quality_metrics(
+                    task_output.raw,
+                    self.expected_output
+                )
+                logger.info(f"Task {self.id}: Quality metrics calculated: {metrics}")
+                
+                quality_score = metrics.get("accuracy", 0.0)
+                logger.info(f"Task {self.id}: Quality score: {quality_score}")
+                
+                # Store in both short and long-term memory with higher threshold
+                logger.info(f"Task {self.id}: Finalizing task output in memory...")
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7,  # Only high quality outputs in long-term memory
+                    metrics=metrics,
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Finalized task output in memory")
+                
+                # Store quality metrics separately
+                logger.info(f"Task {self.id}: Storing quality metrics...")
+                self.memory.store_quality(
+                    text=task_output.raw,
+                    quality_score=quality_score,
+                    task_id=self.id,
+                    metrics=metrics
+                )
+                
+                # Store in both short and long-term memory with higher threshold
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7  # Only high quality outputs in long-term memory
+                )
+                
+                # Build context for next tasks
+                if self.next_tasks:
+                    logger.info(f"Task {self.id}: Building context for next tasks...")
+                    context = self.memory.build_context_for_task(
+                        task_descr=task_output.raw,
+                        max_items=5
+                    )
+                    logger.info(f"Task {self.id}: Built context for next tasks: {len(context)} items")
+                
+                logger.info(f"Task {self.id}: Memory operations complete")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to process memory operations: {e}")
+                logger.exception(e)  # Print full stack trace
+                # Continue execution even if memory operations fail
+
+        # Execute original callback
         if self.callback:
-            if asyncio.iscoroutinefunction(self.callback):
-                await self.callback(task_output)
-            else:
-                loop = asyncio.get_event_loop()
-                await loop.run_in_executor(None, self.callback, task_output) 
+            try:
+                if asyncio.iscoroutinefunction(self.callback):
+                    await self.callback(task_output)
+                else:
+                    self.callback(task_output)
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to execute callback: {e}")
+                logger.exception(e)

agents/praisonaiagents/tools/pandas_tools.py

@@ -160,6 +160,9 @@ class PandasTools:
             Dict: Summary statistics and information
         """
         try:
+            if not isinstance(df, pd.DataFrame):
+                raise TypeError(f"Expected pandas DataFrame, got {type(df).__name__}")
+                
             numeric_summary = df.describe().to_dict()
             # Convert numpy types to native Python types
             for col in numeric_summary:

agents/praisonaiagents/tools/yfinance_tools.py

@@ -145,7 +145,15 @@ class YFinanceTools:
                 return [{"error": "yfinance package not available"}]
 
             hist = ticker.history(period=period, interval=interval, start=start, end=end)
-            return hist.reset_index().to_dict('records')
+            data = hist.reset_index().to_dict('records')
+            
+            # Convert timestamps to ISO format strings
+            for record in data:
+                if 'Date' in record and hasattr(record['Date'], 'isoformat'):
+                    record['Date'] = record['Date'].isoformat()
+                if 'Datetime' in record and hasattr(record['Datetime'], 'isoformat'):
+                    record['Datetime'] = record['Datetime'].isoformat()
+            return data
         except Exception as e:
             error_msg = f"Error fetching historical data: {str(e)}"
             logging.error(error_msg)

agents/programming-agent.py

@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+agent = Agent(
+    instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+    tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+)
+agent.start(
+    "Write a python script using yfinance to find the stock price of Tesla"
+    "First check if required packages are installed"
+    "Run it using execute_code"
+    "execute_command if you want to run any terminal command"
+    "search internet using duckduckgo if you want to know update python package information"
+    "Analyse the output using analyze_code and fix error if required"
+    "if no package is installed, install it"
+    "then run the code"
+)

agents/prompt_chaining copy.py

@@ -0,0 +1,91 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+from typing import List, Dict
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+def create_prompt_chain():
+    # Create agents for each step in the chain
+    agent1 = Agent(
+        name="Time Checker",
+        role="Time checker",
+        goal="Check if the time is even or odd",
+        instructions="Check if the time is even or odd",
+        tools=[get_time_check]
+    )
+
+    agent2 = Agent(
+        name="Advanced Analyzer",
+        role="Advanced data analyzer",
+        goal="Perform in-depth analysis of processed data",
+        instructions="Analyze the processed data in detail"
+    )
+
+    agent3 = Agent(
+        name="Final Processor",
+        role="Final data processor",
+        goal="Generate final output based on analysis",
+        instructions="Create final output based on analyzed data"
+    )
+
+    # Create tasks for each step
+    initial_task = Task(
+        name="time_check",
+        description="Getting time check and checking if it is even or odd",
+        expected_output="Getting time check and checking if it is even or odd",
+        agent=agent1,
+        is_start=True,  # Mark as the starting task
+        task_type="decision",  # This task will make a decision
+        next_tasks=["advanced_analysis"],  # Next task if condition passes
+        condition={
+            "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+            "odd": ["final_processing"]  # If fails, exit the chain
+        }
+    )
+
+    analysis_task = Task(
+        name="advanced_analysis",
+        description="Perform advanced analysis on the processed data",
+        expected_output="Analyzed data ready for final processing",
+        agent=agent2,
+        next_tasks=["final_processing"]
+    )
+
+    final_task = Task(
+        name="final_processing",
+        description="Generate final output",
+        expected_output="Final processed result",
+        agent=agent3
+    )
+
+    # Create the workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3],
+        tasks=[initial_task, analysis_task, final_task],
+        process="workflow",  # Use workflow process type
+        verbose=True
+    )
+
+    return workflow
+
+def main():
+    # Create and run the prompt chain
+    workflow = create_prompt_chain()
+    
+    # Run the workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nWorkflow Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+if __name__ == "__main__":
+    main()

agents/prompt_chaining.py

@@ -0,0 +1,78 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create agents for each step in the chain
+agent1 = Agent(
+    name="Time Checker",
+    role="Time checker",
+    goal="Check if the time is even or odd",
+    instructions="Check if the time is even or odd",
+    tools=[get_time_check]
+)
+
+agent2 = Agent(
+    name="Advanced Analyzer",
+    role="Advanced data analyzer",
+    goal="Perform in-depth analysis of processed data",
+    instructions="Analyze the processed data in detail"
+)
+
+agent3 = Agent(
+    name="Final Processor",
+    role="Final data processor",
+    goal="Generate final output based on analysis",
+    instructions="Create final output based on analyzed data"
+)
+
+# Create tasks for each step
+initial_task = Task(
+    name="time_check",
+    description="Getting time check and checking if it is even or odd",
+    expected_output="Getting time check and checking if it is even or odd",
+    agent=agent1,
+    is_start=True,  # Mark as the starting task
+    task_type="decision",  # This task will make a decision
+    next_tasks=["advanced_analysis"],  # Next task if condition passes
+    condition={
+        "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+        "odd": "exit"  # If fails, exit the chain
+    }
+)
+
+analysis_task = Task(
+    name="advanced_analysis",
+    description="Perform advanced analysis on the processed data",
+    expected_output="Analyzed data ready for final processing",
+    agent=agent2,
+    next_tasks=["final_processing"]
+)
+
+final_task = Task(
+    name="final_processing",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=agent3
+)
+
+# Create the workflow manager
+workflow = PraisonAIAgents(
+    agents=[agent1, agent2, agent3],
+    tasks=[initial_task, analysis_task, final_task],
+    process="workflow",  # Use workflow process type
+    verbose=True
+)
+
+# Run the workflow
+results = workflow.start()
+
+# Print results
+print("\nWorkflow Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.28"
+version = "0.0.39"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }
@@ -13,4 +13,22 @@ dependencies = [
     "pydantic",
     "rich",
     "openai"
-] 
+]
+
+[project.optional-dependencies]
+memory = [
+    "chromadb>=0.5.23"
+]
+
+knowledge = [
+    "mem0ai>=0.1.0",
+    "chromadb==0.5.23",
+    "markitdown",
+    "chonkie"
+]
+
+# Combined features
+all = [
+    "praisonaiagents[memory]",
+    "praisonaiagents[knowledge]"
+]

agents/recommendation-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")

agents/research-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")

agents/routing copy.py

@@ -0,0 +1,82 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+current_time = int(time.time())
+result = "even" if current_time % 2 == 0 else "odd"
+print(f"Time check: {current_time} is {result}")
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": "exit"
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+    next_tasks=["final_process"]
+)
+
+final_task = Task(
+    name="final_process",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/routing.py

@@ -0,0 +1,83 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": ["process_invalid"]
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+)
+
+final_task = Task(
+    name="process_invalid",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/sample.md

@@ -0,0 +1,5 @@
+# Sample Markdown
+## Section 1
+This is a test markdown file.
+## Section 2
+It contains some formatted text and sections.

agents/sample.txt

@@ -0,0 +1,3 @@
+This is a sample text file.
+It contains multiple lines.
+This is for testing purposes.

agents/shopping-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")

agents/single-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

agents/streamlit-agents.py

@@ -0,0 +1,21 @@
+import streamlit as st
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+st.title("AI Research Assistant")
+st.write("Enter your research query below to get started!")
+
+# Initialize the research agent
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+
+# Create the input field
+query = st.text_input("Research Query", placeholder="Enter your research topic...")
+
+# Add a search button
+if st.button("Search"):
+    if query:
+        with st.spinner("Researching..."):
+            result = agent.start(query)
+            st.write(result)
+    else:
+        st.warning("Please enter a research query")

agents/structured_response_example.py

@@ -34,7 +34,8 @@ def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
         backstory="Expert at gathering information and providing structured analysis",
         verbose=verbose,
         self_reflect=True,  # Enable self-reflection for better quality
-        markdown=True
+        markdown=True,
+        llm="gpt-4o-mini"
     )
     
     # Create the analysis prompt
@@ -71,7 +72,7 @@ Return ONLY the JSON object, no other text.
         output_json=AnalysisReport  # This ensures response matches our model
     )
     
-    return AnalysisReport.parse_raw(response)
+    return AnalysisReport.model_validate_json(response)
 
