adding docs

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 +1,91 @@
+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/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)