 if __name__ == "__main__":
     # Example usage

agents/tesla-stock-price.csv

@@ -0,0 +1,758 @@
+"date","close","volume","open","high","low"
+"11:34","270.49","4,787,699","264.50","273.88","262.24"
+"2018/10/15","259.5900","6189026.0000","259.0600","263.2800","254.5367"
+"2018/10/12","258.7800","7189257.0000","261.0000","261.9900","252.0100"
+"2018/10/11","252.2300","8128184.0000","257.5300","262.2500","249.0300"
+"2018/10/10","256.8800","12781560.0000","264.6100","265.5100","247.7700"
+"2018/10/09","262.8000","12037780.0000","255.2500","266.7700","253.3000"
+"2018/10/08","250.5600","13371180.0000","264.5200","267.7599","249.0000"
+"2018/10/05","261.9500","17900710.0000","274.6500","274.8800","260.0000"
+"2018/10/04","281.8300","9638885.0000","293.9500","294.0000","277.6700"
+"2018/10/03","294.8000","7982272.0000","303.3300","304.6000","291.5700"
+"2018/10/02","301.0200","11699690.0000","313.9500","316.8400","299.1500"
+"2018/10/01","310.7000","21714210.0000","305.7700","311.4400","301.0500"
+"2018/09/28","264.7700","33597290.0000","270.2600","278.0000","260.5550"
+"2018/09/27","307.5200","7337760.0000","312.9000","314.9600","306.9100"
+"2018/09/26","309.5800","7835863.0000","301.9100","313.8900","301.1093"
+"2018/09/25","300.9900","4472287.0000","300.0000","304.6000","296.5000"
+"2018/09/24","299.6800","4834384.0000","298.4800","302.9993","293.5800"
+"2018/09/21","299.1000","5038497.0000","297.7000","300.5800","295.3700"
+"2018/09/20","298.3300","7332477.0000","303.5600","305.9800","293.3300"
+"2018/09/19","299.0200","8264353.0000","280.5100","300.0000","280.5000"
+"2018/09/18","284.9600","16494280.0000","296.6900","302.6400","275.5000"
+"2018/09/17","294.8400","6866208.0000","290.0400","300.8700","288.1300"
+"2018/09/14","295.2000","6757652.0000","288.7600","297.3256","286.5200"
+"2018/09/13","289.4600","6333281.0000","288.0200","295.0000","285.1800"
+"2018/09/12","290.5400","10002150.0000","281.4400","292.5000","278.6463"
+"2018/09/11","279.4400","9161061.0000","279.4700","282.0000","273.5500"
+"2018/09/10","285.5000","14232250.0000","273.2600","286.0300","271.0000"
+"2018/09/07","263.2400","22442310.0000","260.1000","268.3500","252.2548"
+"2018/09/06","280.9500","7472756.0000","284.8000","291.1700","278.8800"
+"2018/09/05","280.7400","7707342.0000","285.0500","286.7800","277.1800"
+"2018/09/04","288.9500","8304571.0000","296.9400","298.1900","288.0000"
+"2018/08/31","301.6600","5367048.0000","302.0000","305.3082","298.6000"
+"2018/08/30","303.1500","7210625.0000","302.2600","304.6000","297.7200"
+"2018/08/29","305.0100","7410538.0000","310.2700","311.8500","303.6900"
+"2018/08/28","311.8600","7639453.0000","318.4100","318.8800","311.1900"
+"2018/08/27","319.2700","13062650.0000","318.0000","322.4350","308.8100"
+"2018/08/24","322.8200","3592734.0000","320.7000","323.8500","319.4000"
+"2018/08/23","320.1000","5138685.0000","319.1400","327.3200","318.1000"
+"2018/08/22","321.6400","5935853.0000","320.8700","323.8800","314.6700"
+"2018/08/21","321.9000","13147770.0000","310.6100","324.7900","309.0000"
+"2018/08/20","308.4400","17380430.0000","291.7040","308.5000","288.2000"
+"2018/08/17","305.5000","18895880.0000","323.5000","326.7700","303.5300"
+"2018/08/16","335.4500","6045392.0000","339.9100","342.2800","333.8200"
+"2018/08/15","338.6900","9073033.0000","341.9060","344.4900","332.1400"
+"2018/08/14","347.6400","6948511.0000","358.4500","359.1995","347.1000"
+"2018/08/13","356.4100","10444920.0000","361.1300","363.1900","349.0200"
+"2018/08/10","355.4900","11530510.0000","354.0000","360.0000","346.0000"
+"2018/08/09","352.4500","16951800.0000","365.5500","367.0100","345.7300"
+"2018/08/08","370.3400","24535420.0000","369.0900","382.6400","367.1200"
+"2018/08/07","379.5700","30596400.0000","343.8400","387.4600","339.1501"
+"2018/08/06","341.9900","8536766.0000","345.4600","354.9800","341.8200"
+"2018/08/03","348.1700","13646640.0000","347.8100","355.0000","342.5300"
+"2018/08/02","349.5400","23159160.0000","328.4400","349.9900","323.1600"
+"2018/08/01","300.8400","8768099.0000","297.9900","303.0000","293.0000"
+"2018/07/31","298.1400","5055094.0000","292.2500","298.3200","289.0700"
+"2018/07/30","290.1700","6799581.0000","295.9000","296.1000","286.1300"
+"2018/07/27","297.1800","5694690.0000","307.2500","307.6933","295.3404"
+"2018/07/26","306.6500","4619567.0000","304.8500","310.7000","303.6411"
+"2018/07/25","308.7400","7031382.0000","296.7400","309.6200","294.5025"
+"2018/07/24","297.4300","9586186.0000","304.4209","307.7171","292.5452"
+"2018/07/23","303.2000","10974820.0000","301.8400","305.5000","292.8601"
+"2018/07/20","313.5800","5159831.0000","321.2300","323.2400","311.7093"
+"2018/07/19","320.2300","5907116.0000","316.3300","323.5400","314.0100"
+"2018/07/18","323.8500","5610085.0000","325.0000","325.5000","316.2500"
+"2018/07/17","322.6900","6987782.0000","308.8100","324.7400","308.5000"
+"2018/07/16","310.1000","7800454.0000","311.7100","315.1600","306.2500"
+"2018/07/13","318.8700","5866776.0000","315.5800","319.5849","309.2500"
+"2018/07/12","316.7100","5711851.0000","321.4300","323.2300","312.7700"
+"2018/07/11","318.9600","4878595.0000","315.8000","321.9400","315.0700"
+"2018/07/10","322.4700","9437557.0000","324.5600","327.6771","319.2000"
+"2018/07/09","318.5100","7579541.0000","311.9900","318.5200","308.0000"
+"2018/07/06","308.9000","8860198.0000","304.9550","312.0700","302.0000"
+"2018/07/05","309.1600","17457570.0000","313.7600","314.3900","296.2200"
+"2018/07/03","310.8600","12282640.0000","331.7500","332.4900","309.6900"
+"2018/07/02","335.0700","18732710.0000","360.0700","364.7800","329.8500"
+"2018/06/29","342.9500","6475903.0000","353.3300","353.8600","342.4100"
+"2018/06/28","349.9300","8388172.0000","348.6600","357.0200","346.1100"
+"2018/06/27","344.5000","8313817.0000","345.0000","350.7900","339.5000"
+"2018/06/26","342.0000","7434749.0000","336.0540","343.5500","325.7990"
+"2018/06/25","333.0100","6925089.0000","330.1200","338.4700","327.5000"
+"2018/06/22","333.6300","10253770.0000","351.5400","352.2500","332.0000"
+"2018/06/21","347.5100","7949846.0000","362.0000","366.2139","346.2700"
+"2018/06/20","362.2200","8370268.0000","358.0400","364.3800","352.0000"
+"2018/06/19","352.5500","12734840.0000","365.1600","370.0000","346.2500"
+"2018/06/18","370.8300","12025450.0000","355.4000","373.7300","354.5000"
+"2018/06/15","358.1700","10827560.0000","353.8400","364.6700","351.2500"
+"2018/06/14","357.7200","10961150.0000","347.6300","358.7500","346.6000"
+"2018/06/13","344.7800","9401467.0000","346.7060","347.2000","339.8000"
+"2018/06/12","342.7700","22256430.0000","344.7000","354.9700","338.0000"
+"2018/06/11","332.1000","13166050.0000","322.5100","334.6600","322.5000"
+"2018/06/08","317.6600","8192175.0000","319.0000","324.4800","317.1500"
+"2018/06/07","316.0900","14336110.0000","316.1500","330.0000","313.5800"
+"2018/06/06","319.5000","18742400.0000","300.5000","322.1700","297.4800"
+"2018/06/05","291.1300","5877873.0000","297.7000","297.8000","286.7400"
+"2018/06/04","296.7400","4793787.0000","294.3400","299.0000","293.5480"
+"2018/06/01","291.8200","5417984.0000","285.8600","291.9500","283.8390"
+"2018/05/31","284.7300","5912825.0000","287.2100","290.3700","282.9300"
+"2018/05/30","291.7200","7473590.0000","283.2900","295.0050","281.6000"
+"2018/05/29","283.7600","5657153.0000","278.5100","286.5000","276.1500"
+"2018/05/25","278.8500","3872621.0000","277.6250","279.6400","275.6100"
+"2018/05/24","277.8500","4170789.0000","278.4000","281.1100","274.8900"
+"2018/05/23","279.0700","5972237.0000","277.7600","279.9099","274.0000"
+"2018/05/22","275.0100","8938501.0000","287.7620","288.0000","273.4220"
+"2018/05/21","284.4900","9167844.0000","281.3300","291.4900","281.3000"
+"2018/05/18","276.8200","7243699.0000","284.6500","284.6500","274.0000"
+"2018/05/17","284.5400","4418258.0000","285.9000","289.1873","283.9700"
+"2018/05/16","286.4800","5669644.0000","283.8300","288.8100","281.5600"
+"2018/05/15","284.1800","9509753.0000","285.0100","286.9600","280.5000"
+"2018/05/14","291.9700","7265501.0000","303.3200","304.9400","291.6200"
+"2018/05/11","301.0600","4665321.0000","307.7000","308.8800","299.0800"
+"2018/05/10","305.0200","5642771.0000","307.4997","312.9900","304.1119"
+"2018/05/09","306.8500","5712255.0000","300.4100","307.0100","300.0500"
+"2018/05/08","301.9700","5927447.0000","300.7950","307.7500","299.0000"
+"2018/05/07","302.7700","8673090.0000","297.5000","305.9588","295.1700"
+"2018/05/04","294.0900","8565506.0000","283.0000","296.8600","279.5200"
+"2018/05/03","284.4500","17339090.0000","278.7900","288.0400","275.2300"
+"2018/05/02","301.1500","8346176.0000","298.5700","306.8500","297.7844"
+"2018/05/01","299.9200","4614946.0000","293.5100","300.8200","293.2200"
+"2018/04/30","293.9000","4224068.0000","293.6100","298.7300","292.5000"
+"2018/04/27","294.0750","4346854.0000","285.3700","294.4700","283.8300"
+"2018/04/26","285.4800","4348108.0000","278.7500","285.7900","276.5000"
+"2018/04/25","280.6900","4000421.0000","283.5000","285.1600","277.2500"
+"2018/04/24","283.4600","5678242.0000","285.0000","287.0900","278.4600"
+"2018/04/23","283.3700","4880743.0000","291.2900","291.6200","282.3300"
+"2018/04/20","290.2400","5622630.0000","295.1700","299.9800","289.7500"
+"2018/04/19","300.0800","6083800.0000","291.0800","301.0100","288.5500"
+"2018/04/18","293.3500","6531563.0000","291.0800","300.2400","288.1600"
+"2018/04/17","287.6900","6931152.0000","288.8700","292.1700","282.5100"
+"2018/04/16","291.2100","6289497.0000","299.0000","299.6600","289.0100"
+"2018/04/13","300.3400","7319225.0000","303.6000","303.9499","295.9800"
+"2018/04/12","294.0800","7595525.0000","302.3200","303.9500","293.6800"
+"2018/04/11","300.9300","7470204.0000","300.7400","308.9800","299.6600"
+"2018/04/10","304.7000","11017010.0000","298.9700","307.1000","293.6800"
+"2018/04/09","289.6600","10241410.0000","300.3700","309.5000","289.2100"
+"2018/04/06","299.3000","13516530.0000","301.0000","309.2800","295.5000"
+"2018/04/05","305.7200","19025860.0000","289.3400","306.2600","288.2000"
+"2018/04/04","286.9400","19880180.0000","252.7800","288.3700","252.0000"
+"2018/04/03","267.5300","18829650.0000","269.8200","273.3500","254.4900"
+"2018/04/02","252.4800","16093290.0000","256.2600","260.3318","244.5901"
+"2018/03/29","266.1300","15014440.0000","256.4900","270.9599","248.2100"
+"2018/03/28","257.7800","20962280.0000","264.5800","268.6800","252.1000"
+"2018/03/27","279.1800","13787060.0000","304.0000","304.2700","277.1800"
+"2018/03/26","304.1800","8364937.0000","307.3400","307.5900","291.3600"
+"2018/03/23","301.5400","6639960.0000","311.2500","311.6100","300.4500"
+"2018/03/22","309.1000","4924928.0000","313.8900","318.8200","308.1800"
+"2018/03/21","316.5300","5954247.0000","310.2500","322.4400","310.1900"
+"2018/03/20","310.5500","4761031.0000","314.8700","316.2500","308.7600"
+"2018/03/19","313.5600","7467890.0000","316.5000","320.7500","309.6700"
+"2018/03/16","321.3500","6113365.0000","325.9600","327.4000","319.0700"
+"2018/03/15","325.6000","6554568.0000","329.3800","332.8500","321.1000"
+"2018/03/14","326.6300","7952152.0000","336.7600","339.8100","323.9300"
+"2018/03/13","341.8400","5960636.0000","344.9200","345.1200","336.2635"
+"2018/03/12","345.5100","8252919.0000","328.6100","347.2100","326.5000"
+"2018/03/09","327.1700","5502779.0000","324.1000","328.4900","322.3700"
+"2018/03/08","329.1000","3514367.0000","332.8600","333.3000","326.2740"
+"2018/03/07","332.3000","5001940.0000","325.4400","332.5000","321.7400"
+"2018/03/06","328.2000","4236483.0000","333.7500","336.3700","327.0300"
+"2018/03/05","333.3500","3820702.0000","332.3900","337.7500","329.2929"
+"2018/03/02","335.1200","5088789.0000","326.9800","335.2200","322.9700"
+"2018/03/01","330.9300","6872552.0000","345.0100","348.6700","330.0700"
+"2018/02/28","343.0600","6066044.0000","352.5700","355.2400","342.2200"
+"2018/02/27","350.9900","4785022.0000","356.2500","359.9900","350.0100"
+"2018/02/26","357.4200","4334500.0000","353.5000","359.0000","352.3550"
+"2018/02/23","352.0500","5814177.0000","347.8300","354.9900","347.1000"
+"2018/02/22","346.1700","6961880.0000","335.5300","347.4400","334.7501"
+"2018/02/21","333.3000","3207250.0000","336.0300","339.6929","333.1700"
+"2018/02/20","334.7700","4005962.0000","334.4700","340.8400","331.5000"
+"2018/02/16","335.4900","5640184.0000","332.5000","343.1200","331.6400"
+"2018/02/15","334.0650","5909823.0000","324.5000","334.1200","322.4000"
+"2018/02/14","322.3100","3948077.0000","320.8400","326.1700","318.5200"
+"2018/02/13","323.6600","4554631.0000","315.0200","324.1900","312.5105"
+"2018/02/12","315.7300","6224479.0000","316.1300","318.0800","306.2500"
+"2018/02/09","310.4200","12924430.0000","319.9300","320.9845","294.7600"
+"2018/02/08","315.2300","10296420.0000","343.3100","348.6200","314.6000"
+"2018/02/07","345.0000","6729682.0000","338.9900","346.0000","335.6600"
+"2018/02/06","333.9700","5080032.0000","325.2100","336.2200","323.5000"
+"2018/02/05","333.1300","4441166.0000","337.9700","344.4700","333.0000"
+"2018/02/02","343.7500","3696157.0000","348.4400","351.9500","340.5100"
+"2018/02/01","349.2500","4187440.0000","351.0000","359.6600","348.6300"
+"2018/01/31","354.3100","6199373.0000","347.5100","356.1900","345.1900"
+"2018/01/30","345.8200","4710684.0000","345.1400","348.2700","342.1700"
+"2018/01/29","349.5300","4738208.0000","339.8500","350.8500","338.2800"
+"2018/01/26","342.8500","4535457.0000","341.5000","344.0000","335.7100"
+"2018/01/25","337.6400","6725208.0000","348.2700","349.2000","336.4000"
+"2018/01/24","345.8900","5277386.0000","354.5800","354.7500","343.5200"
+"2018/01/23","352.7900","5447622.0000","360.0000","360.5000","351.0000"
+"2018/01/22","351.5600","6199777.0000","349.4000","357.8300","349.2000"
+"2018/01/19","350.0200","4871181.0000","345.0000","350.5899","342.6000"
+"2018/01/18","344.5700","5679629.0000","345.6700","352.3000","343.7400"
+"2018/01/17","347.1600","7092554.0000","340.4700","349.0000","339.7500"
+"2018/01/16","340.0600","6429234.0000","337.5400","345.0000","334.8000"
+"2018/01/12","336.2200","4822509.0000","338.6300","340.4100","333.6700"
+"2018/01/11","337.9500","6640863.0000","335.2400","344.8099","333.2600"
+"2018/01/10","334.8000","4303053.0000","332.2000","337.0000","330.0000"
+"2018/01/09","333.6900","7134525.0000","335.1600","338.8000","327.4050"
+"2018/01/08","336.4100","9832610.0000","316.0000","337.0199","315.5000"
+"2018/01/05","316.5800","4588728.0000","316.6200","317.2400","312.0000"
+"2018/01/04","314.6200","9944593.0000","312.8700","318.5500","305.6800"
+"2018/01/03","317.2500","4438520.0000","321.0000","325.2500","315.5500"
+"2018/01/02","320.5300","4345615.0000","312.0000","322.1099","311.0000"
+"2017/12/29","311.3500","3769504.0000","316.1800","316.4100","310.0000"
+"2017/12/28","315.3600","4306746.0000","311.7500","315.8200","309.5400"
+"2017/12/27","311.6400","4705084.0000","316.0000","317.6800","310.7500"
+"2017/12/26","317.2900","4368699.0000","323.8300","323.9400","316.5800"
+"2017/12/22","325.2000","4207602.0000","329.5100","330.9214","324.8200"
+"2017/12/21","331.6600","4369994.0000","329.5900","333.7400","327.2100"
+"2017/12/20","328.9800","5907378.0000","332.6900","333.1000","325.0400"
+"2017/12/19","331.1000","6820868.0000","340.2600","341.4925","330.3000"
+"2017/12/18","338.8700","5466546.0000","344.9000","346.7300","337.5800"
+"2017/12/15","343.4500","6921246.0000","342.0400","343.9000","335.7600"
+"2017/12/14","337.8900","5792024.0000","341.0100","347.4400","336.9000"
+"2017/12/13","339.0300","6217701.0000","340.9300","344.2200","336.5000"
+"2017/12/12","341.0300","8715570.0000","330.4500","341.4400","330.0300"
+"2017/12/11","328.9100","7926090.0000","314.6300","329.0100","313.7500"
+"2017/12/08","315.1300","3466088.0000","314.6000","316.9800","311.2600"
+"2017/12/07","311.2400","4779590.0000","312.0000","318.6341","311.0500"
+"2017/12/06","313.2600","7184477.0000","300.1000","313.3900","300.0000"
+"2017/12/05","303.7000","4643708.0000","302.0000","308.0000","301.0000"
+"2017/12/04","305.2000","5834227.0000","306.5000","308.2650","300.6100"
+"2017/12/01","306.5300","4286082.0000","305.4400","310.3200","305.0500"
+"2017/11/30","308.8500","4345434.0000","308.5600","310.7000","304.5400"
+"2017/11/29","307.5400","8757463.0000","317.3000","318.0000","301.2300"
+"2017/11/28","317.5500","4941703.0000","316.3600","320.0000","313.9200"
+"2017/11/27","316.8100","4537779.0000","313.2500","317.3400","309.5100"
+"2017/11/24","315.5500","3244065.0000","313.7900","316.4100","311.0000"
+"2017/11/22","312.6000","4913283.0000","316.7700","317.4200","311.8400"
+"2017/11/21","317.8100","7256381.0000","310.8600","318.2300","308.7100"
+"2017/11/20","308.7400","8241821.0000","313.7900","315.5000","304.7500"
+"2017/11/17","315.0500","13720830.0000","325.6700","326.6700","313.1500"
+"2017/11/16","312.5000","5794409.0000","313.9900","318.1400","311.3000"
+"2017/11/15","311.3000","5951835.0000","306.0100","312.4900","301.5000"
+"2017/11/14","308.7000","5661904.0000","315.0000","316.3500","306.9000"
+"2017/11/13","315.4000","7565242.0000","300.1300","316.8000","299.1100"
+"2017/11/10","302.9900","4621080.0000","302.5000","308.3600","301.8500"
+"2017/11/09","302.9900","5440173.0000","302.5000","304.4600","296.3000"
+"2017/11/08","304.3900","4725355.0000","305.5000","306.8900","301.3001"
+"2017/11/07","306.0500","5284844.0000","301.0200","306.5000","300.0300"
+"2017/11/06","302.7800","6480689.0000","307.0000","307.5000","299.0100"
+"2017/11/03","306.0900","8885933.0000","299.5000","306.2500","295.1300"
+"2017/11/02","299.2600","19771280.0000","300.1300","308.6900","292.6300"
+"2017/11/01","321.0800","8242487.0000","332.2500","332.6089","320.2600"
+"2017/10/31","331.5300","5650062.0000","320.2300","331.9500","320.1800"
+"2017/10/30","320.0800","4247619.0000","319.1800","323.7800","317.2500"
+"2017/10/27","320.8700","6968365.0000","319.7500","324.5900","316.6600"
+"2017/10/26","326.1700","5010087.0000","327.7800","330.2300","323.2000"
+"2017/10/25","325.8400","8583619.0000","336.7000","337.5000","323.5600"
+"2017/10/24","337.3400","4485692.0000","338.8000","342.8000","336.1600"
+"2017/10/23","337.0200","5739598.0000","349.8800","349.9500","336.2500"
+"2017/10/20","345.1000","4925657.0000","352.6900","354.5500","344.3400"
+"2017/10/19","351.8100","5055715.0000","355.5600","357.1465","348.2000"
+"2017/10/18","359.6500","4936024.0000","355.9700","363.0000","354.1300"
+"2017/10/17","355.7500","3290290.0000","350.9100","356.2200","350.0700"
+"2017/10/16","350.6000","5373090.0000","353.7600","354.4800","347.1600"
+"2017/10/13","355.5700","3538807.0000","356.9800","358.4900","353.6800"
+"2017/10/12","355.6800","4073552.0000","352.9500","359.7800","352.6400"
+"2017/10/11","354.6000","4494733.0000","353.8900","357.6000","351.1500"
+"2017/10/10","355.5900","6972263.0000","346.8000","355.6300","345.5305"
+"2017/10/09","342.9400","7461461.0000","349.6500","351.7500","342.6700"
+"2017/10/06","356.8800","4272866.0000","353.1000","360.0992","352.2500"
+"2017/10/05","355.3300","4167913.0000","356.0000","357.4350","351.3500"
+"2017/10/04","355.0100","8147995.0000","351.2500","358.6200","349.6000"
+"2017/10/03","348.1400","10122550.0000","335.9000","348.5500","331.2800"
+"2017/10/02","341.5300","5227547.0000","342.5200","343.7000","335.5100"
+"2017/09/29","341.1000","5099051.0000","341.8600","344.6800","338.6010"
+"2017/09/28","339.6000","5309779.0000","339.8800","342.7500","335.4000"
+"2017/09/27","340.9700","6027375.0000","349.9000","351.4890","340.5000"
+"2017/09/26","345.2500","7149649.0000","350.9300","351.2400","340.9000"
+"2017/09/25","344.9900","7587158.0000","353.1500","357.4690","342.8800"
+"2017/09/22","351.0900","8142895.0000","366.4900","369.8999","350.8800"
+"2017/09/21","366.4800","4610872.0000","374.9000","376.8300","364.5100"
+"2017/09/20","373.9100","4909958.0000","373.0000","378.2490","371.0700"
+"2017/09/19","375.1000","6435208.0000","380.0000","382.3900","373.5700"
+"2017/09/18","385.0000","7177773.0000","380.2500","389.6100","377.6800"
+"2017/09/15","379.8100","5407227.0000","374.5100","380.0000","372.7000"
+"2017/09/14","377.6400","7170263.0000","364.3300","377.9600","362.6300"
+"2017/09/13","366.2300","4171185.0000","363.8200","368.0700","359.5900"
+"2017/09/12","362.7500","5966204.0000","364.4900","368.7600","360.4000"
+"2017/09/11","363.6900","7651000.0000","351.3500","363.7100","350.0000"
+"2017/09/08","343.4000","3261333.0000","348.9900","349.7800","342.3000"
+"2017/09/07","350.6100","4235045.0000","345.9800","352.4800","343.4500"
+"2017/09/06","344.5300","4082001.0000","349.5000","350.9790","341.5600"
+"2017/09/05","349.5900","3836402.0000","353.8000","355.4900","345.8900"
+"2017/09/01","355.4000","3048550.0000","356.1200","357.5900","353.6902"
+"2017/08/31","355.9000","4063330.0000","353.5500","358.4400","352.8200"
+"2017/08/30","353.1800","3404165.0000","349.6700","353.4700","347.0000"
+"2017/08/29","347.3600","4069303.0000","339.4800","349.0500","338.7500"
+"2017/08/28","345.6600","3746240.0000","347.2800","347.3500","339.7200"
+"2017/08/25","348.0500","3475186.0000","354.2400","355.6900","347.3000"
+"2017/08/24","352.9300","4571574.0000","352.5200","356.6600","349.7400"
+"2017/08/23","352.7700","4941947.0000","338.9900","353.4900","338.3041"
+"2017/08/22","341.3500","4314268.0000","341.1300","342.2400","337.3725"
+"2017/08/21","337.8600","6489850.0000","345.8200","345.8200","331.8500"
+"2017/08/18","347.4600","5390805.0000","352.9100","354.0000","345.8000"
+"2017/08/17","351.9200","5010825.0000","361.2100","363.3000","351.5900"
+"2017/08/16","362.9100","3406173.0000","363.0000","366.5000","362.5200"
+"2017/08/15","362.3300","3084601.0000","365.2000","365.4900","359.3700"
+"2017/08/14","363.8000","4507969.0000","364.6300","367.6600","362.6000"
+"2017/08/11","357.8700","4357562.0000","356.9700","361.2600","353.6200"
+"2017/08/10","355.4000","7063054.0000","361.6000","366.6504","354.6600"
+"2017/08/09","363.5300","6881182.0000","361.0000","370.0000","358.9500"
+"2017/08/08","365.2200","7431838.0000","357.5300","368.5800","357.4000"
+"2017/08/07","355.1700","6309331.0000","357.3500","359.4800","352.7500"
+"2017/08/04","356.9100","9233295.0000","347.0000","357.2700","343.3000"
+"2017/08/03","347.0900","13524270.0000","345.3300","350.0000","343.1500"
+"2017/08/02","325.8900","11942350.0000","318.9400","327.1200","311.2200"
+"2017/08/01","319.5700","8284997.0000","323.0000","324.4500","316.1300"
+"2017/07/31","323.4700","8525454.0000","335.5000","341.4900","321.0400"
+"2017/07/28","335.0700","4869891.0000","336.8900","339.6000","332.5100"
+"2017/07/27","334.4600","8285879.0000","346.0000","347.5000","326.2900"
+"2017/07/26","343.8500","4809514.0000","340.3600","345.5000","338.1200"
+"2017/07/25","339.6000","6977932.0000","345.0000","345.6000","334.1500"
+"2017/07/24","342.5200","8598292.0000","330.2400","343.3990","330.0100"
+"2017/07/21","328.4000","4871708.0000","329.4600","331.2575","325.8000"
+"2017/07/20","329.9200","5142516.0000","326.9000","330.2200","324.2000"
+"2017/07/19","325.2600","6345884.0000","328.2300","331.6500","323.2193"
+"2017/07/18","328.2400","6360710.0000","317.5000","329.1300","315.6600"
+"2017/07/17","319.5700","9827506.0000","325.5400","327.1000","313.4500"
+"2017/07/14","327.7800","5607924.0000","323.1900","328.4200","321.2200"
+"2017/07/13","323.4100","8582315.0000","330.1100","331.6000","319.9700"
+"2017/07/12","329.5200","10317240.0000","330.4000","333.1000","324.5000"
+"2017/07/11","327.2200","11526760.0000","316.0000","327.2800","314.3000"
+"2017/07/10","316.0500","13760290.0000","312.9000","317.9400","303.1300"
+"2017/07/07","313.2200","14129950.0000","313.5000","317.0000","307.3800"
+"2017/07/06","308.8300","19258040.0000","317.2600","320.7899","306.3000"
+"2017/07/05","327.0900","16998300.0000","347.2000","347.2400","326.3300"
+"2017/07/03","352.6200","6305401.0000","370.2400","371.3500","351.5000"
+"2017/06/30","361.6100","5823046.0000","363.7100","366.7674","359.6187"
+"2017/06/29","360.7500","8202463.0000","370.6100","371.0000","354.1000"
+"2017/06/28","371.2400","6290323.0000","366.6800","371.7400","362.5200"
+"2017/06/27","362.3700","6977494.0000","376.4000","376.4000","362.0200"
+"2017/06/26","377.4900","6596854.0000","386.6900","386.9500","373.1000"
+"2017/06/23","383.4500","6425180.0000","382.4500","386.9900","379.3450"
+"2017/06/22","382.6100","7510406.0000","377.9900","385.0000","373.5700"
+"2017/06/21","376.4000","4916770.0000","374.3500","376.9900","368.0200"
+"2017/06/20","372.2400","7419110.0000","376.6700","378.8800","369.7300"
+"2017/06/19","369.8000","6433434.0000","375.0000","376.7000","367.8000"
+"2017/06/16","371.4000","6347450.0000","377.9750","378.0100","370.1000"
+"2017/06/15","375.3400","10412650.0000","372.5000","375.4600","366.4900"
+"2017/06/14","380.6600","12778480.0000","381.0850","384.2500","376.3100"
+"2017/06/13","375.9500","11773370.0000","367.6200","376.0000","366.6100"
+"2017/06/12","359.0100","10507860.0000","357.9900","364.5000","350.6200"
+"2017/06/09","357.3200","17250060.0000","374.4200","376.8700","354.8000"
+"2017/06/08","370.0000","9028677.0000","363.7500","371.9000","360.2200"
+"2017/06/07","359.6500","9348692.0000","356.3400","360.5000","355.1400"
+"2017/06/06","352.8500","11031920.0000","344.7000","359.4929","339.9700"
+"2017/06/05","347.3200","6769174.0000","338.5000","348.4400","334.2100"
+"2017/06/02","339.8500","5583952.0000","339.7700","342.8800","335.9300"
+"2017/06/01","340.3700","7601764.0000","344.0000","344.8800","337.2900"
+"2017/05/31","341.0100","9937556.0000","337.6900","342.8900","335.1600"
+"2017/05/30","335.1000","7771536.0000","326.0000","336.2800","325.7600"
+"2017/05/26","325.1400","7793009.0000","317.2800","325.4900","316.3100"
+"2017/05/25","316.8300","5000432.0000","311.0200","316.9700","307.8100"
+"2017/05/24","310.2200","5035192.0000","306.5100","311.0000","305.4000"
+"2017/05/23","303.8600","4314267.0000","310.4600","310.7300","303.4800"
+"2017/05/22","310.3500","4324305.0000","312.8000","314.3700","306.8000"
+"2017/05/19","310.8300","4654580.0000","315.5000","316.5000","310.2000"
+"2017/05/18","313.0600","5609153.0000","307.0000","313.9400","305.3100"
+"2017/05/17","306.1100","6695657.0000","314.3900","314.6300","305.5000"
+"2017/05/16","317.0100","4141066.0000","317.5900","320.0600","315.1400"
+"2017/05/15","315.8800","7606854.0000","318.3800","320.2000","312.5300"
+"2017/05/12","324.8100","4118613.0000","325.4800","327.0000","321.5300"
+"2017/05/11","323.1000","4747172.0000","323.4000","326.0000","319.6000"
+"2017/05/10","325.2200","5734524.0000","321.5600","325.5000","318.1200"
+"2017/05/09","321.2600","9663374.0000","309.3800","321.9900","309.1000"
+"2017/05/08","307.1900","7002907.0000","310.9000","313.7900","305.8200"
+"2017/05/05","308.3500","8117449.0000","298.0000","308.5500","296.8000"
+"2017/05/04","295.4600","14135990.0000","307.4350","307.7700","290.7601"
+"2017/05/03","311.0200","6879259.0000","317.6700","321.5300","310.4500"
+"2017/05/02","318.8900","5316551.0000","324.0000","327.6599","316.5601"
+"2017/05/01","322.8300","8819888.0000","314.8800","327.2500","314.8100"
+"2017/04/28","314.0700","4496659.0000","309.8300","314.8000","308.0000"
+"2017/04/27","308.6300","3462663.0000","311.6900","313.0900","307.5000"
+"2017/04/26","310.1700","4683131.0000","312.3700","314.5000","309.0000"
+"2017/04/25","313.7900","6734162.0000","308.0000","313.9800","305.8600"
+"2017/04/24","308.0300","5077771.0000","309.2200","310.5500","306.0215"
+"2017/04/21","305.6000","4501958.0000","302.0000","306.4000","300.4200"
+"2017/04/20","302.5100","6145961.0000","306.5100","309.1500","300.2300"
+"2017/04/19","305.5200","3891145.0000","302.4600","306.6200","302.1100"
+"2017/04/18","300.2500","3034225.0000","299.7000","300.8399","297.9000"
+"2017/04/17","301.4400","4128067.0000","302.7000","304.0000","298.6800"
+"2017/04/13","304.0000","9275682.0000","296.7000","307.3900","295.3000"
+"2017/04/12","296.8400","6043648.0000","306.3400","308.4481","296.3200"
+"2017/04/11","308.7100","5718053.0000","313.3800","313.4700","305.5000"
+"2017/04/10","312.3900","7653623.0000","309.1500","313.7299","308.7100"
+"2017/04/07","302.5400","4566632.0000","297.5000","302.6900","297.1500"
+"2017/04/06","298.7000","5517731.0000","296.8800","301.9400","294.1000"
+"2017/04/05","295.0000","7858565.0000","302.0400","304.8800","294.2000"
+"2017/04/04","303.7000","10108230.0000","296.8900","304.8100","294.5300"
+"2017/04/03","298.5200","13864850.0000","286.9000","299.0000","284.5800"
+"2017/03/31","278.3000","3293698.0000","278.7300","279.6800","276.3197"
+"2017/03/30","277.9200","4141437.0000","278.0400","282.0000","277.2100"
+"2017/03/29","277.3800","3672526.0000","278.3400","279.6000","275.5400"
+"2017/03/28","277.4500","7978665.0000","277.0200","280.6800","275.0000"
+"2017/03/27","270.2200","6221361.0000","260.6000","270.5700","259.7500"
+"2017/03/24","263.1600","5637668.0000","255.7000","263.8900","255.0100"
+"2017/03/23","254.7800","3309844.0000","255.3900","257.6720","253.3000"
+"2017/03/22","255.0100","4056735.0000","251.5600","255.0700","250.5100"
+"2017/03/21","250.6800","6901555.0000","262.8300","264.8000","250.2400"
+"2017/03/20","261.9200","3601616.0000","260.6000","264.5500","258.8210"
+"2017/03/17","261.5000","6491018.0000","264.0000","265.3300","261.2000"
+"2017/03/16","262.0500","7127180.0000","262.4000","265.7500","259.0600"
+"2017/03/15","255.7300","5233365.0000","257.0000","261.0000","254.2700"
+"2017/03/14","258.0000","7581719.0000","246.1100","258.1200","246.0200"
+"2017/03/13","246.1700","3011280.0000","244.8200","246.8500","242.7810"
+"2017/03/10","243.6900","3062785.0000","246.2100","246.5000","243.0000"
+"2017/03/09","244.9000","3876494.0000","247.6300","248.6600","243.0000"
+"2017/03/08","246.8700","3726746.0000","247.0000","250.0700","245.3200"
+"2017/03/07","248.5900","3452587.0000","251.9200","253.8900","248.3200"
+"2017/03/06","251.2100","3353601.0000","247.9100","251.7000","247.5100"
+"2017/03/03","251.5700","2925481.0000","250.7400","251.9000","249.0000"
+"2017/03/02","250.4800","3345751.0000","249.7100","253.2800","248.2700"
+"2017/03/01","250.0200","4804963.0000","254.1800","254.8500","249.1100"
+"2017/02/28","249.9900","6073890.0000","244.1900","251.0000","243.9000"
+"2017/02/27","246.2300","11450160.0000","248.1700","248.3600","242.0100"
+"2017/02/24","257.0000","8166869.0000","252.6600","258.2500","250.2000"
+"2017/02/23","255.9900","14877090.0000","264.0000","264.6600","255.5600"
+"2017/02/22","273.5100","8537811.0000","280.3100","283.4500","272.6000"
+"2017/02/21","277.3900","5647575.0000","275.4500","281.4000","274.0100"
+"2017/02/17","272.2300","6251469.0000","265.8000","272.8900","264.1500"
+"2017/02/16","268.9500","7063860.0000","277.6000","280.0000","268.5000"
+"2017/02/15","279.7600","4943879.0000","280.0000","282.2400","276.4400"
+"2017/02/14","280.9800","7341450.0000","279.0300","287.3900","278.6100"
+"2017/02/13","280.6000","7023072.0000","270.7400","280.7899","270.5100"
+"2017/02/10","269.2300","3618336.0000","269.7900","270.9500","266.1100"
+"2017/02/09","269.2000","7812600.0000","266.2500","271.1800","266.1500"
+"2017/02/08","262.0800","3912428.0000","257.3500","263.3600","256.2000"
+"2017/02/07","257.4800","4244063.0000","258.1900","260.0000","256.4200"
+"2017/02/06","257.7700","3557600.0000","251.0000","257.8200","250.6300"
+"2017/02/03","251.3300","2185230.0000","251.9100","252.1790","249.6800"
+"2017/02/02","251.5500","2498799.0000","248.3400","252.4200","247.7100"
+"2017/02/01","249.2400","3953105.0000","253.0500","253.2000","249.0500"
+"2017/01/31","251.9300","4112013.0000","249.2400","255.8900","247.7000"
+"2017/01/30","250.6300","3798638.0000","252.5300","255.2899","247.1000"
+"2017/01/27","252.9500","3161774.0000","251.3800","253.0000","248.5200"
+"2017/01/26","252.5100","3143717.0000","254.2900","255.7400","250.7500"
+"2017/01/25","254.4700","5145301.0000","257.3100","258.4600","251.8000"
+"2017/01/24","254.6100","4958144.0000","250.0000","254.8000","249.6500"
+"2017/01/23","248.9200","6262097.0000","245.8500","250.8899","245.5000"
+"2017/01/20","244.7300","4199720.0000","245.4600","246.0000","243.0100"
+"2017/01/19","243.7600","7717244.0000","247.2500","248.6800","240.7500"
+"2017/01/18","238.3600","3767272.0000","236.6500","239.7100","235.5800"
+"2017/01/17","235.5800","4614731.0000","236.7000","239.9600","234.3700"
+"2017/01/13","237.7500","6085610.0000","230.0000","237.8500","229.5900"
+"2017/01/12","229.5900","3787547.0000","229.0600","230.7000","225.5800"
+"2017/01/11","229.7300","3649910.0000","229.0700","229.9800","226.6800"
+"2017/01/10","229.8700","3658981.0000","232.0000","232.0000","226.8900"
+"2017/01/09","231.2800","3972716.0000","228.9700","231.9200","228.0000"
+"2017/01/06","229.0100","5524153.0000","226.9300","230.3100","225.4500"
+"2017/01/05","226.7500","5908927.0000","226.4200","227.4800","221.9500"
+"2017/01/04","226.9900","11204560.0000","214.7500","228.0000","214.3100"
+"2017/01/03","216.9900","5901494.0000","214.8600","220.3300","210.9600"
+"2016/12/30","213.6900","4641288.0000","216.3000","217.5000","211.6800"
+"2016/12/29","214.6800","4043086.0000","218.5600","219.2000","214.1225"
+"2016/12/28","219.7400","3778544.0000","221.5300","223.8000","217.2000"
+"2016/12/27","219.5300","5913335.0000","214.8800","222.2500","214.4200"
+"2016/12/23","213.3400","4668263.0000","208.0000","213.4500","207.7100"
+"2016/12/22","208.4500","3108427.0000","208.2200","209.9900","206.5000"
+"2016/12/21","207.7000","5206143.0000","208.4500","212.2300","207.4100"
+"2016/12/20","208.7900","4684269.0000","203.0500","209.0000","202.5000"
+"2016/12/19","202.7300","3486937.0000","202.4900","204.4500","199.8400"
+"2016/12/16","202.4900","3795444.0000","198.0800","202.5900","197.6000"
+"2016/12/15","197.5800","3218653.0000","198.4100","200.7400","197.3900"
+"2016/12/14","198.6900","4147314.0000","198.7400","203.0000","196.7600"
+"2016/12/13","198.1500","6819322.0000","193.1800","201.2800","193.0000"
+"2016/12/12","192.4300","2436428.0000","192.8000","194.4200","191.0400"
+"2016/12/09","192.1800","2720937.0000","190.8700","193.8360","190.8100"
+"2016/12/08","192.2900","3193684.0000","192.0500","192.5000","189.5400"
+"2016/12/07","193.1500","5457071.0000","186.1500","193.4000","185.0000"
+"2016/12/06","185.8500","3389365.0000","185.5200","186.5800","182.6825"
+"2016/12/05","186.8000","4069480.0000","182.5100","188.8900","182.5100"
+"2016/12/02","181.4700","4040201.0000","182.8800","184.8800","180.0000"
+"2016/12/01","181.8800","5119105.0000","188.2500","188.5300","181.0000"
+"2016/11/30","189.4000","3544891.0000","191.0000","191.8900","187.5000"
+"2016/11/29","189.5700","4435312.0000","195.5600","196.7300","189.5000"
+"2016/11/28","196.1200","4521082.0000","195.4800","199.3500","194.5500"
+"2016/11/25","196.6500","2366098.0000","193.6400","197.2372","193.6400"
+"2016/11/23","193.1400","4890285.0000","190.6100","195.6440","189.0000"
+"2016/11/22","191.1700","5601238.0000","185.8400","191.4700","183.7100"
+"2016/11/21","184.5200","4355872.0000","185.0400","188.8900","184.4100"
+"2016/11/18","185.0200","5206473.0000","190.6500","193.0000","185.0000"
+"2016/11/17","188.6600","4880969.0000","183.4900","189.4900","182.1101"
+"2016/11/16","183.9300","3432668.0000","182.6500","184.7300","181.2100"
+"2016/11/15","183.7700","3899743.0000","182.7800","186.4300","182.0500"
+"2016/11/14","181.4500","6547913.0000","188.0000","188.2500","178.1900"
+"2016/11/11","188.5600","3987091.0000","184.2400","188.8800","183.0000"
+"2016/11/10","185.3500","6746290.0000","191.0500","191.6100","180.4200"
+"2016/11/09","190.0600","8166447.0000","186.8750","192.0000","183.9500"
+"2016/11/08","194.9400","3252927.0000","193.7900","197.4900","191.2600"
+"2016/11/07","193.2100","3857682.0000","193.5900","194.2900","190.0500"
+"2016/11/04","190.5600","5143915.0000","189.0000","193.4600","185.9600"
+"2016/11/03","187.4200","2644550.0000","189.0000","191.4700","187.0401"
+"2016/11/02","188.0200","4242250.0000","190.0500","192.6951","187.5050"
+"2016/11/01","190.7900","7023674.0000","198.0400","198.5000","188.1050"
+"2016/10/31","197.7300","4691320.0000","202.4900","202.4900","195.8100"
+"2016/10/28","199.9700","4273456.0000","204.0000","205.3200","199.8300"
+"2016/10/27","204.0100","13070990.0000","211.3400","213.7000","201.6500"
+"2016/10/26","202.2400","5346893.0000","201.0000","203.1900","200.1000"
+"2016/10/25","202.3400","2442118.0000","202.9000","204.6900","201.2000"
+"2016/10/24","202.7600","2747344.0000","201.0000","203.9452","200.2500"
+"2016/10/21","200.0900","2936908.0000","198.6000","201.5700","197.4100"
+"2016/10/20","199.1000","5069410.0000","202.1200","203.0000","197.0500"
+"2016/10/19","203.5600","6966185.0000","199.7400","206.6600","198.0600"
+"2016/10/18","199.1000","5676046.0000","195.9900","199.4700","193.2600"
+"2016/10/17","193.9600","4518331.0000","197.0500","198.3900","192.0000"
+"2016/10/14","196.5100","4265471.0000","200.6600","201.4300","196.3000"
+"2016/10/13","200.2400","2495140.0000","200.5000","200.8950","197.0500"
+"2016/10/12","201.5100","1969099.0000","200.9500","203.8800","200.4200"
+"2016/10/11","200.1000","2326754.0000","201.8500","202.2000","198.3100"
+"2016/10/10","200.9500","3311340.0000","201.3500","204.1400","199.6600"
+"2016/10/07","196.6100","3489480.0000","201.0000","201.3200","195.8000"
+"2016/10/06","201.0000","4697937.0000","202.4600","204.2099","200.2100"
+"2016/10/05","208.4600","1872456.0000","212.2400","213.1500","208.1200"
+"2016/10/04","211.4100","3537953.0000","213.1000","213.3200","208.8200"
+"2016/10/03","213.7000","5989342.0000","212.3000","215.6688","208.2500"
+"2016/09/30","204.0300","2581730.0000","202.2100","204.9800","199.5500"
+"2016/09/29","200.7000","2714040.0000","205.6000","207.3300","200.5800"
+"2016/09/28","206.2700","2079324.0000","207.5100","208.2500","205.2600"
+"2016/09/27","205.8100","3367907.0000","209.6500","209.9818","204.6093"
+"2016/09/26","208.9900","2393373.0000","206.5000","211.0000","206.5000"
+"2016/09/23","207.4500","2904125.0000","205.9900","210.1800","205.6700"
+"2016/09/22","206.4300","2381600.0000","206.4000","207.2800","203.0000"
+"2016/09/21","205.2200","2631285.0000","206.3700","207.0000","201.5600"
+"2016/09/20","204.6400","2408556.0000","206.8500","207.7500","203.9100"
+"2016/09/19","206.3400","2298318.0000","207.0000","209.4300","205.0000"
+"2016/09/16","205.4000","3104266.0000","200.4200","205.7000","199.0000"
+"2016/09/15","200.4200","3082942.0000","196.4900","202.5193","196.4000"
+"2016/09/14","196.4100","2256143.0000","195.7500","197.9248","194.8562"
+"2016/09/13","196.0500","3583140.0000","197.0600","198.4900","193.4500"
+"2016/09/12","198.3000","3714049.0000","195.0000","201.3690","194.1000"
+"2016/09/09","194.4700","3753734.0000","199.0900","199.9200","193.7000"
+"2016/09/08","197.3600","3372293.0000","199.5500","199.8900","196.3600"
+"2016/09/07","201.7100","3637918.0000","205.5000","206.4968","200.7100"
+"2016/09/06","202.8300","4386193.0000","199.0200","203.2500","199.0000"
+"2016/09/02","197.7800","5970154.0000","202.3300","203.2000","196.2000"
+"2016/09/01","200.7700","7932444.0000","209.0100","211.0999","200.5000"
+"2016/08/31","212.0100","3274854.0000","210.4300","212.6000","208.6500"
+"2016/08/30","211.3400","3164222.0000","216.1100","216.1100","210.5200"
+"2016/08/29","215.2000","3261381.0000","220.1500","220.4000","215.0000"
+"2016/08/26","219.9900","2238087.0000","222.1400","222.8550","218.8200"
+"2016/08/25","220.9600","1760212.0000","223.1100","223.8000","220.7700"
+"2016/08/24","222.6200","2566947.0000","227.0500","227.1500","222.2200"
+"2016/08/23","224.8400","4747745.0000","224.3200","228.4900","222.8000"
+"2016/08/22","222.9300","2061692.0000","224.1700","225.1100","222.6800"
+"2016/08/19","225.0000","1658616.0000","223.5400","225.1690","222.5300"
+"2016/08/18","223.5100","1699300.0000","223.8200","225.6600","222.2900"
+"2016/08/17","223.2400","1786408.0000","224.3300","224.8300","222.8000"
+"2016/08/16","223.6100","2251115.0000","225.4900","227.1900","223.4101"
+"2016/08/15","225.5900","2017964.0000","226.0200","229.5000","224.9300"
+"2016/08/12","225.6100","1811850.0000","225.4100","226.6500","224.0400"
+"2016/08/11","224.9100","1876959.0000","226.1700","227.5700","223.4100"
+"2016/08/10","225.6500","2336496.0000","228.2400","229.8700","224.6200"
+"2016/08/09","229.0800","2199766.0000","226.8200","231.5375","226.6500"
+"2016/08/08","226.1600","2259465.0000","228.0000","229.6000","226.0900"
+"2016/08/05","230.0300","3190342.0000","230.0000","232.0000","227.4000"
+"2016/08/04","230.6100","4142155.0000","225.6900","230.8600","222.0500"
+"2016/08/03","225.7900","3808747.0000","227.3700","229.6990","224.2100"
+"2016/08/02","227.2000","3928706.0000","229.3700","229.8700","221.4000"
+"2016/08/01","230.0100","4014048.0000","235.5000","236.6300","229.3800"
+"2016/07/29","234.7900","3061574.0000","230.7000","235.2800","230.2400"
+"2016/07/28","230.6100","2415462.0000","227.9500","230.7600","226.6000"
+"2016/07/27","228.4900","2887028.0000","229.3400","233.3600","226.9200"
+"2016/07/26","229.5100","3427251.0000","227.6900","230.0000","225.3000"
+"2016/07/25","230.0100","4479483.0000","222.2700","231.3900","221.3715"
+"2016/07/22","222.2700","2570462.0000","221.9900","224.5000","218.8800"
+"2016/07/21","220.5000","4425756.0000","226.0000","227.8470","219.1000"
+"2016/07/20","228.3600","2556119.0000","226.4700","229.8000","225.0000"
+"2016/07/19","225.2600","3047535.0000","225.0000","229.1000","224.7500"
+"2016/07/18","226.2500","3394588.0000","219.6400","227.0900","218.3000"
+"2016/07/15","220.4000","2230821.0000","222.5200","222.7499","219.6400"
+"2016/07/14","221.5300","2657899.0000","223.1200","224.9400","221.0500"
+"2016/07/13","222.5300","3540699.0000","225.5000","225.5900","220.2900"
+"2016/07/12","224.6500","4573875.0000","224.1000","227.5000","223.2200"
+"2016/07/11","224.7800","5369066.0000","219.9600","226.7800","219.5100"
+"2016/07/08","216.7800","4067131.0000","217.8000","219.8100","214.5000"
+"2016/07/07","215.9400","3603385.0000","213.1000","218.1200","213.0100"
+"2016/07/06","214.4400","4910285.0000","210.0000","215.2300","209.0000"
+"2016/07/05","213.9800","5169832.0000","209.7300","214.5441","208.0000"
+"2016/07/01","216.5000","5396030.0000","206.1400","218.2400","206.0000"
+"2016/06/30","212.2800","4794440.0000","212.9700","213.4999","209.0200"
+"2016/06/29","210.1900","5987120.0000","205.1300","211.7800","203.0000"
+"2016/06/28","201.7900","6158996.0000","201.8900","204.0500","199.4100"
+"2016/06/27","198.5500","7211823.0000","190.8600","198.8100","187.8700"
+"2016/06/24","193.1500","6988949.0000","190.0500","195.1200","189.7300"
+"2016/06/23","196.4000","10098190.0000","195.6900","197.5500","192.1300"
+"2016/06/22","196.6600","23706100.0000","199.4700","205.9500","195.7500"
+"2016/06/21","219.6100","2855619.0000","220.6800","222.5690","218.8100"
+"2016/06/20","219.7000","3549768.0000","219.5000","223.7500","218.2300"
+"2016/06/17","215.4700","3107734.0000","217.8100","219.9900","214.5000"
+"2016/06/16","217.9300","2438692.0000","217.4200","218.0400","213.5000"
+"2016/06/15","217.7000","2905002.0000","216.9500","221.9000","215.1300"
+"2016/06/14","214.9600","3575822.0000","218.8800","222.2000","212.5300"
+"2016/06/13","217.8700","4187833.0000","219.5000","225.7700","217.6600"
+"2016/06/10","218.7900","5994548.0000","227.3900","227.9700","218.4217"
+"2016/06/09","229.3600","4454222.0000","234.9800","235.3300","227.0600"
+"2016/06/08","235.5200","5965110.0000","233.8000","240.8450","232.6050"
+"2016/06/07","232.3400","6194154.0000","222.2400","234.4400","221.5200"
+"2016/06/06","220.6800","2242634.0000","218.0000","220.9000","215.4500"
+"2016/06/03","218.9900","2226110.0000","220.0000","221.9400","218.0100"
+"2016/06/02","218.9600","2017279.0000","219.5900","219.9090","217.1100"
+"2016/06/01","219.5600","2982270.0000","221.4800","222.4000","216.8900"
+"2016/05/31","223.2300","2786682.0000","223.0400","224.7497","221.5001"
+"2016/05/27","223.0400","3645792.0000","224.9900","225.9300","220.7500"
+"2016/05/26","225.1200","4066512.0000","220.5000","225.2600","219.0500"
+"2016/05/25","219.5800","3131279.0000","217.9100","221.3600","216.5100"
+"2016/05/24","217.9100","3009327.0000","216.6000","218.7400","215.1800"
+"2016/05/23","216.2200","5096751.0000","219.8700","222.6000","215.8600"
+"2016/05/20","220.2800","8996546.0000","216.9900","220.5500","216.3500"
+"2016/05/19","215.2100","6817922.0000","213.6200","216.7900","207.3000"
+"2016/05/18","211.1700","5552692.0000","209.1500","215.3100","207.7500"
+"2016/05/17","204.6600","2842028.0000","209.0500","209.8199","204.0200"
+"2016/05/16","208.2900","2945754.0000","208.1500","213.1500","207.9200"
+"2016/05/13","207.6100","2820616.0000","207.7800","211.2000","206.7000"
+"2016/05/12","207.2800","3650075.0000","211.4400","211.6700","203.6572"
+"2016/05/11","208.9600","5159640.0000","207.5900","215.4800","206.0500"
+"2016/05/10","208.6900","4068053.0000","207.5500","209.4700","205.0000"
+"2016/05/09","208.9200","4772992.0000","215.7200","216.1500","206.8000"
+"2016/05/06","214.9300","5683675.0000","210.8700","216.3700","208.1100"
+"2016/05/05","211.5300","11248340.0000","228.4600","228.6400","209.7900"
+"2016/05/04","222.5600","8550004.0000","230.2900","234.4600","220.4000"
+"2016/05/03","232.3200","4296325.0000","237.3600","238.9100","231.6200"
+"2016/05/02","241.8000","3838958.0000","241.5000","243.1900","234.8200"
+"2016/04/29","240.7600","5405164.0000","248.1400","248.4300","237.8100"
+"2016/04/28","247.7100","2515595.0000","249.8500","253.4300","247.4400"
+"2016/04/27","251.4700","3198105.0000","252.7500","255.0000","249.4000"
+"2016/04/26","253.7400","3217779.0000","252.0500","255.7300","249.3900"
+"2016/04/25","251.8200","3665088.0000","253.0100","257.3800","250.7600"
+"2016/04/22","253.7500","3783126.0000","248.8900","254.0000","245.7100"
+"2016/04/21","248.2900","2777079.0000","248.9900","250.9000","246.9100"
+"2016/04/20","249.9700","5192343.0000","246.2600","253.6600","241.5000"
+"2016/04/19","247.3700","6351360.0000","253.1200","254.3699","241.2510"
+"2016/04/18","253.8800","4263246.0000","252.2300","258.3100","251.6600"
+"2016/04/15","254.5100","3748593.0000","251.3100","254.6000","249.1200"
+"2016/04/14","251.8600","4129258.0000","253.0000","256.8390","251.0501"
+"2016/04/13","254.5300","4921554.0000","248.5100","255.5000","247.3300"
+"2016/04/12","247.8200","5755351.0000","249.5000","251.8000","243.6300"
+"2016/04/11","249.9200","9156404.0000","251.0000","258.9900","245.3000"
+"2016/04/08","250.0700","7352707.0000","260.5000","260.8200","248.0201"
+"2016/04/07","257.2000","8845567.0000","266.4500","269.3400","254.5100"
+"2016/04/06","265.4200","11695760.0000","253.9700","267.7400","253.4500"
+"2016/04/05","255.4700","9931470.0000","240.5000","256.5600","240.0000"
+"2016/04/04","246.9900","13381160.0000","249.1200","252.1200","243.6400"
+"2016/04/01","237.5900","15979910.0000","244.8250","247.9000","233.2500"
+"2016/03/31","229.7700","7975638.0000","229.3400","237.4200","225.0100"
+"2016/03/30","226.8900","4025670.0000","235.0900","235.5000","226.5000"
+"2016/03/29","230.1300","4006037.0000","229.8900","232.3800","225.3300"
+"2016/03/28","230.2600","3878599.0000","231.6100","234.8100","225.0000"
+"2016/03/24","227.7500","4956604.0000","215.7800","228.8877","215.0000"
+"2016/03/23","222.5800","4936203.0000","232.3700","234.7300","222.0300"
+"2016/03/22","234.2400","4303052.0000","237.2100","238.9900","232.5580"
+"2016/03/21","238.3200","5289014.0000","235.3400","239.8800","235.0000"
+"2016/03/18","232.7400","4704761.0000","229.1000","234.4800","228.0600"
+"2016/03/17","226.3800","3769083.0000","221.4700","228.5000","220.0000"
+"2016/03/16","221.9300","3511873.0000","218.0000","222.5800","217.0200"
+"2016/03/15","218.3400","3133230.0000","214.2700","218.9700","211.5000"
+"2016/03/14","215.1500","4062304.0000","212.6500","216.7200","210.6400"
+"2016/03/11","207.5000","3339148.0000","207.9300","209.4200","205.3300"
+"2016/03/10","205.1800","5191602.0000","210.0000","213.2900","200.6710"
+"2016/03/09","208.7200","3207597.0000","204.5200","209.3726","202.7900"
+"2016/03/08","202.6000","4175498.0000","203.5000","207.5000","202.2000"
+"2016/03/07","205.2900","5331097.0000","197.6800","209.7000","197.4000"
+"2016/03/04","201.0400","6479858.0000","198.0000","204.0300","197.5001"
+"2016/03/03","195.7400","4822993.0000","188.2800","197.4200","184.2200"
+"2016/03/02","188.3400","4858631.0000","183.7300","188.5200","181.5000"
+"2016/03/01","186.3500","6695704.0000","194.2500","195.9484","182.7000"
+"2016/02/29","191.9300","4495402.0000","192.4000","196.3500","189.2220"
+"2016/02/26","190.3400","6054061.0000","188.7000","192.0000","185.0000"
+"2016/02/25","187.4300","5727008.0000","178.6500","188.5192","175.2000"
+"2016/02/24","179.0000","5385369.0000","172.7500","179.5000","167.8400"
+"2016/02/23","177.2100","5982993.0000","176.1600","181.7300","173.6800"
+"2016/02/22","177.7400","5055340.0000","170.1200","178.9100","169.8500"
+"2016/02/19","166.5800","2955832.0000","163.6600","167.4900","162.5000"
+"2016/02/18","166.7700","3883107.0000","172.4200","172.9500","164.7700"
+"2016/02/17","168.6800","5820264.0000","159.0000","169.3400","156.6800"
+"2016/02/16","155.1700","5561638.0000","158.7000","162.9500","154.1100"
+"2016/02/12","151.0400","7233264.0000","155.0000","157.0100","143.7000"
+"2016/02/11","150.4700","14236900.0000","152.0000","163.2600","147.0000"
+"2016/02/10","143.6700","10104690.0000","150.5000","154.9700","141.7400"
+"2016/02/09","148.2500","8639661.0000","142.3200","159.7900","141.0500"
+"2016/02/08","147.9900","9306754.0000","157.1050","157.1500","146.0000"
+"2016/02/05","162.6000","9434495.0000","171.3000","173.0000","157.7442"
+"2016/02/04","175.3300","4379442.0000","170.7000","175.9800","166.9900"
+"2016/02/03","173.4800","7927640.0000","183.5900","183.9400","170.1800"
+"2016/02/02","182.7800","5764817.0000","192.4200","193.1200","180.2300"
+"2016/02/01","196.9400","5295685.0000","188.7600","199.5200","182.7500"
+"2016/01/29","191.2000","2848929.0000","189.9500","193.7400","188.0800"
+"2016/01/28","189.7000","4586328.0000","190.7900","191.2800","182.4100"
+"2016/01/27","188.0700","3577353.0000","192.3800","193.2600","185.7700"
+"2016/01/26","193.5600","4956918.0000","196.7000","197.8200","188.8800"
+"2016/01/25","196.3800","2695408.0000","200.0600","203.5700","195.8800"
+"2016/01/22","202.5500","3122066.0000","204.8010","205.5000","199.0300"
+"2016/01/21","199.9700","3164424.0000","201.5500","203.2300","195.0200"
+"2016/01/20","198.7000","5826110.0000","199.4000","201.2800","191.2500"
+"2016/01/19","204.7200","4031271.0000","208.7100","210.4700","200.7800"
+"2016/01/15","204.9900","5325773.0000","198.9700","205.0700","197.2500"
+"2016/01/14","206.1800","6490579.0000","202.2100","210.0000","193.3800"
+"2016/01/13","200.3100","4120993.0000","212.0100","212.6500","200.0000"
+"2016/01/12","209.9700","3079051.0000","211.6000","213.7395","205.3100"
+"2016/01/11","207.8500","4090843.0000","214.0100","214.4500","203.0000"
+"2016/01/08","211.0000","3572360.0000","217.8600","220.4400","210.7700"
+"2016/01/07","215.6500","3550704.0000","214.1900","218.4400","213.6700"
+"2016/01/06","219.0400","3774353.0000","220.0000","220.0500","215.9800"
+"2016/01/05","223.4300","3185525.0000","226.3600","226.8900","220.0000"
+"2016/01/04","223.4100","6823510.0000","230.7200","231.3800","219.0000"
+"2015/12/31","240.0100","2713817.0000","238.5100","243.4500","238.3700"
+"2015/12/30","238.0900","3694448.0000","236.6000","243.6340","235.6707"
+"2015/12/29","237.1900","2402438.0000","230.0600","237.7200","229.5470"
+"2015/12/28","228.9500","1900171.0000","231.4900","231.9800","225.5400"
+"2015/12/24","230.5700","710277.0000","230.5600","231.8800","228.2800"
+"2015/12/23","229.7000","1551031.0000","232.1800","233.4500","228.1300"
+"2015/12/22","229.9500","1958836.0000","234.9900","236.5500","229.6300"
+"2015/12/21","232.5600","1951581.0000","231.6900","235.8300","231.0800"
+"2015/12/18","230.4600","3008057.0000","232.8900","235.9000","229.2900"
+"2015/12/17","233.3900","3294460.0000","233.9400","237.7600","229.8149"
+"2015/12/16","234.5100","5100443.0000","222.1000","234.8800","220.7300"
+"2015/12/15","221.0900","2241686.0000","221.8200","222.2200","218.0000"
+"2015/12/14","218.5800","2827797.0000","217.5100","220.9200","214.8700"
+"2015/12/11","217.0200","3259114.0000","225.2400","225.7500","216.6400"
+"2015/12/10","227.0700","2070088.0000","224.7100","228.4900","223.6400"
+"2015/12/09","224.5200","3057036.0000","226.7000","227.5000","220.7200"
+"2015/12/08","226.7200","2682259.0000","227.5200","228.8000","224.2000"
+"2015/12/07","231.1300","3140864.0000","227.7000","235.6300","226.1500"
+"2015/12/04","230.3800","2572014.0000","232.4600","233.2700","227.6600"
+"2015/12/03","232.7100","2937233.0000","235.4800","237.4500","230.0000"
+"2015/12/02","231.9900","2980644.0000","237.0000","238.6000","231.2300"
+"2015/12/01","237.1900","3728562.0000","231.0600","238.0000","231.0500"
+"2015/11/30","230.2600","2591205.0000","231.7900","234.2800","229.0800"
+"2015/11/27","231.6100","1949353.0000","231.0600","232.2500","227.0100"
+"2015/11/25","229.6400","3986945.0000","221.3400","230.8250","220.3750"
+"2015/11/24","218.2500","2478389.0000","215.3700","221.0000","215.0000"
+"2015/11/23","217.7500","2525196.0000","217.3500","219.1800","214.6798"
+"2015/11/20","220.0100","4394199.0000","223.4900","225.0000","213.5800"
+"2015/11/19","221.8000","2499593.0000","220.5400","226.1900","220.3000"
+"2015/11/18","221.0700","2810200.0000","214.5000","221.3800","212.5200"
+"2015/11/17","214.0000","2148326.0000","215.2000","216.0000","211.4000"
+"2015/11/16","214.3100","2920362.0000","206.0900","214.9800","205.8000"
+"2015/11/13","207.1900","3423139.0000","212.9500","212.9900","206.5200"
+"2015/11/12","212.9400","2913511.0000","217.8500","219.0000","212.6600"
+"2015/11/11","219.0800","3337501.0000","217.7700","219.4800","213.6300"
+"2015/11/10","216.5000","4612408.0000","223.4800","223.7000","216.0800"
+"2015/11/09","225.3300","3846179.0000","232.9900","232.9900","224.3100"
+"2015/11/06","232.3600","2444264.0000","230.7000","233.3590","229.5000"
+"2015/11/05","231.7700","4494944.0000","230.5800","234.5843","229.1900"
+"2015/11/04","231.6300","12713530.0000","227.0000","232.7400","225.2000"
+"2015/11/03","208.3500","8065091.0000","213.8500","214.4400","207.7500"
+"2015/11/02","213.7900","3924514.0000","208.9200","215.8000","207.2200"
+"2015/10/30","206.9300","4432433.0000","210.4000","211.6300","203.8900"
+"2015/10/29","211.6300","1794743.0000","211.7500","213.7481","210.6400"
+"2015/10/28","212.9600","2726880.0000","211.3100","213.4500","208.3000"
+"2015/10/27","210.3500","3512832.0000","214.8400","217.1000","207.5100"
+"2015/10/26","215.2600","3388227.0000","211.3800","215.8800","210.0000"
+"2015/10/23","209.0900","4226411.0000","215.0000","215.3500","207.6900"
+"2015/10/22","211.7200","2818801.0000","211.5600","215.7500","209.4000"
+"2015/10/21","210.0900","4177956.0000","211.9900","214.8100","208.8000"
+"2015/10/20","213.0300","14877020.0000","227.7200","228.6000","202.0000"
+"2015/10/19","228.1000","2506836.0000","226.5000","231.1500","224.9400"
+"2015/10/16","227.0100","4327574.0000","223.0400","230.4805","222.8700"
+"2015/10/15","221.3100","2835920.0000","216.4300","221.7300","213.7000"

agents/tools-class.py

@@ -0,0 +1,115 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+import requests
+from typing import Any, Dict, List, Optional
+from pydantic import BaseModel, Field
+
+class EXASearchTool(BaseModel):
+    """Wrapper for EXA Search API."""
+    search_url: str = "https://api.exa.ai/search"
+    headers: Dict = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    max_results: Optional[int] = None
+
+    def run(self, query: str) -> str:
+        """Run query through EXA and return concatenated results.
+        
+        Args:
+            query (str): The search query to use
+            
+        Returns:
+            str: The concatenated search results
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return self._parse_results(results['results'])
+        return ""
+
+    def results(self, query: str, max_results: Optional[int] = None) -> List[Dict[str, Any]]:
+        """Run query through EXA and return metadata.
+        
+        Args:
+            query (str): The search query to use
+            max_results (Optional[int]): Maximum number of results to return
+            
+        Returns:
+            List[Dict[str, Any]]: List of result dictionaries
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return results['results'][:max_results] if max_results else results['results']
+        return []
+
+    def _parse_results(self, results: List[Dict[str, Any]]) -> str:
+        """Parse results into a readable string format.
+        
+        Args:
+            results (List[Dict[str, Any]]): List of result dictionaries
+            
+        Returns:
+            str: Formatted string of results
+        """
+        strings = []
+        for result in results:
+            try:
+                strings.append('\n'.join([
+                    f"Title: {result['title']}",
+                    f"Score: {result['score']}",
+                    f"Url: {result['url']}",
+                    f"ID: {result['id']}",
+                    "---"
+                ]))
+            except KeyError:
+                continue
+
+        content = '\n'.join(strings)
+        return f"\nSearch results: {content}\n"
+
+# Create an agent with the tool
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information about 'AI Agents Framework'",
+    backstory="I am an AI assistant that can search GitHub.",
+    tools=[EXASearchTool],
+    self_reflect=False
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI Agents Framework'",
+    expected_output="Information about AI Agents Framework",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/video-agent.py

@@ -0,0 +1,36 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Video Analysis Agent
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Task with Video File
+task1 = Task(
+    name="analyze_video",
+    description="""Watch this video and provide:
+    1. A summary of the main events
+    2. Key objects and people visible
+    3. Any text or important information shown
+    4. The overall context and setting""",
+    expected_output="Comprehensive analysis of the video content",
+    agent=video_agent,
+    images=["video.mp4"]  
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[task1],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/websearch-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Web Search Agent", tools=[duckduckgo])
+agent.start("Search about AI 2024")

agents/wikipedia-agent.py

@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)

docs/agents/agents.mdx

@@ -0,0 +1,85 @@
+---
+title: "AI Agents"
+description: "Overview of all available PraisonAI agents and their capabilities"
+icon: "robot"
+---
+
+PraisonAI provides a diverse set of specialized agents for various tasks. Each agent is designed with specific capabilities and tools to handle different types of tasks effectively.
+
+## Data & Analysis
+
+<CardGroup cols={2}>
+  <Card title="Data Analyst" icon="chart-line" href="/agents/data-analyst">
+    Analyze data from various sources, create visualizations, and generate insights.
+  </Card>
+  <Card title="Finance" icon="money-bill-trend-up" href="/agents/finance">
+    Track stocks, analyze financial data, and provide investment recommendations.
+  </Card>
+  <Card title="Research" icon="magnifying-glass-chart" href="/agents/research">
+    Conduct comprehensive research and analysis across various topics.
+  </Card>
+  <Card title="Wikipedia" icon="book" href="/agents/wikipedia">
+    Search and extract information from Wikipedia articles.
+  </Card>
+</CardGroup>
+
+## Media & Content
+
+<CardGroup cols={2}>
+  <Card title="Image Analysis" icon="image" href="/agents/image">
+    Analyze and understand visual content from images.
+  </Card>
+  <Card title="Image to Text" icon="text" href="/agents/image-to-text">
+    Convert images to textual descriptions and extract text content.
+  </Card>
+  <Card title="Video" icon="video" href="/agents/video">
+    Analyze video content and extract meaningful information.
+  </Card>
+  <Card title="Markdown" icon="markdown" href="/agents/markdown">
+    Generate and format content in Markdown syntax.
+  </Card>
+</CardGroup>
+
+## Search & Recommendations
+
+<CardGroup cols={2}>
+  <Card title="Web Search" icon="globe" href="/agents/websearch">
+    Perform intelligent web searches and gather information.
+  </Card>
+  <Card title="Recommendation" icon="thumbs-up" href="/agents/recommendation">
+    Generate personalized recommendations based on preferences.
+  </Card>
+  <Card title="Shopping" icon="shop" href="/agents/shopping">
+    Compare prices and find the best deals across stores.
+  </Card>
+  <Card title="Planning" icon="calendar" href="/agents/planning">
+    Create travel plans and detailed itineraries.
+  </Card>
+</CardGroup>
+
+## Development
+
+<CardGroup cols={2}>
+  <Card title="Programming" icon="code" href="/agents/programming">
+    Write, analyze, and debug code across multiple languages.
+  </Card>
+  <Card title="Single Agent" icon="circle-1" href="/agents/single">
+    Simple, focused agent for basic tasks without external tools.
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+Each agent can be easily initialized and customized for your specific needs. Here's a basic example:
+
+```python
+from praisonaiagents import Agent
+
+# Create an agent with specific instructions
+agent = Agent(instructions="Your task-specific instructions")
+
+# Start the agent with a task
+response = agent.start("Your task description")
+```
+
+For more detailed information about each agent, click on the respective cards above.

docs/agents/data-analyst.mdx

@@ -0,0 +1,120 @@
+---
+title: "Data Analyst Agent"
+sidebarTitle: "Data Analyst"
+description: "Learn how to create AI agents for data analysis and insights generation."
+icon: "chart-line"
+---
+
+```mermaid
+flowchart LR
+    In[Data Source] --> Reader[Data Reader]
+    Reader --> Analyzer[Data Analyzer]
+    Analyzer --> Generator[Insights Generator]
+    Generator --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Reader fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `data_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import (
+            read_csv, read_excel, write_csv, write_excel,
+            filter_data, get_summary, group_by, pivot_table
+        )
+
+        agent = Agent(
+            instructions="You are a Data Analyst Agent",
+            tools=[
+                read_csv, read_excel, write_csv, write_excel,
+                filter_data, get_summary, group_by, pivot_table
+            ]
+        )
+
+        # Start analysis with a specific task
+        agent.start(f"""
+            Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+            Analyse the data and give me the insights
+            read_csv to read the file
+        """)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Data Analysis Workflow
+
+The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here's how it works:
+
+1. **Data Reading**: The agent can read data from various sources:
+   - CSV files using `read_csv`
+   - Excel files using `read_excel`
+
+2. **Data Analysis**: Multiple analysis tools are available:
+   - `filter_data`: Filter datasets based on conditions
+   - `get_summary`: Generate statistical summaries
+   - `group_by`: Group data by specific columns
+   - `pivot_table`: Create pivot tables for analysis
+
+3. **Data Export**: Results can be exported to:
+   - CSV format using `write_csv`
+   - Excel format using `write_excel`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Data Sources" icon="table">
+    Support for both CSV and Excel file formats.
+  </Card>
+  <Card title="Analysis Tools" icon="calculator">
+    Comprehensive suite of analysis tools including filtering, summarization, and pivoting.
+  </Card>
+  <Card title="Data Export" icon="file-export">
+    Export capabilities to various formats.
+  </Card>
+  <Card title="Automated Insights" icon="lightbulb">
+    Automatic generation of data insights and patterns.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing stock data
+agent.start("""
+    1. Read 'stock_data.csv'
+    2. Filter data for the last 30 days
+    3. Calculate daily returns
+    4. Generate summary statistics
+    5. Export results to 'analysis_results.xlsx'
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out other specialized agents like the [Finance Agent](/agents/finance) for specific use cases

docs/agents/finance.mdx

@@ -0,0 +1,97 @@
+---
+title: "Finance Agent"
+sidebarTitle: "Finance"
+description: "Learn how to create AI agents for financial analysis and investment recommendations."
+icon: "money-bill-trend-up"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Price[Stock Price]
+    Price --> Info[Stock Info]
+    Info --> History[Historical Data]
+    History --> Out[Investment Advice]
+    
+    style In fill:#8B0000,color:#fff
+    style Price fill:#2E8B57,color:#fff
+    style Info fill:#2E8B57,color:#fff
+    style History fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Finance Agent can analyze stock market data and provide investment recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `finance_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+        agent = Agent(
+            instructions="You are a Research Agent",
+            tools=[get_stock_price, get_stock_info, get_historical_data]
+        )
+        agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Financial Analysis
+
+The Finance Agent specializes in stock market analysis and investment recommendations using three key components:
+
+1. **Real-time Price Data**: Uses `get_stock_price` to fetch current market prices
+2. **Company Information**: Retrieves company details using `get_stock_info`
+3. **Historical Analysis**: Analyzes trends with `get_historical_data`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Real-time Data" icon="clock">
+    Access to current stock market prices and information.
+  </Card>
+  <Card title="Company Analysis" icon="building">
+    Detailed company information and fundamentals.
+  </Card>
+  <Card title="Historical Trends" icon="chart-line">
+    Analysis of historical price movements and patterns.
+  </Card>
+  <Card title="Investment Advice" icon="money-bill">
+    AI-powered investment recommendations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing specific stocks
+agent.start("""
+    1. Get current prices for AAPL and GOOGL
+    2. Retrieve company information
+    3. Analyze 6-month historical data
+    4. Provide investment recommendations
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex financial analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Data Analyst Agent](/agents/data-analyst) for detailed data analysis capabilities

docs/agents/image-to-text.mdx

@@ -0,0 +1,140 @@
+---
+title: "Image to Text Agent"
+sidebarTitle: "Image to Text"
+description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
+icon: "text"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> OCR[Text Extractor]
+    OCR --> Analyzer[Content Analyzer]
+    Analyzer --> Generator[Description Generator]
+    Generator --> Out[Text Output]
+    
+    style In fill:#8B0000,color:#fff
+    style OCR fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image-to-Text Agent can extract text from images and generate comprehensive descriptions.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `image_to_text.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image-to-Text Agent
+        image_text_agent = Agent(
+            name="ImageTextConverter",
+            role="Image Text Extraction Specialist",
+            goal="Convert image content to textual descriptions and extract text",
+            backstory="""You are an expert in OCR and image understanding.
+            You excel at extracting text from images and generating detailed descriptions.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create text extraction task
+        extraction_task = Task(
+            name="extract_text",
+            description="Extract all text from this image and describe its layout.",
+            expected_output="Extracted text and layout description",
+            agent=image_text_agent,
+            images=["document.jpg"]
+        )
+
+        # Create description task
+        description_task = Task(
+            name="generate_description",
+            description="Generate a detailed description of the image content.",
+            expected_output="Comprehensive description of visual elements",
+            agent=image_text_agent,
+            images=["scene.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_text_agent],
+            tasks=[extraction_task, description_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image-to-Text Conversion
+
+The Image-to-Text Agent combines multiple capabilities to convert visual content into textual form:
+
+1. **OCR Processing**: Extracts text from images using optical character recognition
+2. **Layout Analysis**: Understands the spatial arrangement of text and visual elements
+3. **Content Description**: Generates natural language descriptions of image content
+4. **Text Formatting**: Preserves text formatting and structure where possible
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Text Extraction" icon="text">
+    Advanced OCR capabilities for text extraction.
+  </Card>
+  <Card title="Layout Understanding" icon="table-layout">
+    Analysis of text and content layout.
+  </Card>
+  <Card title="Content Description" icon="pen-to-square">
+    Detailed descriptions of visual content.
+  </Card>
+  <Card title="Format Preservation" icon="paragraph">
+    Maintains text formatting and structure.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Processing a document image
+document_task = Task(
+    name="process_document",
+    description="Extract text and analyze document layout",
+    expected_output="Extracted text with layout information",
+    agent=image_text_agent,
+    images=["business_document.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_text_agent],
+    tasks=[document_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document processing
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving text extraction accuracy
+- Check out the [Image Agent](/agents/image) for pure image analysis capabilities

docs/agents/image.mdx

@@ -0,0 +1,139 @@
+---
+title: "Image Analysis Agent"
+sidebarTitle: "Image Analysis"
+description: "Learn how to create AI agents for image analysis and visual content understanding."
+icon: "image"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> Analyzer[Image Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Generator[Description Generator]
+    Generator --> Out[Analysis Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `image_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image Analysis Agent
+        image_agent = Agent(
+            name="ImageAnalyst",
+            role="Image Analysis Specialist",
+            goal="Analyze images and videos to extract meaningful information",
+            backstory="""You are an expert in computer vision and image analysis.
+            You excel at describing images, detecting objects, and understanding visual content.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create tasks for different types of analysis
+        task1 = Task(
+            name="analyze_landmark",
+            description="Describe this famous landmark and its architectural features.",
+            expected_output="Detailed description of the landmark's architecture and significance",
+            agent=image_agent,
+            images=["https://example.com/landmark.jpg"]
+        )
+
+        task2 = Task(
+            name="analyze_local_image",
+            description="What objects can you see in this image? Describe their arrangement.",
+            expected_output="Detailed description of objects and their spatial relationships",
+            agent=image_agent,
+            images=["local_image.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_agent],
+            tasks=[task1, task2],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image Analysis
+
+The Image Analysis Agent is designed to process and understand visual content through several key capabilities:
+
+1. **Image Input**: Supports both URL and local file inputs
+2. **Object Detection**: Identifies and locates objects within images
+3. **Architectural Analysis**: Specialized in analyzing architectural features
+4. **Spatial Understanding**: Describes spatial relationships between objects
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Input Sources" icon="images">
+    Support for both URL and local image files.
+  </Card>
+  <Card title="Object Detection" icon="magnifying-glass">
+    Advanced object detection and recognition.
+  </Card>
+  <Card title="Architectural Analysis" icon="building">
+    Specialized analysis of architectural features.
+  </Card>
+  <Card title="Detailed Descriptions" icon="pen">
+    Generation of comprehensive visual descriptions.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing a landmark image
+landmark_task = Task(
+    name="analyze_famous_landmark",
+    description="Analyze this historical landmark",
+    expected_output="Architectural details and historical significance",
+    agent=image_agent,
+    images=["https://example.com/landmark.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[landmark_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex image analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Video Agent](/agents/video) for video content analysis

docs/agents/markdown.mdx

@@ -0,0 +1,120 @@
+---
+title: "Markdown Agent"
+sidebarTitle: "Markdown"
+description: "Learn how to create AI agents for generating and formatting content in Markdown."
+icon: "markdown"
+---
+
+```mermaid
+flowchart LR
+    In[Content Request] --> Generator[Content Generator]
+    Generator --> Formatter[Markdown Formatter]
+    Formatter --> Validator[Format Validator]
+    Validator --> Out[Markdown Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Formatter fill:#2E8B57,color:#fff
+    style Validator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Markdown Agent can generate and format content in Markdown syntax.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `markdown_generator.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        # Create Markdown Agent
+        markdown_agent = Agent(
+            name="MarkdownWriter",
+            role="Markdown Content Specialist",
+            goal="Generate well-formatted content in Markdown syntax",
+            instructions="You are a Markdown Agent, output in markdown format",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Generate content
+        response = markdown_agent.start(
+            "Write a blog post about artificial intelligence"
+        )
+        
+        # Save to file
+        with open('blog_post.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Markdown Generation
+
+The Markdown Agent specializes in creating properly formatted Markdown content:
+
+1. **Content Generation**: Creates original content based on prompts
+2. **Markdown Formatting**: Applies proper Markdown syntax
+3. **Structure Validation**: Ensures correct formatting
+4. **Document Organization**: Creates well-structured documents
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Content Creation" icon="pen-to-square">
+    Generates original, well-structured content.
+  </Card>
+  <Card title="Markdown Syntax" icon="code">
+    Proper implementation of Markdown formatting.
+  </Card>
+  <Card title="Document Structure" icon="sitemap">
+    Organized document hierarchy and sections.
+  </Card>
+  <Card title="Format Validation" icon="check">
+    Ensures correct Markdown syntax usage.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Generate a technical documentation
+agent = Agent(
+    instructions="You are a Markdown Agent, output in markdown format"
+)
+
+# Generate API documentation
+response = agent.start("""
+    Create technical documentation for a REST API with:
+    - Introduction
+    - Authentication
+    - Endpoints
+    - Examples
+""")
+
+# Save documentation
+with open('api_docs.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document generation
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving content quality
+- Check out the [Documentation Agent](/agents/documentation) for specialized documentation generation

docs/agents/planning.mdx

@@ -0,0 +1,109 @@
+---
+title: "Planning Agent"
+sidebarTitle: "Planning"
+description: "Learn how to create AI agents for trip planning and itinerary generation."
+icon: "calendar"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Search[Web Search]
+    Search --> Analyzer[Options Analyzer]
+    Analyzer --> Generator[Plan Generator]
+    Generator --> Out[Itinerary]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Planning Agent can search for travel options and create detailed itineraries.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `travel_planner.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Planning Agent
+        planning_agent = Agent(
+            name="TravelPlanner",
+            role="Travel Planning Specialist",
+            goal="Create comprehensive travel plans and itineraries",
+            instructions="You are a Planning Agent",
+            tools=[duckduckgo]
+        )
+
+        # Generate travel plan
+        response = planning_agent.start(
+            "I want to go to London next week, find me a good hotel and flight"
+        )
+        
+        # Save plan
+        with open('travel_plan.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Travel Planning
+
+The Planning Agent combines multiple capabilities to create comprehensive travel plans:
+
+1. **Web Search**: Uses DuckDuckGo to find current travel options
+2. **Options Analysis**: Evaluates and compares different choices
+3. **Plan Generation**: Creates detailed itineraries
+4. **Recommendations**: Provides personalized suggestions
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Flight Search" icon="plane">
+    Finds and compares flight options.
+  </Card>
+  <Card title="Hotel Booking" icon="hotel">
+    Recommends suitable accommodations.
+  </Card>
+  <Card title="Itinerary Creation" icon="calendar-days">
+    Generates detailed travel schedules.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Uses current pricing and availability.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a weekend trip plan
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex travel planning
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendations
+- Check out the [Research Agent](/agents/research) for detailed destination research

docs/agents/programming.mdx

@@ -0,0 +1,125 @@
+---
+title: "Programming Agent"
+sidebarTitle: "Programming"
+description: "Learn how to create AI agents for code development, analysis, and execution."
+icon: "code"
+---
+
+```mermaid
+flowchart LR
+    In[Code Request] --> Analyzer[Code Analyzer]
+    Analyzer --> Generator[Code Generator]
+    Generator --> Executor[Code Executor]
+    Executor --> Debugger[Code Debugger]
+    Debugger --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Executor fill:#2E8B57,color:#fff
+    style Debugger fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Programming Agent can analyze, generate, execute, and debug code.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `code_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+        from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+        from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+        agent = Agent(
+            instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+            tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+        )
+        agent.start(
+            "Write a python script using yfinance to find the stock price of Tesla"
+            "First check if required packages are installed"
+            "Run it using execute_code"
+            "execute_command if you want to run any terminal command"
+            "search internet using duckduckgo if you want to know update python package information"
+            "Analyse the output using analyze_code and fix error if required"
+            "if no package is installed, install it"
+            "then run the code"
+        )
+        ```
+    </Step>
+</Steps>
+
+## Understanding Code Development
+
+The Programming Agent combines multiple tools for comprehensive code development:
+
+1. **Code Tools**:
+   - `execute_code`: Run Python code
+   - `analyze_code`: Analyze code structure
+   - `format_code`: Format code to standards
+   - `lint_code`: Check code quality
+   - `disassemble_code`: View bytecode
+
+2. **Shell Tools**:
+   - `execute_command`: Run terminal commands
+   - `list_processes`: View running processes
+   - `kill_process`: Terminate processes
+   - `get_system_info`: System information
+
+3. **Web Tools**:
+   - `duckduckgo`: Search for programming resources
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Code Execution" icon="play">
+    Run and test code directly.
+  </Card>
+  <Card title="Code Analysis" icon="magnifying-glass-code">
+    Analyze and improve code quality.
+  </Card>
+  <Card title="System Integration" icon="terminal">
+    Execute system commands and manage processes.
+  </Card>
+  <Card title="Package Management" icon="box">
+    Handle dependencies and installations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create and run a data analysis script
+response = agent.start("""
+    1. Create a script that:
+       - Uses pandas for data analysis
+       - Reads a CSV file
+       - Performs basic statistics
+       - Creates a visualization
+    2. Check and install required packages
+    3. Execute and analyze the results
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex development workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving code quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for specialized data analysis

docs/agents/recommendation.mdx

@@ -0,0 +1,109 @@
+---
+title: "Recommendation Agent"
+sidebarTitle: "Recommendation"
+description: "Learn how to create AI agents for personalized recommendations across various domains."
+icon: "thumbs-up"
+---
+
+```mermaid
+flowchart LR
+    In[User Preferences] --> Search[Content Search]
+    Search --> Analyzer[Preference Analyzer]
+    Analyzer --> Generator[Recommendation Generator]
+    Generator --> Out[Recommendations]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `recommendation_system.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Recommendation Agent
+        recommendation_agent = Agent(
+            name="RecommendationEngine",
+            role="Recommendation Specialist",
+            goal="Generate personalized recommendations based on preferences",
+            instructions="You are a Recommendation Agent",
+            tools=[duckduckgo]
+        )
+
+        # Get recommendations
+        response = recommendation_agent.start(
+            "Recommend me a good movie to watch in 2025"
+        )
+        
+        # Save recommendations
+        with open('recommendations.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Recommendation System
+
+The Recommendation Agent uses multiple approaches to generate personalized suggestions:
+
+1. **Content Search**: Uses DuckDuckGo to find current options
+2. **Preference Analysis**: Understands user preferences
+3. **Recommendation Generation**: Creates personalized suggestions
+4. **Content Filtering**: Filters based on relevance and quality
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Personalization" icon="user">
+    Tailored recommendations based on preferences.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Up-to-date content and information.
+  </Card>
+  <Card title="Multi-domain" icon="layer-group">
+    Recommendations across various categories.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Quality-based content selection.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Get personalized recommendations
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex recommendation systems
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Research Agent](/agents/research) for detailed content research

docs/agents/research.mdx

@@ -0,0 +1,108 @@
+---
+title: "Research Agent"
+sidebarTitle: "Research"
+description: "Learn how to create AI agents for conducting comprehensive research and analysis."
+icon: "magnifying-glass-chart"
+---
+
+```mermaid
+flowchart LR
+    In[Research Query] --> Search[Web Search]
+    Search --> Analyzer[Content Analyzer]
+    Analyzer --> Synthesizer[Information Synthesizer]
+    Synthesizer --> Out[Research Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Research Agent can gather, analyze, and synthesize information from various sources.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `research_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Research Agent
+        research_agent = Agent(
+            name="ResearchAssistant",
+            role="Research Specialist",
+            goal="Conduct comprehensive research and analysis",
+            instructions="You are a Research Agent",
+            tools=[duckduckgo]
+        )
+
+        # Conduct research
+        response = research_agent.start(
+            "Research about AI developments in 2024"
+        )
+        
+        # Save research findings
+        with open('research_report.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Research Process
+
+The Research Agent employs a systematic approach to information gathering and analysis:
+
+1. **Web Search**: Uses DuckDuckGo to find relevant sources
+2. **Content Analysis**: Evaluates source credibility and relevance
+3. **Information Synthesis**: Combines findings into coherent insights
+4. **Report Generation**: Creates structured research reports
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Web Research" icon="globe">
+    Comprehensive online information gathering.
+  </Card>
+  <Card title="Content Analysis" icon="microscope">
+    In-depth evaluation of sources.
+  </Card>
+  <Card title="Information Synthesis" icon="layer-group">
+    Integration of multiple sources.
+  </Card>
+  <Card title="Report Generation" icon="file-lines">
+    Structured research documentation.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex research projects
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for data-driven research

docs/agents/shopping.mdx

@@ -0,0 +1,109 @@
+---
+title: "Shopping Agent"
+sidebarTitle: "Shopping"
+description: "Learn how to create AI agents for price comparison and shopping assistance."
+icon: "shop"
+---
+
+```mermaid
+flowchart LR
+    In[Product Query] --> Search[Store Search]
+    Search --> Analyzer[Price Analyzer]
+    Analyzer --> Comparator[Price Comparator]
+    Comparator --> Out[Price Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Comparator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Shopping Agent can search for products, compare prices, and provide shopping recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `shopping_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Shopping Agent
+        shopping_agent = Agent(
+            name="ShoppingAssistant",
+            role="Shopping Specialist",
+            goal="Find the best prices and deals across stores",
+            instructions="You are a Shopping Agent",
+            tools=[duckduckgo]
+        )
+
+        # Search for product prices
+        response = shopping_agent.start(
+            "I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table"
+        )
+        
+        # Save price comparison
+        with open('price_comparison.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Shopping Assistant
+
+The Shopping Agent combines multiple capabilities for effective price comparison:
+
+1. **Store Search**: Uses DuckDuckGo to find retailers
+2. **Price Analysis**: Gathers pricing information
+3. **Price Comparison**: Compares prices across stores
+4. **Deal Finding**: Identifies special offers and discounts
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Price Comparison" icon="scale-balanced">
+    Compare prices across multiple stores.
+  </Card>
+  <Card title="Deal Detection" icon="tag">
+    Find special offers and discounts.
+  </Card>
+  <Card title="Store Analysis" icon="store">
+    Evaluate retailer reliability.
+  </Card>
+  <Card title="Price Tracking" icon="chart-line">
+    Monitor price changes over time.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Compare product prices
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex shopping research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving price accuracy
+- Check out the [Research Agent](/agents/research) for detailed product research

docs/agents/single.mdx

@@ -0,0 +1,102 @@
+---
+title: "Single Agent"
+sidebarTitle: "Single"
+description: "Learn how to create a basic single-purpose AI agent for simple tasks."
+icon: "circle-1"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> Agent[Single Agent]
+    Agent --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how to create and use a simple single-purpose agent.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `simple_agent.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+        agent.start("Write a blog post about AI")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Single Agent
+
+The Single Agent is the simplest form of PraisonAI agent:
+
+1. **Simple Setup**: Minimal configuration required
+2. **Single Purpose**: Focused on one specific task
+3. **Direct Execution**: Straightforward input-output flow
+4. **No Dependencies**: No external tools required
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Simple Setup" icon="square-1">
+    Minimal configuration needed.
+  </Card>
+  <Card title="Focused Task" icon="bullseye">
+    Single-purpose execution.
+  </Card>
+  <Card title="Quick Start" icon="bolt">
+    Rapid implementation.
+  </Card>
+  <Card title="No Tools" icon="toolbox">
+    No external dependencies.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a content generation agent
+from praisonaiagents import Agent
+
+# Create content agent
+agent = Agent(
+    instructions="You are a Content Generation Agent, create engaging content"
+)
+
+# Generate content
+response = agent.start("""
+    Write a short story about:
+    - A future world
+    - With advanced AI
+    - And human collaboration
+""")
+
+# Save the story
+with open('ai_story.txt', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for more complex workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving output quality
+- Check out other specialized agents like the [Markdown Agent](/agents/markdown) for specific use cases

docs/agents/video.mdx

@@ -0,0 +1,149 @@
+---
+title: "Video Agent"
+sidebarTitle: "Video"
+description: "Learn how to create AI agents for video analysis and content understanding."
+icon: "video"
+---
+
+```mermaid
+flowchart LR
+    In[Video Input] --> Analyzer[Content Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Transcriber[Text Transcriber]
+    Transcriber --> Out[Analysis Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Transcriber fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Video Agent can analyze video content, detect objects, and extract meaningful information.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `video_analyzer.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Video Analysis Agent
+        video_agent = Agent(
+            name="VideoAnalyst",
+            role="Video Analysis Specialist",
+            goal="Analyze videos to extract meaningful information",
+            backstory="""You are an expert in computer vision and video analysis.
+            You excel at describing content, detecting objects, and understanding context.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create video analysis task
+        analysis_task = Task(
+            name="analyze_video",
+            description="""Analyze this video and provide:
+                1. Summary of main events
+                2. Key objects and people
+                3. Text and important information
+                4. Context and setting""",
+            expected_output="Comprehensive video analysis",
+            agent=video_agent,
+            images=["video.mp4"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[video_agent],
+            tasks=[analysis_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Video Analysis
+
+The Video Agent combines multiple capabilities for comprehensive video understanding:
+
+1. **Content Analysis**: Analyzes video scenes and events
+2. **Object Detection**: Identifies objects and people
+3. **Text Extraction**: Captures text shown in videos
+4. **Context Understanding**: Interprets settings and situations
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Scene Analysis" icon="film">
+    Detailed analysis of video scenes.
+  </Card>
+  <Card title="Object Detection" icon="cube">
+    Identification of objects and people.
+  </Card>
+  <Card title="Text Extraction" icon="closed-captioning">
+    Capture of text and captions.
+  </Card>
+  <Card title="Context Analysis" icon="sitemap">
+    Understanding of video context.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyze a presentation video
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis Specialist",
+    goal="Extract information from presentation videos",
+    llm="gpt-4o-mini"
+)
+
+# Create presentation analysis task
+presentation_task = Task(
+    name="analyze_presentation",
+    description="""Analyze this presentation video:
+        1. Extract key points
+        2. Capture slide content
+        3. Note speaker's main arguments
+        4. Summarize Q&A session""",
+    expected_output="Detailed presentation summary",
+    agent=video_agent,
+    images=["presentation.mp4"]
+)
+
+# Run analysis
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[presentation_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex video analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Image Agent](/agents/image) for still image analysis

docs/agents/websearch.mdx

@@ -0,0 +1,124 @@
+---
+title: "Web Search Agent"
+sidebarTitle: "Web Search"
+description: "Learn how to create AI agents for intelligent web searching and information gathering."
+icon: "globe"
+---
+
+```mermaid
+flowchart LR
+    In[Search Query] --> Search[Web Search]
+    Search --> Filter[Content Filter]
+    Filter --> Summarizer[Content Summarizer]
+    Summarizer --> Out[Search Results]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Filter fill:#2E8B57,color:#fff
+    style Summarizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Web Search Agent can perform intelligent searches and process web content.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `web_search.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Web Search Agent
+        search_agent = Agent(
+            name="WebSearcher",
+            role="Web Search Specialist",
+            goal="Perform intelligent web searches and gather information",
+            instructions="You are a Web Search Agent",
+            tools=[duckduckgo]
+        )
+
+        # Perform search
+        response = search_agent.start(
+            "Search about AI developments in 2024"
+        )
+        
+        # Save search results
+        with open('search_results.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Web Search
+
+The Web Search Agent employs sophisticated search strategies:
+
+1. **Query Processing**: Optimizes search queries
+2. **Content Filtering**: Filters relevant results
+3. **Information Extraction**: Extracts key information
+4. **Result Summarization**: Summarizes findings
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Smart Search" icon="magnifying-glass">
+    Intelligent query processing.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Relevance-based filtering.
+  </Card>
+  <Card title="Information Extraction" icon="scissors">
+    Key information extraction.
+  </Card>
+  <Card title="Result Summary" icon="list">
+    Concise result summaries.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Perform topic research
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(
+    instructions="You are a Web Search Agent",
+    tools=[duckduckgo]
+)
+
+# Research specific topic
+response = agent.start("""
+    Search for information about quantum computing:
+    - Recent breakthroughs
+    - Leading companies
+    - Current applications
+    - Future predictions
+""")
+
+# Save research results
+with open('quantum_computing_research.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex search workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving search relevance
+- Check out the [Research Agent](/agents/research) for comprehensive research capabilities

docs/agents/wikipedia.mdx

@@ -0,0 +1,139 @@
+---
+title: "Wikipedia Agent"
+sidebarTitle: "Wikipedia"
+description: "Learn how to create AI agents for searching and extracting information from Wikipedia."
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    In[Query] --> Search[Wiki Search]
+    Search --> Page[Page Retrieval]
+    Page --> Summary[Content Summary]
+    Summary --> Out[Knowledge Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Page fill:#2E8B57,color:#fff
+    style Summary fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Wikipedia Agent can search, retrieve, and summarize Wikipedia content.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `wikipedia_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        from praisonaiagents.tools import (
+            wiki_search,
+            wiki_summary,
+            wiki_page,
+            wiki_random,
+            wiki_language
+        )
+
+        # Create Wikipedia Agent
+        wiki_agent = Agent(
+            name="WikipediaAssistant",
+            role="Wikipedia Research Specialist",
+            goal="Extract and summarize Wikipedia content",
+            instructions="You are a Wikipedia Agent",
+            tools=[
+                wiki_search,
+                wiki_summary,
+                wiki_page,
+                wiki_random,
+                wiki_language
+            ],
+            self_reflect=True,
+            min_reflect=3,
+            max_reflect=5
+        )
+
+        # Research a topic
+        response = wiki_agent.start("""
+            What is the history of AI?
+            First search the history of AI
+            Read the page of the history of AI
+            Get the summary of the page
+        """)
+        
+        # Save research results
+        with open('ai_history.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Wikipedia Research
+
+The Wikipedia Agent provides comprehensive Wikipedia access through multiple tools:
+
+1. **Wiki Search**: `wiki_search` for finding relevant articles
+2. **Page Access**: `wiki_page` for retrieving full articles
+3. **Summarization**: `wiki_summary` for concise overviews
+4. **Random Articles**: `wiki_random` for discovering content
+5. **Language Support**: `wiki_language` for multilingual access
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Article Search" icon="magnifying-glass">
+    Find relevant Wikipedia articles.
+  </Card>
+  <Card title="Content Retrieval" icon="book-open">
+    Access complete article content.
+  </Card>
+  <Card title="Summarization" icon="compress">
+    Generate concise article summaries.
+  </Card>
+  <Card title="Multilingual" icon="language">
+    Access content in multiple languages.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Research a scientific topic
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex Wikipedia research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Research Agent](/agents/research) for broader research capabilities

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.37 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.48 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/code/installation.mdx

@@ -1,69 +0,0 @@
----
-title: Installation
-description: Set up PraisonAI in your development environment
-icon: download
----
-
-<Note>
-  **Requirements**
-  
-  - Python 3.10 or higher
-  - pip package manager
-  - Virtual environment (recommended)
-</Note>
-
-# Installing PraisonAI
-
-Follow these steps to set up PraisonAI in your development environment.
-
-<Steps>
-  <Step title="Create Virtual Environment (Optional)">
-    First, create and activate a virtual environment:
-    <CodeGroup>
-    ```bash Mac/Linux
-    python -m venv praisonai-env
-    source praisonai-env/bin/activate
-    ```
-
-    ```bash Windows
-    python -m venv praisonai-env
-    .\praisonai-env\Scripts\activate
-    ```
-    </CodeGroup>
-  </Step>
-
-  <Step title="Install PraisonAI">
-    Install the core package and tools:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-</Steps>
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Quick Start Guide"
-    icon="bolt"
-    href="/code/quickstart"
-  >
-    Build your first AI agent
-  </Card>
-  <Card
-    title="API Reference"
-    icon="code"
-    href="/api/praisonaiagents/index"
-  >
-    Explore the API documentation
-  </Card>
-</CardGroup> 

docs/code/introduction.mdx

@@ -1,188 +0,0 @@
----
-title: "Introduction"
-description: "Welcome to PraisonAI - The Next Generation AI Agent Framework"
-icon: "book-open"
----
-
-<Note>
-  PraisonAI is a powerful framework for building and deploying AI agents that can understand, reason, and execute complex tasks autonomously.
-</Note>
-
-# What is PraisonAI?
-
-```mermaid
-graph LR
-    %% Define the main flow
-    Start([▶ Start]) --> Agent1
-    Agent1 --> Process[⚙ Process]
-    Process --> Agent2
-    Agent2 --> Output([✓ Output])
-    Process -.-> Agent1
-    
-    %% Styling for main elements
-    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
-    
-    %% Define subgraphs for agents and their tasks
-    subgraph Agent1[" "]
-        Task1[📋 Task]
-        AgentIcon1[🤖 AI Agent]
-        Tools1[🔧 Tools]
-        
-        Task1 --- AgentIcon1
-        AgentIcon1 --- Tools1
-        
-        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph Agent2[" "]
-        Task2[📋 Task]
-        AgentIcon2[🤖 AI Agent]
-        Tools2[🔧 Tools]
-        
-        Task2 --- AgentIcon2
-        AgentIcon2 --- Tools2
-        
-        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
-    end
-    
-    %% Style for agent containers
-    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-
-## Core Components
-
-<CardGroup cols={2}>
-  <Card title="Agents" icon="user-robot">
-    Autonomous AI entities that can understand context and execute tasks
-  </Card>
-  <Card title="Tasks" icon="list-check">
-    Units of work that agents can execute independently or collaboratively
-  </Card>
-  <Card title="Process" icon="diagram-project">
-    Workflow management for coordinating agent activities
-  </Card>
-  <Card title="Tools" icon="toolbox">
-    Extensible capabilities that agents can leverage
-  </Card>
-</CardGroup>
-
-## Key Features
-
-<Steps>
-  <Step title="Autonomous Agents">
-    Create intelligent agents that can:
-    - Understand natural language instructions
-    - Make decisions based on context
-    - Execute complex tasks autonomously
-  </Step>
-  <Step title="Flexible Architecture">
-    Build with:
-    - Modular components
-    - Extensible tools
-    - Customizable workflows
-  </Step>
-  <Step title="Advanced Capabilities">
-    Leverage:
-    - Multi-agent collaboration
-    - Memory and context management
-    - Tool integration
-  </Step>
-</Steps>
-
-## Use Cases
-
-<CardGroup cols={2}>
-  <Card title="Customer Service" icon="headset">
-    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
-  </Card>
-  <Card title="Data Analysis" icon="chart-line">
-    Create agents that can process, analyze, and derive insights from complex datasets.
-  </Card>
-  <Card title="Content Creation" icon="pen-nib">
-    Deploy agents that can generate, edit, and optimize content across various formats.
-  </Card>
-  <Card title="Process Automation" icon="gears">
-    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
-  </Card>
-</CardGroup>
-
-## Getting Started
-
-<Warning>
-  Before proceeding, ensure you have Python 3.9+ installed on your system.
-</Warning>
-
-### Quick Setup
-
-<CodeGroup>
-  ```bash pip
-  pip install praisonai
-  ```
-</CodeGroup>
-
-### Basic Example
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-# Create an agent
-researcher = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI",
-    backstory="You are an expert at a technology research group",
-    verbose=True,
-    llm="gpt-4o"
-)
-
-# Define a task
-task = Task(
-    name="research_task",
-    description="Analyze 2024's AI advancements",
-    expected_output="A detailed report",
-    agent=researcher
-)
-
-# Run the agents
-agents = PraisonAIAgents(
-    agents=[researcher],
-    tasks=[task],
-    verbose=False
-)
-
-result = agents.start()
-```
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Installation"
-    icon="download"
-    href="/code/installation"
-  >
-    Complete installation guide and setup instructions
-  </Card>
-  <Card
-    title="Quick Start"
-    icon="play"
-    href="/code/quickstart"
-  >
-    Build your first AI agent in minutes
-  </Card>
-</CardGroup>
-
-<Tip>
-  Join our community on [Discord](https://www.youtube.com/redirect?event=channel_description&redir_token=QUFFLUhqbUpiZE00YlF1MjB1LVNIeTFxTnV6MWRRc18wZ3xBQ3Jtc0ttQVZ0aFJ4RXRDanlBQU1Vd3lHU3BBcml3bzhoek1pTmlkZkFkWjlNVDBpRkxZd1dTOWhPc20xbHJOb1NibmNYeTEydklJT0pZOVJFQW93MmJOQXU0WkJNVzNsd29SZ3FBNFVwMTFGNThNSHBWVUhjSQ&q=https%3A%2F%2Fdiscord.gg%2FnNZu5gGT59) to connect with other developers and get help!
-</Tip> 

docs/code/quickstart.mdx

@@ -1,195 +0,0 @@
----
-title: Quick Start
-description: Build your first AI agent with PraisonAI
-icon: bolt
----
-
-<Steps>
-
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent">
-    Create `app.py`:
-## Code Example
-
-<CodeGroup>
-  ```python Basic
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
-
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
-
-  result = agents.start()
-  ```
-
-  ```python Advanced
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-  </Step>
-
-  <Step title="Run the Program">
-    Execute your script:
-    ```bash Terminal
-    python app.py
-    ```
-
-    You should see:
-    - Agent initialization
-    - Agents progress
-    - Final results
-    - Generated report
-  </Step>
-</Steps>
-
-## Advanced Usage
-
-### Creating Custom Tool
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# 1. Tool
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-
-# 2. Agent
-data_agent = Agent(
-    name="DataCollector",
-    role="Search Specialist",
-    goal="Perform internet searches to collect relevant information.",
-    backstory="Expert in finding and organising internet data.",
-    tools=[internet_search_tool],
-    self_reflect=False
-)
-
-# 3. Tasks
-collect_task = Task(
-    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-    expected_output="List of search results with titles, URLs, and snippets.",
-    agent=data_agent,
-    name="collect_data",
-)
-
-# 4. Start Agents
-agents = PraisonAIAgents(
-    agents=[data_agent],
-    tasks=[collect_task],
-    process="sequential"
-)
-
-agents.start()
-```
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Core Concepts"
-    icon="book"
-    href="/concepts/agents"
-  >
-    Learn about agents, tasks, and processes
-  </Card>
-  <Card
-    title="API Reference"
-    icon="code"
-    href="/api/praisonaiagents/index"
-  >
-    Explore detailed API documentation
-  </Card>
-</CardGroup> 

docs/concepts/agents.mdx

@@ -4,11 +4,8 @@ description: "Understanding Agents in PraisonAI"
 icon: "user"
 ---
 
-<Note>
-  Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
-</Note>
-
 # Understanding Agents
+Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
 
 ```mermaid
 graph LR
@@ -19,45 +16,34 @@ graph LR
     Agent2 --> Output([✓ Output])
     Process -.-> Agent1
     
-    %% Styling for main elements
-    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
-    
     %% Define subgraphs for agents and their tasks
-    subgraph Agent1[" "]
+    subgraph Agent1[ ]
         Task1[📋 Task]
         AgentIcon1[🤖 AI Agent]
         Tools1[🔧 Tools]
         
         Task1 --- AgentIcon1
         AgentIcon1 --- Tools1
-        
-        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
     end
     
-    subgraph Agent2[" "]
+    subgraph Agent2[ ]
         Task2[📋 Task]
         AgentIcon2[🤖 AI Agent]
         Tools2[🔧 Tools]
         
         Task2 --- AgentIcon2
         AgentIcon2 --- Tools2
-        
-        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
-        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
     end
-    
-    %% Style for agent containers
-    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
 ```
 
 ## Key Components
@@ -105,43 +91,254 @@ graph LR
   ```
 </CodeGroup>
 
-## Agent Types
 
 <Steps>
-  <Step title="Basic Agent">
-    - Single-purpose focus
-    - Direct user interaction
-    - Limited tool set
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
   </Step>
-  <Step title="Specialized Agent">
-    - Domain expertise
-    - Advanced capabilities
-    - Custom tools
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
   </Step>
-  <Step title="Collaborative Agent">
-    - Team interaction
-    - Task delegation
-    - Shared context
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+    ```bash Terminal
+    python app.py
+    ```
+
+    You should see:
+    - Agent initialization
+    - Agents progress
+    - Final results
+    - Generated report
   </Step>
 </Steps>
 
+## Agent Types
+
+<CardGroup cols={3}>
+  <Card title="Basic Agent" icon="user" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Perfect for straightforward tasks and direct interactions
+      </Accordion>
+      <Accordion title="Features">
+        - Single-purpose focus
+        - Direct user interaction
+        - Limited tool set
+        - Ideal for simple tasks
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Specialized Agent" icon="user-gear" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Experts in specific domains with advanced capabilities
+      </Accordion>
+      <Accordion title="Features">
+        - Domain expertise
+        - Advanced capabilities
+        - Custom tools
+        - Deep knowledge base
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Collaborative Agent" icon="users" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Designed for team-based operations and complex workflows
+      </Accordion>
+      <Accordion title="Features">
+        - Team interaction
+        - Task delegation
+        - Shared context
+        - Coordinated actions
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
 ## Best Practices
 
-<Warning>
-  Always implement proper error handling and resource management in your agent configurations.
-</Warning>
+<Note>
+Always implement proper error handling and resource management in your agent configurations.
+</Note>
 
-### Agent Design
-1. Clear role definition
-2. Specific, measurable goals
-3. Relevant tool selection
-4. Appropriate memory configuration
+<CardGroup cols={2}>
+  <Card title="Agent Design" icon="compass-drafting" iconType="solid">
+    <Steps>
+      <Step title="Role Definition">
+        Define clear, specific roles for each agent
+      </Step>
+      <Step title="Goal Setting">
+        Set specific, measurable goals
+      </Step>
+      <Step title="Tool Selection">
+        Choose relevant tools for the task
+      </Step>
+      <Step title="Memory Setup">
+        Configure appropriate memory settings
+      </Step>
+    </Steps>
+  </Card>
 
-### Agent Interaction
-1. Clear communication protocols
-2. Defined delegation rules
-3. Error handling mechanisms
-4. Resource management
+  <Card title="Agent Interaction" icon="network-wired" iconType="solid">
+    <Steps>
+      <Step title="Communication">
+        Establish clear communication protocols
+      </Step>
+      <Step title="Delegation">
+        Define explicit delegation rules
+      </Step>
+      <Step title="Error Handling">
+        Implement robust error handling
+      </Step>
+      <Step title="Resource Management">
+        Set up efficient resource allocation
+      </Step>
+    </Steps>
+  </Card>
+</CardGroup>
+
+## Async Capabilities
+
+<CardGroup cols={1}>
+  <Card title="Key Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Async Support">
+        ```python
+        async def main():
+            agent = Agent(name="AsyncAgent")
+            result = await agent.aprocess_task()
+        ```
+        - Full async/await support
+        - Non-blocking operations
+        - Enhanced performance
+      </Tab>
+      
+      <Tab title="Parallel Execution">
+        ```python
+        async def parallel_tasks():
+            tasks = [agent1.task(), agent2.task()]
+            results = await asyncio.gather(*tasks)
+        ```
+        - Parallel task execution
+        - Efficient resource usage
+        - Improved throughput
+      </Tab>
+      
+      <Tab title="Integration">
+        ```python
+        @agent.async_tool
+        async def custom_tool():
+            async with aiohttp.ClientSession() as session:
+                # Async operations
+                pass
+        ```
+        - Async tool integration
+        - Callback support
+        - Mixed sync/async operations
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
 
 ## Advanced Features
 

docs/concepts/knowledge.mdx

@@ -0,0 +1,241 @@
+---
+title: "AI Agents with Knowledge"
+sidebarTitle: "Knowledge"
+description: "Learn how to create AI agents with custom knowledge bases"
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    subgraph In[Input]
+        PDF[PDF]
+        TXT[TXT]
+        MD[MD]
+    end
+
+    subgraph Router[Vector Store]
+        DB[(Chroma)]
+    end
+    
+    subgraph Out[Agents]
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+
+    In --> Router
+    Router --> A1
+    Router --> A2
+    Router --> A3
+
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
+|---------|--------------------------------|---------------------------|
+| When Used | Pre-loaded before agent execution | Created and updated during runtime |
+| Purpose | Provide static reference information | Store dynamic context and interactions |
+| Storage | Read-only knowledge base | Read-write memory store |
+| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
+| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    Install PraisonAI Agents with knowledge support:
+    ```bash
+    pip install "praisonaiagents[knowledge]"
+    ```
+  </Step>
+
+  <Step title="Set API Key">
+    Set your OpenAI API key:
+    ```bash
+    export OPENAI_API_KEY=xxxxx
+    ```
+  </Step>
+
+  <Step title="Create Script">
+    Create a new file `app.py`:
+    ```python
+    from praisonaiagents import Agent
+
+    agent = Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["small.pdf"]
+    )
+
+    agent.start("What is KAG in one line?")
+    ```
+  </Step>
+</Steps>
+
+## Basic Usage
+
+The simplest way to create a knowledge-based agent is without any configuration:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"]
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Advanced Configuration
+
+For more control over the knowledge base, you can specify a configuration:
+
+```python
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Multi-Agent Knowledge System
+
+For more complex scenarios, you can create a knowledge-based system with multiple agents:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()
+```
+
+## Understanding Knowledge Configuration
+
+<AccordionGroup>
+  <Accordion title="Vector Store Options">
+    - **Provider**: Choose between different vector store backends (e.g., "chroma")
+    - **Collection Name**: Name for your knowledge collection
+    - **Path**: Location to store the vector database
+  </Accordion>
+  
+  <Accordion title="Supported File Types">
+    - PDF documents (*.pdf)
+    - Text files (*.txt)
+    - Markdown files (*.md, *.mdx)
+    - And more...
+  </Accordion>
+
+  <Accordion title="Multi-Agent Setup">
+    - **Role**: Define specialized roles for knowledge agents
+    - **Goal**: Set specific knowledge management objectives
+    - **Process**: Choose between sequential or parallel execution
+  </Accordion>
+</AccordionGroup>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Custom Knowledge" icon="book">
+    Import your own documents and data as knowledge sources
+  </Card>
+  <Card title="Vector Storage" icon="database">
+    Efficient storage and retrieval of knowledge embeddings
+  </Card>
+  <Card title="Multiple Sources" icon="layer-group">
+    Combine multiple documents and file types
+  </Card>
+  <Card title="Persistent Storage" icon="hard-drive">
+    Save and reuse knowledge bases across sessions
+  </Card>
+</CardGroup>
+
+## Best Practices
+
+1. **Document Preparation**
+   - Clean and well-formatted documents work best
+   - Break large documents into smaller chunks
+   - Use consistent formatting
+
+2. **Knowledge Organization**
+   - Group related documents together
+   - Use meaningful file names
+   - Keep knowledge bases focused and relevant
+
+3. **Performance Optimization**
+   - Monitor vector store size
+   - Clean up unused collections
+   - Use appropriate chunk sizes
+
+4. **Multi-Agent Coordination**
+   - Define clear roles and responsibilities
+   - Set appropriate logging levels for debugging
+   - Use unique collection names for different agent groups
+
+## Next Steps
+
+- Learn about [Memory Management](/concepts/memory) for long-term recall
+- Explore [Tool Integration](/concepts/tools) for enhanced capabilities
+- Check out [Examples](/examples) for implementation ideas

docs/concepts/memory.mdx

@@ -0,0 +1,472 @@
+---
+title: "AI Agents with Memory"
+sidebarTitle: "Memory"
+description: "Learn how to create AI agents with memory capabilities for maintaining context and information across tasks."
+icon: "brain"
+---
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
+|---------|--------------------------------|---------------------------|
+| When Used | Pre-loaded before agent execution | Created and updated during runtime |
+| Purpose | Provide static reference information | Store dynamic context and interactions |
+| Storage | Read-only knowledge base | Read-write memory store |
+| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
+| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install "praisonaiagents[memory]" duckduckgo_search 
+            ```
+            duckduckgo_search is a tool that allows agents to search the web.
+            It is required for the multiple agents example shown below.
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about AI",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about AI trends",
+      expected_output="Well-written blog post about AI trends",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with memory enabled
+  agents = PraisonAIAgents(
+      agents=[blog_agent],
+      tasks=[blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+  from praisonaiagents.tools import duckduckgo
+
+  # Create research agent with memory
+  research_agent = Agent(
+      role="Research Analyst",
+      goal="Research and document key information about topics",
+      backstory="Expert at analyzing and storing information in memory",
+      llm="gpt-4o-mini",
+      tools=[duckduckgo]
+  )
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about the research",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create research task
+  research_task = Task(
+      description="Research and document key information about AI trends",
+      expected_output="Detailed research findings about AI trends",
+      agent=research_agent
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about the research findings",
+      expected_output="Well-written blog post based on research",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with advanced memory configuration
+  agents = PraisonAIAgents(
+      agents=[research_agent, blog_agent],
+      tasks=[research_task, blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+</CodeGroup>
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert at analyzing and storing information in memory.
+    goal: Research and document key information about topics
+    role: Research Analyst
+    llm: gpt-4o-mini
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research and document key information about topics.
+        expected_output: Detailed research findings.
+
+  writer:
+    backstory: Expert at writing blog posts.
+    goal: Write a blog post about the research
+    role: Blog Writer
+    llm: gpt-4o-mini
+    tasks:
+      blog_task:
+        description: Write a blog post about the research.
+        expected_output: Well-written blog post based on research.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Memory
+
+<Card title="What is Agent Memory?" icon="question">
+  Memory in AI agents enables them to:
+  - Maintain context across multiple tasks
+  - Remember previous interactions and findings
+  - Build upon past knowledge
+  - Share information between agents
+  - Create more coherent and contextual responses
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Context Retention" icon="brain">
+    Maintain information across multiple interactions.
+  </Card>
+  <Card title="Information Sharing" icon="share-nodes">
+    Share knowledge between multiple agents.
+  </Card>
+  <Card title="Long-term Storage" icon="database">
+    Store and retrieve information over extended periods.
+  </Card>
+  <Card title="Memory Types" icon="layer-group">
+    Support for different memory types (short-term, long-term).
+  </Card>
+</CardGroup>
+
+## Multi-Agent Memory
+
+<Tabs>
+  <Tab title="Code">
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import duckduckgo
+
+    # Create first agent for research
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and analyze market trends",
+        backstory="Expert in market research and data analysis",
+        tools=[duckduckgo],
+        verbose=True
+    )
+
+    # Create second agent for report writing
+    writer = Agent(
+        role="Report Writer",
+        goal="Create comprehensive market reports",
+        backstory="Expert in technical writing and report creation",
+        verbose=True
+    )
+
+    # Create research task
+    research_task = Task(
+        description="Research current market trends",
+        expected_output="Detailed market analysis",
+        agent=researcher
+    )
+
+    # Create writing task
+    report_task = Task(
+        description="Create a market report based on research",
+        expected_output="Comprehensive market report",
+        agent=writer
+    )
+
+    # Create and start the agents with memory
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[research_task, report_task],
+        memory=True,
+        process="sequential"
+    )
+
+    # Start execution
+    result = agents.start()
+    ```
+  </Tab>
+  <Tab title="No Code">
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert in market research and data analysis.
+    goal: Research and analyze market trends
+    role: Research Analyst
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research current market trends.
+        expected_output: Detailed market analysis.
+
+  writer:
+    backstory: Expert in technical writing and report creation.
+    goal: Create comprehensive market reports
+    role: Report Writer
+    tasks:
+      report_task:
+        description: Create a market report based on research.
+        expected_output: Comprehensive market report.
+```
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with memory configuration
+agent = Agent(
+    role="Research Analyst",
+    goal="Research and retain information",
+    backstory="Expert in research and analysis",
+    tools=[duckduckgo],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+
+# Create agents with memory options
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,  # Enable memory
+    memory_config={
+        "provider": "rag",  # Use RAG for semantic search
+        "use_embedding": True,  # Enable embeddings for better search
+        "short_db": ".praison/short_term.db",  # Path for short-term memory
+        "long_db": ".praison/long_term.db",    # Path for long-term memory
+        "rag_db_path": ".praison/chroma_db"    # Path for vector database
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Memory Issues" icon="triangle-exclamation">
+    If memory isn't working as expected:
+    - Check memory configuration
+    - Enable verbose mode for debugging
+    - Verify memory provider settings
+  </Card>
+
+  <Card title="Context Flow" icon="code">
+    If context isn't being maintained:
+    - Review task dependencies
+    - Check memory configuration
+    - Verify agent communication
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="../features/autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="../features/mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, configure memory settings based on your specific use case requirements and expected interaction patterns.
+</Note>
+
+### Memory Configuration Options
+
+The memory system in PraisonAI supports various configuration options to customize how agents store and retrieve information:
+
+```python
+memory_config = {
+    # Memory Provider
+    "provider": "rag",        # Options: "rag", "mem0", "none"
+    "use_embedding": True,    # Enable semantic search with embeddings
+    
+    # Storage Paths
+    "short_db": ".praison/short_term.db",  # Short-term memory SQLite DB
+    "long_db": ".praison/long_term.db",    # Long-term memory SQLite DB
+    "rag_db_path": ".praison/chroma_db",   # Vector database path
+    
+    # Memory Settings
+    "ttl": 3600,             # Time to live for memory items (in seconds)
+    
+    # Optional Mem0 Config (if using mem0 provider)
+    "config": {
+        "api_key": "...",    # Mem0 API key
+        "org_id": "...",     # Organization ID
+        "project_id": "..."  # Project ID
+    }
+}
+
+# Create agents with memory configuration
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    memory_config=memory_config
+)
+```
+
+### Memory Types
+
+PraisonAI's memory system includes several types of memory:
+
+<CardGroup cols={2}>
+  <Card title="Short-term Memory" icon="bolt">
+    - Temporary storage for current context
+    - Automatically cleared between sessions
+    - Fast access for immediate task context
+  </Card>
+  <Card title="Long-term Memory" icon="database">
+    - Persistent storage for important information
+    - Semantic search capabilities with RAG
+    - Quality-based storage decisions
+  </Card>
+</CardGroup>
+
+### Memory Quality Control
+
+PraisonAI includes built-in quality control for memory storage:
+
+```python
+# Example of storing with quality metrics
+agents.memory.store_long_term(
+    text="Important information to remember",
+    metadata={
+        "task_id": "task_123",
+        "agent": "research_agent"
+    },
+    completeness=0.9,    # How complete is the information
+    relevance=0.85,      # How relevant to the task
+    clarity=0.95,        # How clear and well-structured
+    accuracy=0.9,        # How accurate is the information
+    weights={            # Custom weights for quality score
+        "completeness": 0.3,
+        "relevance": 0.3,
+        "clarity": 0.2,
+        "accuracy": 0.2
+    }
+)
+
+# Search with quality filter
+results = agents.memory.search_long_term(
+    query="search query",
+    min_quality=0.8,     # Only return high-quality matches
+    limit=5              # Maximum number of results
+)

docs/concepts/process.mdx

@@ -10,96 +10,287 @@ Process types in PraisonAI define how tasks are executed and how agents collabor
 
 ## Process Types Overview
 
-Three types of processes are implemented in PraisonAI:
+<CardGroup cols={3}>
+  <Card
+    title="Sequential Process"
+    icon="arrow-right"
+    href="/sequential-process"
+  >
+    Linear task execution in a predefined order
+  </Card>
+  <Card
+    title="Hierarchical Process"
+    icon="sitemap"
+    href="/hierarchical-process"
+  >
+    Manager-coordinated task execution with dynamic assignment
+  </Card>
+  <Card
+    title="Workflow Process"
+    icon="diagram-project"
+    href="/workflow-process"
+  >
+    Complex task flows with conditional execution and state management
+  </Card>
+</CardGroup>
 
-<Frame>
-  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
-          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M4 6h16M4 12h16m-7 6h7" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Sequential Process</h3>
-      </div>
-      <p>Linear task execution in a predefined order</p>
-    </div>
+## Sequential Process
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
-          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M12 6V4m0 2a2 2 0 100 4m0-4a2 2 0 110 4m-6 8a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4m6 6v10m6-2a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Hierarchical Process</h3>
-      </div>
-      <p>Manager-coordinated task execution with dynamic assignment</p>
-    </div>
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
-          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Workflow Process</h3>
-      </div>
-      <p>Complex task flows with conditional execution and state management</p>
-    </div>
-  </div>
-</Frame>
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
 
-### Sequential Process
 The simplest form of task execution where tasks are performed one after another.
 
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Characteristics
-    - Linear execution flow
-    - Predictable order
-    - Simple dependency management
-    - Direct task progression
+<Card>
+  ### Characteristics
+  - Linear execution flow
+  - Predictable order
+  - Simple dependency management
+  - Direct task progression
 
-    #### Usage
-    ```python
-    agents = PraisonAIAgents(
-        agents=[agent1, agent2],
-        tasks=[task1, task2, task3],
-        process="sequential"
-    )
-    ```
-  </div>
-</Frame>
+  ### Usage
+  ```python
+  agents = PraisonAIAgents(
+      agents=[agent1, agent2],
+      tasks=[task1, task2, task3],
+      process="sequential"
+  )
+  ```
+</Card>
+
+## Hierarchical Process
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
 
-### Hierarchical Process
 Uses a manager agent to coordinate task execution and agent assignments.
 
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Characteristics
-    - Manager-driven coordination
-    - Dynamic task assignment
-    - Flexible execution order
-    - Intelligent resource allocation
+<Card>
+  ### Characteristics
+  - Manager-driven coordination
+  - Dynamic task assignment
+  - Flexible execution order
+  - Intelligent resource allocation
 
-    #### Configuration
-    ```python
-    agents = PraisonAIAgents(
-        agents=[manager_agent, worker_agent1, worker_agent2],
-        tasks=[task1, task2],
-        process="hierarchical",
-        manager_llm="gpt-4o"
-    )
-    ```
-  </div>
-</Frame>
+  ### Configuration
+  ```python
+  agents = PraisonAIAgents(
+      agents=[manager_agent, worker_agent1, worker_agent2],
+      tasks=[task1, task2],
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+  ```
+</Card>
+
+## Workflow Process
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
 
-### Workflow Process
 Advanced process type supporting complex task relationships and conditional execution.
 
+<Card>
+  ### Features
+  - Task dependencies
+  - Conditional branching
+  - Loop handling
+  - Context sharing
+  - State management
+
+  ### Implementation
+  ```python
+  task_workflow = {
+      "start_task": {
+          "next": ["decision_task"],
+          "type": "task"
+      },
+      "decision_task": {
+          "type": "decision",
+          "conditions": {
+              "success": ["process_task"],
+              "failure": ["error_task"]
+          }
+      }
+  }
+  ```
+</Card>
+
+## Getting Started
+
+<Steps>
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent Sequential Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False,
+      process="sequential"
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents Hierarchical Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents 
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+<br /><br />
 <div className="relative w-full aspect-video">
   <iframe
     className="absolute top-0 left-0 w-full h-full"
@@ -111,110 +302,152 @@ Advanced process type supporting complex task relationships and conditional exec
 </div>
 <br />
 
-
-<Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    #### Features
-    - Task dependencies
-    - Conditional branching
-    - Loop handling
-    - Context sharing
-    - State management
-
-    #### Implementation
-    ```python
-    task_workflow = {
-        "start_task": {
-            "next": ["decision_task"],
-            "type": "task"
-        },
-        "decision_task": {
-            "type": "decision",
-            "conditions": {
-                "success": ["process_task"],
-                "failure": ["error_task"]
-            }
-        }
-    }
-    ```
-  </div>
-</Frame>
-
 ## Advanced Features
 
-### State Management
-- Task state tracking
-- Context preservation
-- Data flow control
+<CardGroup cols={2}>
+  <Card title="State Management" icon="database" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Task State Tracking">
+        Monitor and manage the progress of each task in real-time
+      </Accordion>
+      <Accordion title="Context Preservation">
+        Maintain important information across different stages of execution
+      </Accordion>
+      <Accordion title="Data Flow Control">
+        Manage how data moves between tasks and agents efficiently
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Error Handling
-- Graceful failure recovery
-- Alternative path execution
-- Error reporting
+  <Card title="Error Handling" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Graceful Recovery">
+        Automatically handle failures and continue execution
+      </Accordion>
+      <Accordion title="Alternative Paths">
+        Switch to backup plans when primary execution fails
+      </Accordion>
+      <Accordion title="Error Reporting">
+        Detailed error logs and diagnostic information
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Monitoring
-- Progress tracking
-- Performance metrics
-- Resource utilization
+  <Card title="Monitoring" icon="chart-line" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Progress Tracking">
+        Real-time visibility into task completion status
+      </Accordion>
+      <Accordion title="Performance Metrics">
+        Measure execution time and resource efficiency
+      </Accordion>
+      <Accordion title="Resource Usage">
+        Monitor system resource utilization
+      </Accordion>
+    </AccordionGroup>
+  </Card>
 
-### Integration
-- External system coordination
-- API synchronization
-- Event handling 
+  <Card title="Integration" icon="plug" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="External Systems">
+        Connect with other services and platforms
+      </Accordion>
+      <Accordion title="API Sync">
+        Maintain data consistency across systems
+      </Accordion>
+      <Accordion title="Event Handling">
+        React to system and external events
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
 
-## Async Process Execution
-All process types (sequential, workflow, hierarchical) support async execution through async generators. This enables efficient handling of async tasks and parallel processing.
+## Async Processing
 
-### Async Process Methods
-- `asequential`: Async version of sequential process
-- `aworkflow`: Async version of workflow process
-- `ahierarchical`: Async version of hierarchical process
+<Note>
+All process types support asynchronous execution through async generators, enabling efficient parallel processing and non-blocking operations.
+</Note>
 
-### Example Usage:
+### Core Async Methods
 
-```python
-import asyncio
-from praisonaiagents import Agent, Task, PraisonAIAgents
+<CardGroup cols={3}>
+  <Card title="asequential" icon="arrow-right">
+    Async version of sequential process for non-blocking linear execution
+  </Card>
+  <Card title="aworkflow" icon="diagram-project">
+    Async workflow process for complex parallel task execution
+  </Card>
+  <Card title="ahierarchical" icon="sitemap">
+    Async hierarchical process for distributed task management
+  </Card>
+</CardGroup>
 
-async def main():
-    # Create agents and tasks
-    agents = [async_agent1, async_agent2]
-    tasks = [async_task1, async_task2]
+### Process-Specific Features
 
-    # Initialize PraisonAIAgents with async workflow
-    agents = PraisonAIAgents(
-        agents=agents,
-        tasks=tasks,
-        process="workflow"  # or "sequential" or "hierarchical"
-    )
+<Tabs>
+  <Tab title="Sequential Process">
+    <Card>
+      - Tasks execute in order but don't block
+      - Maintains sequence while allowing async operations
+      - Perfect for I/O-heavy tasks
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="sequential",
+              async_mode=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Workflow Process">
+    <Card>
+      - Parallel execution of independent tasks
+      - Async branching and merging
+      - Dynamic task scheduling based on conditions
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="workflow",
+              async_mode=True,
+              parallel_tasks=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Hierarchical Process">
+    <Card>
+      - Async manager delegation
+      - Parallel subtask execution
+      - Dynamic worker assignment
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="hierarchical",
+              async_mode=True,
+              dynamic_assignment=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+</Tabs>
 
-    # Start async execution
-    result = await agents.astart()
-    print(result)
+### Key Benefits
 
-# Run the async workflow
-asyncio.run(main())
-```
-
-### Async Process Features:
-- Parallel task execution in workflow
-- Async task delegation in hierarchical process
-- Non-blocking task execution
-- Efficient resource utilization
-- Mixed sync/async task support
-
-### Process-Specific Async Behavior:
-1. **Sequential Process**:
-   - Tasks are executed in order
-   - Async tasks don't block other tasks
-   - Maintains sequential order while allowing async execution
-
-2. **Workflow Process**:
-   - Parallel execution of independent tasks
-   - Async branching and merging
-   - Dynamic task scheduling
-
-3. **Hierarchical Process**:
-   - Async manager delegation
-   - Parallel subtask execution
-   - Dynamic task assignment 
+<CardGroup cols={2}>
+  <Card title="Performance" icon="bolt">
+    - Efficient resource utilization
+    - Reduced waiting time
+    - Better throughput
+  </Card>
+  
+  <Card title="Flexibility" icon="puzzle-piece">
+    - Mix sync and async tasks
+    - Adaptable execution patterns
+    - Easy scaling
+  </Card>
+</CardGroup>

docs/concepts/tasks.mdx

@@ -4,117 +4,36 @@ description: "Understanding Tasks in PraisonAI"
 icon: "list-check"
 ---
 
-<Note>
-  Tasks are the fundamental units of work in PraisonAI, defining what needs to be done, how it should be done, and who should do it.
-</Note>
-
 # Understanding Tasks
+Tasks are units of work that agents execute. Each task has a clear goal, input requirements, and expected outputs.
 
-<Frame>
-  <div className="p-6 bg-white dark:bg-gray-800 rounded-lg">
-    <div className="grid grid-cols-1 gap-6">
-      {/* Task Flow Steps */}
-      <div className="flex justify-between items-center">
-        {/* Task Definition */}
-        <div className="flex-1 p-4 border-2 border-blue-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-full">
-              <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Task Definition</h4>
-          <p className="text-sm mt-1">Description & Requirements</p>
-        </div>
+```mermaid
+graph LR
+    Input[Input] ---> Task
+    
+    subgraph Task
+        direction LR
+        Role[Role]
+        Goal[Goal]
+        Story[Backstory]
+        Expected[Expected Output]
+        
+        Role --> Goal
+        Goal --> Story
+        Story --> Expected
+    end
+    
+    Task ---> Output[Output]
 
-        {/* Arrow 1 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
 
-        {/* Agent Assignment */}
-        <div className="flex-1 p-4 border-2 border-green-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-green-100 dark:bg-green-900 rounded-full">
-              <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M16 7a4 4 0 11-8 0 4 4 0 018 0zM12 14a7 7 0 00-7 7h14a7 7 0 00-7-7z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Agent Assignment</h4>
-          <p className="text-sm mt-1">Matching & Delegation</p>
-        </div>
-
-        {/* Arrow 2 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
-
-        {/* Execution */}
-        <div className="flex-1 p-4 border-2 border-purple-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-full">
-              <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M13 10V3L4 14h7v7l9-11h-7z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Execution</h4>
-          <p className="text-sm mt-1">Processing & Tools</p>
-        </div>
-
-        {/* Arrow 3 */}
-        <div className="flex-none px-4">
-          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
-          </svg>
-        </div>
-
-        {/* Output */}
-        <div className="flex-1 p-4 border-2 border-yellow-500 rounded-lg text-center">
-          <div className="flex items-center justify-center mb-2">
-            <div className="p-2 bg-yellow-100 dark:bg-yellow-900 rounded-full">
-              <svg className="w-6 h-6 text-yellow-600 dark:text-yellow-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 9l3 3-3 3m5 0h3M5 20h14a2 2 0 002-2V6a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z" />
-              </svg>
-            </div>
-          </div>
-          <h4 className="font-semibold">Output</h4>
-          <p className="text-sm mt-1">Results & Validation</p>
-        </div>
-      </div>
-
-      {/* Task States */}
-      <div className="grid grid-cols-4 gap-4 mt-4">
-        <div className="p-3 border border-gray-200 rounded-lg bg-blue-50 dark:bg-blue-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Pending</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-yellow-50 dark:bg-yellow-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">In Progress</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-green-50 dark:bg-green-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Completed</span>
-          </div>
-        </div>
-        <div className="p-3 border border-gray-200 rounded-lg bg-red-50 dark:bg-red-900">
-          <div className="flex items-center justify-center">
-            <span className="font-semibold">Failed</span>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</Frame>
+    class Input,Output input
+    class Role,Goal,Story,Expected process
+    class Task transparent
+```
 
 ## Core Components
 
@@ -155,6 +74,79 @@ icon: "list-check"
   ```
 </CodeGroup>
 
+# Understanding Tasks
+<br />
+```mermaid
+graph LR
+    %% Task Flow Steps
+    Start([▶ Start]) --> TaskDef
+    TaskDef --> AgentAssign
+    AgentAssign --> Exec
+    Exec --> Output([✓ Output])
+    
+    %% Define subgraphs for each step
+    subgraph TaskDef[" "]
+        TD_Icon[📋 Task Definition]
+        TD_Desc[Description & Requirements]
+        
+        TD_Icon --- TD_Desc
+        
+        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph AgentAssign[" "]
+        AA_Icon[🤖 Agent Assignment]
+        AA_Desc[Matching & Delegation]
+        
+        AA_Icon --- AA_Desc
+        
+        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Exec[" "]
+        E_Icon[⚙️ Execution]
+        E_Desc[Processing & Tools]
+        
+        E_Icon --- E_Desc
+        
+        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Style for containers
+    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br /><br />
+```mermaid
+graph LR
+    %% Task States
+    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
+    InProgress --> Completed([✅ Completed])
+    InProgress --> Failed([❌ Failed])
+    
+    %% Styling
+    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
+    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
+    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
 ## Task Types
 
 <Steps>
@@ -223,17 +215,300 @@ task_b = Task(
   </Card>
 </CardGroup>
 
+## Getting Started
+
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+
 ## Best Practices
 
-<Tip>
-  Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
-</Tip>
+<Note>
+Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
+</Note>
 
-1. Clear task descriptions
-2. Well-defined success criteria
-3. Appropriate tool selection
-4. Error handling consideration
-5. Resource management
+<CardGroup cols={2}>
+  <Card title="Task Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Clear Task Descriptions">
+        Write precise, unambiguous task descriptions that clearly state the task's purpose and requirements.
+        ```python
+        task = Task(
+            description=(
+                "Analyze Q4 2024 sales data "
+                "and generate growth metrics"
+            ),
+            expected_output="Sales growth report"
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Success Criteria">
+        Define measurable success criteria to ensure task completion can be properly evaluated.
+        ```python
+        task = Task(
+            success_criteria={
+                "format": "markdown",
+                "metrics": [
+                    "growth_rate",
+                    "revenue_change"
+                ],
+                "min_confidence": 0.95
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Task Dependencies">
+        Specify any dependencies or prerequisites for the task.
+        ```python
+        task = Task(
+            description="Generate final report",
+            dependencies=[
+                data_collection_task,
+                analysis_task
+            ],
+            wait_for_dependencies=True
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Resource Management" icon="server" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Tool Selection">
+        Choose appropriate tools that match your task's requirements.
+        ```python
+        task = Task(
+            tools=[
+                DataAnalyzer(),
+                ReportGenerator(),
+                MetricsCalculator()
+            ],
+            tool_config={
+                "timeout": 30,
+                "retry": True
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Error Handling">
+        Implement comprehensive error management strategies.
+        ```python
+        task = Task(
+            error_strategy="retry",
+            max_retries=3,
+            fallback_handler=handler,
+            error_callbacks=[
+                log_error,
+                notify_admin
+            ]
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Resource Limits">
+        Set appropriate resource constraints for optimal performance.
+        ```python
+        task = Task(
+            memory_limit="2GB",
+            timeout=300,
+            max_concurrent=5,
+            cpu_priority="high",
+            auto_scale=True
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Performance Monitoring">
+        Configure monitoring and logging for task execution.
+        ```python
+        task = Task(
+            monitoring={
+                "metrics": ["duration", "memory", "cpu"],
+                "log_level": "INFO",
+                "trace_enabled": True,
+                "alert_threshold": 0.85
+            }
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+## Async Task Execution
+
+<CardGroup cols={1}>
+  <Card title="Async Task Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Basic Configuration">
+        ```python
+        async_task = Task(
+            description="Perform async operation",
+            expected_output="Async result",
+            agent=async_agent,
+            async_execution=True,  # Enable async execution
+            callback=async_callback  # Optional async callback
+        )
+        ```
+        - Enable async execution with `async_execution=True`
+        - Perfect for I/O-heavy operations
+        - Supports parallel task execution
+      </Tab>
+      
+      <Tab title="Async Callbacks">
+        ```python
+        async def async_callback(output: TaskOutput):
+            await asyncio.sleep(1)  # Simulate async operation
+            print(f"Processed output: {output.raw}")
+
+        task = Task(
+            description="Task with async callback",
+            expected_output="Result",
+            agent=agent,
+            async_execution=True,
+            callback=async_callback
+        )
+        ```
+        - Support for async callbacks
+        - Process results asynchronously
+        - Handle task completion events
+      </Tab>
+      
+      <Tab title="Mixed Execution">
+        ```python
+        # Create mixed task types
+        sync_task = Task(
+            description="Synchronous operation",
+            async_execution=False
+        )
+
+        async_task = Task(
+            description="Asynchronous operation",
+            async_execution=True
+        )
+
+        # Run with mixed execution
+        agents = PraisonAIAgents(
+            agents=[sync_agent, async_agent],
+            tasks=[sync_task, async_task]
+        )
+        result = await agents.astart()
+        ```
+        - Mix sync and async tasks
+        - Flexible execution patterns
+        - Optimal resource utilization
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
+
+<Tip>
+**Pro Tip**: Use async execution for tasks that involve I/O operations (like API calls or file operations) to improve overall performance. Keep CPU-intensive tasks synchronous.
+</Tip>
 
 ## Next Steps
 
@@ -252,66 +527,4 @@ task_b = Task(
   >
     Explore the complete Task API documentation
   </Card>
-</CardGroup>
-
-## Async Task Execution
-Tasks can be executed asynchronously by setting `async_execution=True`. This is particularly useful for tasks that involve I/O operations or when you want to run multiple tasks in parallel.
-
-### Async Task Configuration
-```python
-async_task = Task(
-    description="Perform async operation",
-    expected_output="Async result",
-    agent=async_agent,
-    async_execution=True,  # Enable async execution
-    callback=async_callback  # Optional async callback
-)
-```
-
-### Async Callbacks
-Tasks support both synchronous and asynchronous callbacks:
-
-```python
-async def async_callback(output: TaskOutput):
-    await asyncio.sleep(1)  # Simulate async operation
-    print(f"Processed output: {output.raw}")
-
-# Task with async callback
-task = Task(
-    description="Task with async callback",
-    expected_output="Result",
-    agent=agent,
-    async_execution=True,
-    callback=async_callback
-)
-```
-
-### Mixing Sync and Async Tasks
-You can mix synchronous and asynchronous tasks in the same workflow:
-
-```python
-# Sync task
-sync_task = Task(
-    description="Synchronous operation",
-    expected_output="Sync result",
-    agent=sync_agent,
-    async_execution=False
-)
-
-# Async task
-async_task = Task(
-    description="Asynchronous operation",
-    expected_output="Async result",
-    agent=async_agent,
-    async_execution=True
-)
-
-# Create agents with mixed tasks
-agents = PraisonAIAgents(
-    agents=[sync_agent, async_agent],
-    tasks=[sync_task, async_task]
-)
-
-# Run asynchronously
-result = await agents.astart()
-``` 
+</CardGroup>

docs/concepts/tools.mdx

@@ -1,54 +1,49 @@
 ---
-title: "Tools"
-description: "Understanding Tools in PraisonAI"
+title: "AI Agents with Tools"
+sidebarTitle: "Tools"
+description: "Learn how to create AI agents that can use tools to interact with external systems and perform actions."
 icon: "screwdriver-wrench"
 ---
 
-# Understanding Tools
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
 
-Tools in PraisonAI are functions that agents can use to interact with external systems, perform computations, or access data. They extend the capabilities of agents beyond just language processing.
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
 
-## Tools Overview
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
 
-<Frame>
-  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
-          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">Search Tools</h3>
-      </div>
-      <p>Tools for searching and retrieving information from various sources</p>
-    </div>
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
-          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 7v8a2 2 0 002 2h6M8 7V5a2 2 0 012-2h4.586a1 1 0 01.707.293l4.414 4.414a1 1 0 01.293.707V15a2 2 0 01-2 2h-2M8 7H6a2 2 0 00-2 2v10a2 2 0 002 2h8a2 2 0 002-2v-2" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">File Tools</h3>
-      </div>
-      <p>Tools for reading, writing, and manipulating files</p>
-    </div>
+| Feature | [Knowledge](/concepts/knowledge) | [Tools](/concepts/tools) |
+|---------|--------------------------------|---------------------------|
+| Purpose | Static reference information | Dynamic interaction capabilities |
+| Access | Read-only reference | Execute actions and commands |
+| Updates | Manual through files | Real-time through tool calls |
+| Storage | Knowledge base | Assigned to specific agents |
+| Persistence | Permanent until changed | Available during agent execution |
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <div className="flex items-center mb-4">
-        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
-          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M3 6l3 1m0 0l-3 9a5.002 5.002 0 006.001 0M6 7l3 9M6 7l6-2m6 2l3-1m-3 1l-3 9a5.002 5.002 0 006.001 0M18 7l3 9m-3-9l-6-2m0-2v2m0 16V5m0 16H9m3 0h3" />
-          </svg>
-        </div>
-        <h3 className="ml-3 text-lg font-semibold">API Tools</h3>
-      </div>
-      <p>Tools for interacting with external APIs and services</p>
-    </div>
-  </div>
-</Frame>
+## Quick Start
+
+Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.
 
 ## Creating Custom Tool
 <Steps>
@@ -99,237 +94,554 @@ Assign the tool to an agent
 </Card>
 
 ## Implementing Tools Full Code Example
+<Tabs>
 
-Tools are implemented as Python functions with type hints and docstrings. Here's a complete example:
+<Tab title="Code">
+<Steps>
 
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
 
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents
-    from duckduckgo_search import DDGS
-    from typing import List, Dict
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
 
-    # 1. Tool Implementation
-    def internet_search_tool(query: str) -> List[Dict]:
-        """
-        Perform Internet Search using DuckDuckGo
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool Implementation
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search using DuckDuckGo
+    
+    Args:
+        query (str): The search query string
         
-        Args:
-            query (str): The search query string
-            
-        Returns:
-            List[Dict]: List of search results containing title, URL, and snippet
-        """
-        results = []
-        ddgs = DDGS()
-        for result in ddgs.text(keywords=query, max_results=5):
-            results.append({
-                "title": result.get("title", ""),
-                "url": result.get("href", ""),
-                "snippet": result.get("body", "")
-            })
-        return results
+    Returns:
+        List[Dict]: List of search results containing title, URL, and snippet
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
 
-    # 2. Assign the tool to an agent
-    data_agent = Agent(
-        name="DataCollector",
-        role="Search Specialist",
-        goal="Perform internet searches to collect relevant information.",
-        backstory="Expert in finding and organising internet data.",
-        tools=[internet_search_tool],
-        self_reflect=False
-    )
+# 2. Assign the tool to an agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
 
-    # 3. Task Definition
-    collect_task = Task(
-        description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-        expected_output="List of search results with titles, URLs, and snippets.",
-        agent=data_agent,
-        name="collect_data",
-    )
+# 3. Task Definition
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+)
 
-    # 4. Start Agents
-    agents = PraisonAIAgents(
-        agents=[data_agent],
-        tasks=[collect_task],
-        process="sequential"
-    )
+# 4. Start Agents
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task],
+    process="sequential"
+)
 
-    agents.start()
-    ```
+agents.start()
+```
+  </Step>
 
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
 
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
 
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+## In-build Tools in PraisonAI
+
+<CardGroup cols={2}>
+  <Card title="Search Tools" icon="magnifying-glass" href="/tools/search">
+    Tools for searching and retrieving information from various sources
+  </Card>
+  <Card title="Python Tools" icon="python" href="/tools/python_tools">
+    Essential Python utilities for data manipulation and scripting
+  </Card>
+  <Card title="Spider Tools" icon="spider" href="/tools/spider_tools">
+    Web crawling and scraping capabilities for data extraction
+  </Card>
+  <Card title="Arxiv Tools" icon="book" href="/tools/arxiv_tools">
+    Access and search academic papers from arXiv repository
+  </Card>
+  <Card title="Newspaper Tools" icon="newspaper" href="/tools/newspaper_tools">
+    Extract and parse content from news articles and websites
+  </Card>
+  <Card title="DuckDB Tools" icon="database" href="/tools/duckdb_tools">
+    Fast analytical SQL database operations and queries
+  </Card>
+  <Card title="DuckDuckGo Tools" icon="duck" href="/tools/duckduckgo_tools">
+    Web search functionality using DuckDuckGo's API
+  </Card>
+  <Card title="Calculator Tools" icon="calculator" href="/tools/calculator_tools">
+    Perform mathematical calculations and conversions
+  </Card>
+  <Card title="YAML Tools" icon="file-code" href="/tools/yaml_tools">
+    Parse and manipulate YAML format data
+  </Card>
+  <Card title="JSON Tools" icon="brackets-curly" href="/tools/json_tools">
+    Handle JSON data structures and operations
+  </Card>
+  <Card title="Pandas Tools" icon="table" href="/tools/pandas_tools">
+    Data analysis and manipulation using Pandas
+  </Card>
+  <Card title="YFinance Tools" icon="chart-line" href="/tools/yfinance_tools">
+    Fetch financial market data from Yahoo Finance
+  </Card>
+  <Card title="Shell Tools" icon="terminal" href="/tools/shell_tools">
+    Execute shell commands and system operations
+  </Card>
+  <Card title="Wikipedia Tools" icon="book-open" href="/tools/wikipedia_tools">
+    Access and search Wikipedia articles and data
+  </Card>
+  <Card title="XML Tools" icon="code" href="/tools/xml_tools">
+    Process and manipulate XML format data
+  </Card>
+  <Card title="File Tools" icon="file" href="/tools/file_tools">
+    File system operations and management utilities
+  </Card>
+  <Card title="Excel Tools" icon="file-excel" href="/tools/excel_tools">
+    Work with Excel spreadsheets and workbooks
+  </Card>
+  <Card title="CSV Tools" icon="file-csv" href="/tools/csv_tools">
+    Handle CSV file operations and transformations
+  </Card>
+</CardGroup>
+
+## Tools Overview
+
+<CardGroup cols={3}>
+  <Card
+    title="Search Tools"
+    icon="magnifying-glass"
+    iconType="solid"
+  >
+    Tools for searching and retrieving information from various sources
+  </Card>
+
+  <Card
+    title="File Tools"
+    icon="file"
+    iconType="solid"
+  >
+    Tools for reading, writing, and manipulating files
+  </Card>
+
+  <Card
+    title="API Tools"
+    icon="code"
+    iconType="solid"
+  >
+    Tools for interacting with external APIs and services
+  </Card>
+</CardGroup>
 
 ## Advanced Tool Features
 
 ### Tool Configuration
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    def configured_tool(
-        query: str,
-        max_results: int = 5,
-        timeout: int = 10
-    ) -> List[Dict]:
-        """
-        Example of a configurable tool
+```python
+def configured_tool(
+    query: str,
+    max_results: int = 5,
+    timeout: int = 10
+) -> List[Dict]:
+    """
+    Example of a configurable tool
+    
+    Args:
+        query (str): Search query
+        max_results (int): Maximum number of results
+        timeout (int): Request timeout in seconds
         
-        Args:
-            query (str): Search query
-            max_results (int): Maximum number of results
-            timeout (int): Request timeout in seconds
-            
-        Returns:
-            List[Dict]: Search results
-        """
-        # Tool implementation
-        pass
-    ```
-  </div>
+    Returns:
+        List[Dict]: Search results
+    """
+    # Tool implementation
+    pass
+```
 </Frame>
 
 ### Tool Chaining
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    def chain_tools(input_data: str) -> Dict:
-        """
-        Example of chaining multiple tools
+```python
+def chain_tools(input_data: str) -> Dict:
+    """
+    Example of chaining multiple tools
+    
+    Args:
+        input_data (str): Input data
         
-        Args:
-            input_data (str): Input data
-            
-        Returns:
-            Dict: Processed results
-        """
-        # 1. Search for data
-        search_results = internet_search_tool(input_data)
-        
-        # 2. Process results
-        processed_data = process_tool(search_results)
-        
-        # 3. Format output
-        return format_tool(processed_data)
-    ```
-  </div>
+    Returns:
+        Dict: Processed results
+    """
+    # 1. Search for data
+    search_results = internet_search_tool(input_data)
+    
+    # 2. Process results
+    processed_data = process_tool(search_results)
+    
+    # 3. Format output
+    return format_tool(processed_data)
+```
 </Frame>
 
 ### Tool Categories
 
-1. **Data Collection Tools**
-   - Web scraping
-   - API integration
-   - Database queries
-
-2. **Processing Tools**
-   - Data transformation
-   - Text analysis
-   - Image processing
-
-3. **Output Tools**
-   - File generation
-   - Report creation
-   - Data visualization
+<CardGroup cols={3}>
+  <Card title="Data Collection Tools">
+    - Web scraping
+    - API integration
+    - Database queries
+  </Card>
+  
+  <Card title="Processing Tools">
+    - Data transformation
+    - Text analysis
+    - Image processing
+  </Card>
+  
+  <Card title="Output Tools">
+    - File generation
+    - Report creation
+    - Data visualization
+  </Card>
+</CardGroup>
 
 ## Tool Integration
 
 ### Adding Tools to Agents
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    # Multiple tools
-    agent = Agent(
-        name="MultiTool Agent",
-        tools=[
-            internet_search_tool,
-            file_processing_tool,
-            api_integration_tool
-        ]
-    )
-    ```
-  </div>
+```python
+# Multiple tools
+agent = Agent(
+    name="MultiTool Agent",
+    tools=[
+        internet_search_tool,
+        file_processing_tool,
+        api_integration_tool
+    ]
+)
+```
 </Frame>
 
 ### Tool Dependencies
 <Frame>
-  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
-    ```python
-    # Tool with dependencies
-    def advanced_tool(data: Dict) -> Dict:
-        """
-        Tool that depends on external libraries
+```python
+# Tool with dependencies
+def advanced_tool(data: Dict) -> Dict:
+    """
+    Tool that depends on external libraries
+    
+    Args:
+        data (Dict): Input data
         
-        Args:
-            data (Dict): Input data
-            
-        Returns:
-            Dict: Processed data
-        """
-        try:
-            import required_library
-            # Tool implementation
-            return processed_result
-        except ImportError:
-            raise Exception("Required library not installed")
-    ```
-  </div>
+    Returns:
+        Dict: Processed data
+    """
+    try:
+        import required_library
+        # Tool implementation
+        return processed_result
+    except ImportError:
+        raise Exception("Required library not installed")
+```
 </Frame>
 
 ## Tool Guidelines
-  <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <h3 className="text-lg font-semibold mb-3">Best Practices</h3>
-      1. **Type Hints**
-         - Use Python type hints
-         - Define clear input/output types
-         - Document complex types
 
-      2. **Documentation**
-         - Write clear docstrings
-         - Explain parameters
-         - Provide usage examples
+<CardGroup cols={2}>
+  <Card title="Best Practices">
+    1. **Type Hints**
+       - Use Python type hints
+       - Define clear input/output types
+       - Document complex types
 
-      3. **Error Handling**
-         - Handle exceptions gracefully
-         - Return meaningful errors
-         - Validate inputs
-    </div>
+    2. **Documentation**
+       - Write clear docstrings
+       - Explain parameters
+       - Provide usage examples
 
-    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
-      <h3 className="text-lg font-semibold mb-3">Tool Types</h3>
-      1. **Search Tools**
-         - Web search
-         - Database queries
-         - Document search
+    3. **Error Handling**
+       - Handle exceptions gracefully
+       - Return meaningful errors
+       - Validate inputs
+  </Card>
 
-      2. **File Tools**
-         - Read/write operations
-         - File conversion
-         - Data extraction
+  <Card title="Tool Types">
+    1. **Search Tools**
+       - Web search
+       - Database queries
+       - Document search
 
-      3. **API Tools**
-         - REST API calls
-         - GraphQL queries
-         - Service integration
-    </div>
-  </div>
+    2. **File Tools**
+       - Read/write operations
+       - File conversion
+       - Data extraction
+
+    3. **API Tools**
+       - REST API calls
+       - GraphQL queries
+       - Service integration
+  </Card>
+</CardGroup>
 
 ## Best Practices Summary
 
-1. **Design Principles**
-   - Single responsibility
-   - Clear interfaces
-   - Proper documentation
-   - Error handling
+<Note>
+Following these best practices will help you create robust, efficient, and secure tools in PraisonAI.
+</Note>
 
-2. **Performance**
-   - Efficient processing
-   - Resource management
-   - Caching when appropriate
-   - Asynchronous operations
+<CardGroup cols={1}>
+  <Card title="Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Single Responsibility">
+        Each tool should have one clear purpose and do it well. Avoid creating tools that try to do too many things.
+        ```python
+        # Good Example
+        def process_image(image: np.array) -> np.array:
+            return processed_image
 
-3. **Security**
-   - Input validation
-   - Rate limiting
-   - API key management
-   - Error masking
+        # Avoid
+        def process_and_save_and_upload(image):
+            # Too many responsibilities
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Clear Interfaces">
+        Define explicit input/output types and maintain consistent parameter naming.
+        ```python
+        def search_tool(
+            query: str,
+            max_results: int = 10
+        ) -> List[Dict[str, Any]]:
+            """
+            Search for information with clear parameters
+            """
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Documentation">
+        Always include detailed docstrings and type hints.
+        ```python
+        def analyze_text(
+            text: str,
+            language: str = "en"
+        ) -> Dict[str, float]:
+            """
+            Analyze text sentiment and emotions.
+            
+            Args:
+                text: Input text to analyze
+                language: ISO language code
+                
+            Returns:
+                Dict with sentiment scores
+            """
+            pass
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Performance Optimization" icon="bolt" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Efficient Processing">
+        Optimize resource usage and processing time.
+        ```python
+        # Use generators for large datasets
+        def process_large_data():
+            for chunk in data_generator():
+                yield process_chunk(chunk)
+        ```
+      </Accordion>
+      
+      <Accordion title="Resource Management">
+        Properly handle resource allocation and cleanup.
+        ```python
+        async with aiohttp.ClientSession() as session:
+            # Resource automatically managed
+            await process_data(session)
+        ```
+      </Accordion>
+      
+      <Accordion title="Caching">
+        Implement caching for frequently accessed data.
+        ```python
+        @cache.memoize(timeout=300)
+        def expensive_operation(data: str) -> Dict:
+            return process_expensive(data)
+        ```
+      </Accordion>
+      
+      <Accordion title="Async Operations">
+        Use async/await for I/O-bound operations.
+        ```python
+        async def fetch_data(urls: List[str]):
+            async with aiohttp.ClientSession() as session:
+                tasks = [fetch_url(session, url) for url in urls]
+                return await asyncio.gather(*tasks)
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Security Best Practices" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Input Validation">
+        Always validate and sanitize inputs to prevent security vulnerabilities.
+        ```python
+        def process_user_input(data: str) -> str:
+            if not isinstance(data, str):
+                raise ValueError("Input must be string")
+            return sanitize_input(data.strip())
+        ```
+      </Accordion>
+      
+      <Accordion title="Rate Limiting">
+        Implement rate limiting for API calls to prevent abuse.
+        ```python
+        @rate_limit(calls=100, period=60)
+        async def api_call():
+            return await make_request()
+        ```
+      </Accordion>
+      
+      <Accordion title="API Key Management">
+        Securely handle API keys and credentials using environment variables.
+        ```python
+        # Use environment variables
+        api_key = os.getenv('API_KEY')
+        if not api_key:
+            raise ConfigError("API key not found")
+        ```
+      </Accordion>
+      
+      <Accordion title="Error Masking">
+        Hide sensitive information in error messages to prevent information leakage.
+        ```python
+        try:
+            result = process_sensitive_data()
+        except Exception as e:
+            # Log detailed error internally
+            logger.error(f"Detailed error: {str(e)}")
+            # Return sanitized error to user
+            raise PublicError("Processing failed")
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<Tip>
+**Pro Tip**: Start with these practices from the beginning of your project. It's easier to maintain good practices than to retrofit them later.
+</Tip>

docs/examples/adaptive-learning.mdx

@@ -0,0 +1,244 @@
+---
+title: "Adaptive Learning"
+description: "Learn how to create AI agents for personalized adaptive learning experiences."
+icon: "graduation-cap"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Assessor[Student Assessor]
+    Assessor --> Generator[Content Generator]
+    Generator --> Evaluator[Performance Evaluator]
+    Evaluator --> Adapter[Difficulty Adapter]
+    Adapter --> |decrease/increase| Generator
+    Adapter --> |maintain| Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Assessor fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Adapter fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Learn how to implement an adaptive learning system using AI agents for personalized education and dynamic content adjustment.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import Dict
+
+def assess_student_level():
+    """Simulates student assessment"""
+    levels = ["beginner", "intermediate", "advanced"]
+    current_time = int(time.time())
+    return levels[current_time % 3]
+
+def generate_content(level: str):
+    """Simulates content generation"""
+    content_types = {
+        "beginner": "basic concepts and examples",
+        "intermediate": "practice problems and applications",
+        "advanced": "complex scenarios and projects"
+    }
+    return content_types.get(level, "basic concepts")
+
+def evaluate_performance():
+    """Simulates performance evaluation"""
+    scores = ["low", "medium", "high"]
+    current_time = int(time.time())
+    return scores[current_time % 3]
+
+def adapt_difficulty(performance: str):
+    """Simulates difficulty adaptation"""
+    adaptations = {
+        "low": "decrease",
+        "medium": "maintain",
+        "high": "increase"
+    }
+    return adaptations.get(performance, "maintain")
+
+# Create specialized agents
+assessor = Agent(
+    name="Student Assessor",
+    role="Level Assessment",
+    goal="Assess student's current level",
+    instructions="Evaluate student's knowledge and skills",
+    tools=[assess_student_level]
+)
+
+generator = Agent(
+    name="Content Generator",
+    role="Content Creation",
+    goal="Generate appropriate learning content",
+    instructions="Create content based on student's level",
+    tools=[generate_content]
+)
+
+evaluator = Agent(
+    name="Performance Evaluator",
+    role="Performance Assessment",
+    goal="Evaluate student's performance",
+    instructions="Assess learning outcomes",
+    tools=[evaluate_performance]
+)
+
+adapter = Agent(
+    name="Difficulty Adapter",
+    role="Content Adaptation",
+    goal="Adapt content difficulty",
+    instructions="Adjust difficulty based on performance",
+    tools=[adapt_difficulty]
+)
+
+# Create workflow tasks
+assessment_task = Task(
+    name="assess_level",
+    description="Assess student's current level",
+    expected_output="Student's proficiency level",
+    agent=assessor,
+    is_start=True,
+    next_tasks=["generate_content"]
+)
+
+generation_task = Task(
+    name="generate_content",
+    description="Generate appropriate content",
+    expected_output="Learning content",
+    agent=generator,
+    next_tasks=["evaluate_performance"]
+)
+
+evaluation_task = Task(
+    name="evaluate_performance",
+    description="Evaluate student's performance",
+    expected_output="Performance assessment",
+    agent=evaluator,
+    next_tasks=["adapt_difficulty"]
+)
+
+adaptation_task = Task(
+    name="adapt_difficulty",
+    description="Adapt content difficulty",
+    expected_output="Difficulty adjustment",
+    agent=adapter,
+    task_type="decision",
+    condition={
+        "decrease": ["generate_content"],
+        "maintain": "",
+        "increase": ["generate_content"]
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[assessor, generator, evaluator, adapter],
+    tasks=[assessment_task, generation_task, evaluation_task, adaptation_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Adaptive Learning Workflow...")
+    print("=" * 50)
+    
+    # Run workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nAdaptive Learning Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()
+```
+    </Step>
+
+    <Step title="Start Agents">
+        Run your adaptive learning system:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+</Note>
+
+## Understanding Adaptive Learning
+
+<Card title="What is Adaptive Learning?" icon="question">
+  Adaptive learning enables:
+  - Personalized learning experiences
+  - Dynamic content adjustment
+  - Performance-based progression
+  - Continuous skill assessment
+  - Intelligent difficulty scaling
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Student Assessment" icon="user-graduate">
+    Evaluate student proficiency:
+    - Knowledge level assessment
+    - Skill gap identification
+    - Learning style analysis
+  </Card>
+  <Card title="Content Generation" icon="book">
+    Create personalized content:
+    - Level-appropriate materials
+    - Custom learning paths
+    - Interactive exercises
+  </Card>
+  <Card title="Performance Tracking" icon="chart-line">
+    Monitor learning progress:
+    - Real-time evaluation
+    - Progress tracking
+    - Achievement metrics
+  </Card>
+  <Card title="Dynamic Adaptation" icon="sliders">
+    Adjust learning experience:
+    - Difficulty scaling
+    - Content optimization
+    - Pace adjustment
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Prompt Chaining" icon="link" href="/features/promptchaining">
+    Learn about chaining prompts for complex workflows
+  </Card>
+  <Card title="Evaluator Optimizer" icon="gauge" href="/features/evaluator-optimiser">
+    Explore how to optimize and evaluate solutions
+  </Card>
+</CardGroup> 

docs/examples/code-review.mdx

@@ -0,0 +1,207 @@
+---
+title: "Code Review"
+sidebarTitle: "Code Review"
+description: "Learn how to create AI agents for automated code review and issue resolution."
+icon: "code"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Analyzer[Code Analyzer]
+    Analyzer --> Suggester[Fix Suggester]
+    Suggester --> Applier[Fix Applier]
+    Applier -->|fixed| Out[Out]
+    Applier -->|manual_review| Suggester
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Suggester fill:#2E8B57,color:#fff
+    style Applier fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how AI agents can automate code review, from analysis through fix suggestion and application.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        import time
+        from typing import List, Dict
+
+        def analyze_code_changes():
+            """Simulates code analysis"""
+            issues = [
+                {"type": "style", "severity": "low", "file": "main.py"},
+                {"type": "security", "severity": "high", "file": "auth.py"},
+                {"type": "performance", "severity": "medium", "file": "data.py"}
+            ]
+            return issues[int(time.time()) % 3]
+
+        def suggest_fixes(issue: Dict):
+            """Simulates fix suggestions"""
+            fixes = {
+                "style": "Apply PEP 8 formatting",
+                "security": "Implement input validation",
+                "performance": "Use list comprehension"
+            }
+            return fixes.get(issue["type"], "Review manually")
+
+        def apply_automated_fix(fix: str):
+            """Simulates applying automated fixes"""
+            success = int(time.time()) % 2 == 0
+            return "fixed" if success else "manual_review"
+
+        # Create specialized agents
+        analyzer = Agent(
+            name="Code Analyzer",
+            role="Code analysis",
+            goal="Analyze code changes and identify issues",
+            instructions="Review code changes and report issues",
+            tools=[analyze_code_changes]
+        )
+
+        fix_suggester = Agent(
+            name="Fix Suggester",
+            role="Solution provider",
+            goal="Suggest fixes for identified issues",
+            instructions="Provide appropriate fix suggestions",
+            tools=[suggest_fixes]
+        )
+
+        fix_applier = Agent(
+            name="Fix Applier",
+            role="Fix implementation",
+            goal="Apply suggested fixes automatically when possible",
+            instructions="Implement suggested fixes and report results",
+            tools=[apply_automated_fix]
+        )
+
+        # Create workflow tasks
+        analysis_task = Task(
+            name="analyze_code",
+            description="Analyze code changes for issues",
+            expected_output="Identified code issues",
+            agent=analyzer,
+            is_start=True,
+            next_tasks=["suggest_fixes"]
+        )
+
+        suggestion_task = Task(
+            name="suggest_fixes",
+            description="Suggest fixes for identified issues",
+            expected_output="Fix suggestions",
+            agent=fix_suggester,
+            next_tasks=["apply_fixes"]
+        )
+
+        fix_task = Task(
+            name="apply_fixes",
+            description="Apply suggested fixes",
+            expected_output="Fix application status",
+            agent=fix_applier,
+            task_type="decision",
+            condition={
+                "fixed": "",
+                "manual_review": ["suggest_fixes"]  # Loop back for manual review
+            }
+        )
+
+        # Create workflow
+        workflow = PraisonAIAgents(
+            agents=[analyzer, fix_suggester, fix_applier],
+            tasks=[analysis_task, suggestion_task, fix_task],
+            process="workflow",
+            verbose=True
+        )
+
+        def main():
+            print("\nStarting Code Review Workflow...")
+            print("=" * 50)
+            
+            # Run workflow
+            results = workflow.start()
+            
+            # Print results
+            print("\nCode Review Results:")
+            print("=" * 50)
+            for task_id, result in results["task_results"].items():
+                if result:
+                    print(f"\nTask: {task_id}")
+                    print(f"Result: {result.raw}")
+                    print("-" * 50)
+
+        if __name__ == "__main__":
+            main()
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Code Review
+
+<Card title="What is Code Review?" icon="question">
+  Automated code review workflow enables:
+  - Automated issue detection
+  - Intelligent fix suggestions
+  - Automated fix application
+  - Manual review routing when needed
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Issue Detection" icon="magnifying-glass">
+    Automatically identify code issues and their severity.
+  </Card>
+  <Card title="Fix Suggestions" icon="lightbulb">
+    Generate appropriate fix suggestions based on issue type.
+  </Card>
+  <Card title="Automated Fixes" icon="wand-magic-sparkles">
+    Apply fixes automatically when possible.
+  </Card>
+  <Card title="Manual Review" icon="user-check">
+    Route complex issues for manual review.
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Prompt Chaining" icon="link" href="/features/promptchaining">
+    Learn about sequential prompt execution
+  </Card>
+  <Card title="Evaluator Optimizer" icon="magnifying-glass-chart" href="/features/evaluator-optimiser">
+    Explore optimization techniques
+  </Card>
+</CardGroup> 

docs/examples/customer-service.mdx

@@ -0,0 +1,206 @@
+---
+title: Customer Service
+sidebar_title: Customer Service
+icon: "user-group"
+---
+
+```mermaid
+flowchart LR
+  In[In] --> Classifier[Query Classifier]
+  Classifier --> Handler[Query Handler]
+  Handler --> Evaluator[Satisfaction Evaluator]
+  Evaluator --> Optimizer[Response Optimizer]
+  Optimizer --> Out[Out]
+  Optimizer --> Classifier
+  
+  style In fill:#8B0000,color:#fff
+  style Out fill:#8B0000,color:#fff
+  style Classifier fill:#2E8B57,color:#fff
+  style Handler fill:#2E8B57,color:#fff
+  style Evaluator fill:#2E8B57,color:#fff
+  style Optimizer fill:#2E8B57,color:#fff
+```
+
+Learn how to create AI agents for automated customer service and response optimization.
+
+## Quick Start
+
+1. Install the PraisonAI Agents package:
+```bash
+pip install praisonaiagents
+```
+
+2. Set your OpenAI API key:
+```bash
+export OPENAI_API_KEY=your_api_key_here
+```
+
+3. Create a file named `app.py`:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import Dict, List
+
+def classify_query():
+    """Simulates query classification"""
+    query_types = [
+        {"type": "technical", "priority": "high", "complexity": "complex"},
+        {"type": "billing", "priority": "medium", "complexity": "simple"},
+        {"type": "general", "priority": "low", "complexity": "simple"}
+    ]
+    return query_types[int(time.time()) % 3]
+
+def handle_query(query: Dict):
+    """Simulates query handling"""
+    responses = {
+        "technical": "Technical support solution provided",
+        "billing": "Billing inquiry resolved",
+        "general": "General information provided"
+    }
+    return responses.get(query["type"], "Query forwarded to specialist")
+
+def evaluate_satisfaction():
+    """Simulates satisfaction evaluation"""
+    scores = ["satisfied", "neutral", "unsatisfied"]
+    return scores[int(time.time()) % 3]
+
+def optimize_response(satisfaction: str):
+    """Simulates response optimization"""
+    optimizations = {
+        "satisfied": "maintain_approach",
+        "neutral": "minor_adjustments",
+        "unsatisfied": "major_revision"
+    }
+    return optimizations.get(satisfaction, "review_process")
+
+# Create specialized agents
+classifier = Agent(
+    name="Query Classifier",
+    role="Query Classification",
+    goal="Classify incoming customer queries",
+    instructions="Analyze and categorize customer queries",
+    tools=[classify_query]
+)
+
+handler = Agent(
+    name="Query Handler",
+    role="Query Resolution",
+    goal="Handle customer queries appropriately",
+    instructions="Provide appropriate responses to queries",
+    tools=[handle_query]
+)
+
+evaluator = Agent(
+    name="Satisfaction Evaluator",
+    role="Satisfaction Assessment",
+    goal="Evaluate customer satisfaction",
+    instructions="Assess response effectiveness",
+    tools=[evaluate_satisfaction]
+)
+
+optimizer = Agent(
+    name="Response Optimizer",
+    role="Service Optimization",
+    goal="Optimize service based on feedback",
+    instructions="Improve response strategies",
+    tools=[optimize_response]
+)
+
+# Create workflow tasks
+classification_task = Task(
+    name="classify_query",
+    description="Classify customer query",
+    expected_output="Query classification",
+    agent=classifier,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "high": ["handle_query", "evaluate_satisfaction"],
+        "medium": ["handle_query", "evaluate_satisfaction"],
+        "low": ["handle_query"]
+    }
+)
+
+handling_task = Task(
+    name="handle_query",
+    description="Handle customer query",
+    expected_output="Query response",
+    agent=handler,
+    next_tasks=["evaluate_satisfaction"]
+)
+
+evaluation_task = Task(
+    name="evaluate_satisfaction",
+    description="Evaluate customer satisfaction",
+    expected_output="Satisfaction level",
+    agent=evaluator,
+    next_tasks=["optimize_response"]
+)
+
+optimization_task = Task(
+    name="optimize_response",
+    description="Optimize response strategy",
+    expected_output="Optimization recommendations",
+    agent=optimizer,
+    task_type="decision",
+    condition={
+        "major_revision": ["classify_query"],
+        "minor_adjustments": "",
+        "maintain_approach": ""
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[classifier, handler, evaluator, optimizer],
+    tasks=[classification_task, handling_task, evaluation_task, optimization_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Customer Service Optimization Workflow...")
+    print("=" * 50)
+    
+    # Run workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nCustomer Service Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()
+```
+
+4. Run the system:
+```bash
+python app.py
+```
+
+<Note>
+**Requirements**
+- Python 3.10 or higher
+- OpenAI API key
+</Note>
+
+## Understanding Customer Service
+
+The customer service workflow demonstrates how AI agents can automate and optimize customer support operations. The system uses specialized agents to classify queries, handle responses, evaluate satisfaction, and continuously improve service quality through feedback-driven optimization.
+
+## Features
+
+- **Query Classification**: Automatically categorizes customer inquiries based on type, priority, and complexity
+- **Intelligent Response Handling**: Provides appropriate responses based on query classification
+- **Satisfaction Evaluation**: Assesses customer satisfaction with provided solutions
+- **Response Optimization**: Continuously improves service quality through feedback analysis
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining)
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) 

docs/examples/emergency-response.mdx

@@ -0,0 +1,241 @@
+---
+title: "Emergency Response"
+sidebarTitle: "Emergency Response"
+description: "Learn how to create AI agents for coordinated emergency response and resource management."
+icon: "truck-medical"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Router[Emergency Router]
+    Router -->|critical/high| Dispatcher[Resource Dispatcher]
+    Router -->|medium/low| Dispatcher
+    Dispatcher --> Monitor[Response Monitor]
+    Monitor -->|ongoing| Coordinator[Response Coordinator]
+    Monitor -->|completed| Out[Out]
+    Coordinator --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style Dispatcher fill:#2E8B57,color:#fff
+    style Monitor fill:#2E8B57,color:#fff
+    style Coordinator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how AI agents can coordinate emergency response, from initial assessment through resource dispatch and response monitoring.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        import time
+        from typing import Dict, List
+
+        def assess_emergency(incident: Dict):
+            """Simulates emergency assessment"""
+            severity_levels = ["low", "medium", "high", "critical"]
+            current_time = int(time.time())
+            severity = severity_levels[current_time % 4]
+            print(f"Incident assessed with {severity} severity")
+            return severity
+
+        def dispatch_resources(severity: str):
+            """Simulates resource dispatch"""
+            resources = {
+                "low": ["local_police"],
+                "medium": ["local_police", "ambulance"],
+                "high": ["local_police", "ambulance", "fire"],
+                "critical": ["local_police", "ambulance", "fire", "special_units"]
+            }
+            dispatched = resources.get(severity, ["local_police"])
+            print(f"Dispatching resources: {dispatched}")
+            return dispatched
+
+        def monitor_response():
+            """Simulates response monitoring"""
+            current_time = int(time.time())
+            status = "completed" if current_time % 3 == 0 else "ongoing"
+            return status
+
+        # Create specialized agents
+        router = Agent(
+            name="Emergency Router",
+            role="Emergency Assessment",
+            goal="Evaluate emergency severity and type",
+            instructions="Assess incident and determine required response",
+            tools=[assess_emergency]
+        )
+
+        dispatcher = Agent(
+            name="Resource Dispatcher",
+            role="Resource Management",
+            goal="Coordinate and dispatch appropriate resources",
+            instructions="Deploy resources based on emergency assessment",
+            tools=[dispatch_resources]
+        )
+
+        monitor = Agent(
+            name="Response Monitor",
+            role="Response Tracking",
+            goal="Track response progress and effectiveness",
+            instructions="Monitor ongoing response and provide status updates",
+            tools=[monitor_response]
+        )
+
+        synthesizer = Agent(
+            name="Response Coordinator",
+            role="Response Synthesis",
+            goal="Coordinate multi-agency response",
+            instructions="Synthesize information and coordinate overall response"
+        )
+
+        # Create workflow tasks
+        assessment_task = Task(
+            name="assess_emergency",
+            description="Evaluate emergency severity and type",
+            expected_output="Emergency severity level",
+            agent=router,
+            is_start=True,
+            task_type="decision",
+            condition={
+                "critical": ["dispatch_resources", "monitor_response"],
+                "high": ["dispatch_resources", "monitor_response"],
+                "medium": ["dispatch_resources"],
+                "low": ["dispatch_resources"]
+            }
+        )
+
+        dispatch_task = Task(
+            name="dispatch_resources",
+            description="Deploy appropriate emergency resources",
+            expected_output="List of dispatched resources",
+            agent=dispatcher,
+            next_tasks=["monitor_response"]
+        )
+
+        monitor_task = Task(
+            name="monitor_response",
+            description="Track response progress",
+            expected_output="Response status",
+            agent=monitor,
+            task_type="decision",
+            condition={
+                "ongoing": ["coordinate_response"],
+                "completed": ""
+            }
+        )
+
+        coordinate_task = Task(
+            name="coordinate_response",
+            description="Coordinate overall emergency response",
+            expected_output="Coordinated response plan",
+            agent=synthesizer,
+            context=[assessment_task, dispatch_task, monitor_task]
+        )
+
+        # Create workflow
+        workflow = PraisonAIAgents(
+            agents=[router, dispatcher, monitor, synthesizer],
+            tasks=[assessment_task, dispatch_task, monitor_task, coordinate_task],
+            process="workflow",
+            verbose=True
+        )
+
+        def main():
+            # Simulate emergency incident
+            incident = {
+                "type": "fire",
+                "location": "123 Main St",
+                "reported_time": time.time()
+            }
+            
+            print("\nStarting Emergency Response Workflow...")
+            print("=" * 50)
+            
+            # Run workflow
+            results = workflow.start(initial_input=incident)
+            
+            # Print results
+            print("\nEmergency Response Results:")
+            print("=" * 50)
+            for task_id, result in results["task_results"].items():
+                if result:
+                    print(f"\nTask: {task_id}")
+                    print(f"Result: {result.raw}")
+                    print("-" * 50)
+
+        if __name__ == "__main__":
+            main()
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Emergency Response
+
+<Card title="What is Emergency Response?" icon="question">
+  Emergency response workflow enables:
+  - Dynamic severity assessment
+  - Automated resource dispatch
+  - Real-time response monitoring
+  - Multi-agency coordination
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Severity Assessment" icon="gauge-high">
+    Automatically evaluate incident severity and type.
+  </Card>
+  <Card title="Resource Management" icon="truck">
+    Dispatch appropriate resources based on severity.
+  </Card>
+  <Card title="Response Monitoring" icon="eye">
+    Track response progress and effectiveness.
+  </Card>
+  <Card title="Response Coordination" icon="people-group">
+    Coordinate multi-agency response efforts.
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Prompt Chaining" icon="link" href="/features/promptchaining">
+    Learn about sequential prompt execution
+  </Card>
+  <Card title="Evaluator Optimizer" icon="magnifying-glass-chart" href="/features/evaluator-optimiser">
+    Explore optimization techniques
+  </Card>
+</CardGroup> 

docs/examples/examples.mdx

@@ -0,0 +1,109 @@
+---
+title: "Examples"
+sidebarTitle: "Examples"
+description: "Explore real-world examples and use cases built with PraisonAI Agents."
+icon: "code"
+---
+
+## Use Cases
+
+<CardGroup cols={2}>
+  <Card 
+    title="Predictive Maintenance" 
+    icon="wrench" 
+    href="/examples/predictive-maintenance"
+  >
+    Learn how to create AI agents for predictive maintenance and equipment monitoring.
+  </Card>
+  
+  <Card 
+    title="Emergency Response" 
+    icon="truck-medical" 
+    href="/examples/emergency-response"
+  >
+    Learn how to create AI agents for coordinated emergency response and resource management.
+  </Card>
+
+  <Card 
+    title="Code Review" 
+    icon="code" 
+    href="/examples/code-review"
+  >
+    Learn how to create AI agents for automated code review and issue resolution.
+  </Card>
+
+  <Card 
+    title="Adaptive Learning" 
+    icon="graduation-cap" 
+    href="/examples/adaptive-learning"
+  >
+    Learn how to create AI agents for personalized adaptive learning experiences.
+  </Card>
+
+  <Card 
+    title="Supply Chain" 
+    icon="truck" 
+    href="/examples/supply-chain"
+  >
+    Learn how to create AI agents for supply chain risk management and mitigation.
+  </Card>
+
+  <Card 
+    title="Customer Service" 
+    icon="headset" 
+    href="/examples/customer-service"
+  >
+    Learn how to create AI agents for automated customer service and support.
+  </Card>
+
+  <Card 
+    title="Fraud Detection" 
+    icon="shield-check" 
+    href="/examples/fraud-detection"
+  >
+    Learn how to create AI agents for real-time fraud detection and alert management.
+  </Card>
+
+  <Card 
+    title="Healthcare Diagnosis" 
+    icon="stethoscope" 
+    href="/examples/healthcare-diagnosis"
+  >
+    Learn how to create AI agents for medical diagnosis and treatment recommendations.
+  </Card>
+
+  <Card 
+    title="Smart City" 
+    icon="city" 
+    href="/examples/smart-city"
+  >
+    Learn how to create AI agents for smart city resource optimization and management.
+  </Card>
+
+  <Card 
+    title="Multilingual Content" 
+    icon="language" 
+    href="/examples/multilingual-content"
+  >
+    Learn how to create AI agents for multilingual content generation and cultural adaptation.
+  </Card>
+</CardGroup>
+
+## What's Next?
+
+<CardGroup cols={2}>
+  <Card
+    title="Features"
+    icon="stars"
+    href="/features/promptchaining"
+  >
+    Explore advanced features like prompt chaining and parallel execution
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api-reference"
+  >
+    View the complete API reference documentation
+  </Card>
+</CardGroup>

docs/examples/fraud-detection.mdx

@@ -0,0 +1,237 @@
+---
+title: "Fraud Detection"
+sidebarTitle: "Fraud Detection"
+description: "Learn how to create AI agents for real-time fraud detection and alert management."
+icon: "shield-check"
+---
+
+```mermaid
+flowchart LR
+    In[In] --> Analyzer[Transaction Analyzer]
+    Analyzer -->|high risk| Verifier[Identity Verifier]
+    Analyzer -->|all risks| Checker[Pattern Checker]
+    Checker --> Generator[Alert Generator]
+    Verifier --> Generator
+    Generator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Verifier fill:#2E8B57,color:#fff
+    style Checker fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how AI agents can detect fraud in real-time, from transaction analysis through alert generation.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        import time
+        from typing import Dict, List
+        import asyncio
+
+        def analyze_transaction():
+            """Simulates transaction analysis"""
+            transactions = [
+                {"type": "credit_card", "amount": 5000, "location": "foreign", "risk": "high"},
+                {"type": "wire", "amount": 2000, "location": "domestic", "risk": "medium"},
+                {"type": "online", "amount": 500, "location": "domestic", "risk": "low"}
+            ]
+            return transactions[int(time.time()) % 3]
+
+        def check_patterns(transaction: Dict):
+            """Simulates pattern checking"""
+            patterns = {
+                "high": ["unusual_location", "large_amount"],
+                "medium": ["frequency_anomaly"],
+                "low": ["within_normal_limits"]
+            }
+            return patterns.get(transaction["risk"], ["unknown"])
+
+        def verify_identity():
+            """Simulates identity verification"""
+            results = ["verified", "suspicious", "failed"]
+            return results[int(time.time()) % 3]
+
+        def generate_alert(verification: str, patterns: List[str]):
+            """Simulates alert generation"""
+            if verification == "failed" or "unusual_location" in patterns:
+                return "high_priority_alert"
+            elif verification == "suspicious":
+                return "medium_priority_alert"
+            return "low_priority_alert"
+
+        # Create specialized agents
+        transaction_analyzer = Agent(
+            name="Transaction Analyzer",
+            role="Transaction Analysis",
+            goal="Analyze transactions for suspicious patterns",
+            instructions="Monitor and analyze financial transactions",
+            tools=[analyze_transaction]
+        )
+
+        pattern_checker = Agent(
+            name="Pattern Checker",
+            role="Pattern Detection",
+            goal="Identify suspicious patterns",
+            instructions="Check for known fraud patterns",
+            tools=[check_patterns]
+        )
+
+        identity_verifier = Agent(
+            name="Identity Verifier",
+            role="Identity Verification",
+            goal="Verify transaction identities",
+            instructions="Perform identity verification checks",
+            tools=[verify_identity]
+        )
+
+        alert_generator = Agent(
+            name="Alert Generator",
+            role="Alert Management",
+            goal="Generate appropriate alerts",
+            instructions="Create and prioritize alerts",
+            tools=[generate_alert]
+        )
+
+        # Create workflow tasks
+        analysis_task = Task(
+            name="analyze_transaction",
+            description="Analyze transaction details",
+            expected_output="Transaction analysis",
+            agent=transaction_analyzer,
+            is_start=True,
+            task_type="decision",
+            condition={
+                "high": ["check_patterns", "verify_identity"],
+                "medium": ["check_patterns"],
+                "low": ["check_patterns"]
+            }
+        )
+
+        pattern_task = Task(
+            name="check_patterns",
+            description="Check for suspicious patterns",
+            expected_output="Identified patterns",
+            agent=pattern_checker,
+            next_tasks=["generate_alert"],
+            async_execution=True
+        )
+
+        verification_task = Task(
+            name="verify_identity",
+            description="Verify transaction identity",
+            expected_output="Verification result",
+            agent=identity_verifier,
+            next_tasks=["generate_alert"],
+            async_execution=True
+        )
+
+        alert_task = Task(
+            name="generate_alert",
+            description="Generate fraud alert",
+            expected_output="Alert priority",
+            agent=alert_generator,
+            context=[pattern_task, verification_task]
+        )
+
+        # Create workflow
+        workflow = PraisonAIAgents(
+            agents=[transaction_analyzer, pattern_checker, identity_verifier, alert_generator],
+            tasks=[analysis_task, pattern_task, verification_task, alert_task],
+            process="workflow",
+            verbose=True
+        )
+
+        async def main():
+            print("\nStarting Fraud Detection Workflow...")
+            print("=" * 50)
+            
+            # Run workflow
+            results = await workflow.astart()
+            
+            # Print results
+            print("\nFraud Detection Results:")
+            print("=" * 50)
+            for task_id, result in results["task_results"].items():
+                if result:
+                    print(f"\nTask: {task_id}")
+                    print(f"Result: {result.raw}")
+                    print("-" * 50)
+
+        if __name__ == "__main__":
+            asyncio.run(main())
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Fraud Detection
+
+<Card title="What is Fraud Detection?" icon="question">
+  Automated fraud detection workflow enables:
+  - Real-time transaction monitoring
+  - Pattern-based detection
+  - Identity verification
+  - Prioritized alert generation
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Transaction Analysis" icon="magnifying-glass">
+    Analyze transactions for risk levels and suspicious patterns.
+  </Card>
+  <Card title="Pattern Detection" icon="chart-line">
+    Identify known fraud patterns and anomalies.
+  </Card>
+  <Card title="Identity Verification" icon="user-check">
+    Verify transaction identities for high-risk cases.
+  </Card>
+  <Card title="Alert Management" icon="bell">
+    Generate and prioritize fraud alerts.
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Prompt Chaining" icon="link" href="/features/promptchaining">
+    Learn about sequential prompt execution
+  </Card>
+  <Card title="Evaluator Optimizer" icon="magnifying-glass-chart" href="/features/evaluator-optimiser">
+    Explore optimization techniques
+  </Card>
+</CardGroup>