Remove deprecated parameters from examples and fix Agent initialization

- Remove `verbose` parameter from Agents() calls across all examples
- Remove `markdown` parameter from Agent() initialization
- Change `model` to `llm` in observability example
- Remove unused `kind` parameter from obs.span()
- Fix thinking_budget initialization to use property setter instead of constructor
- Add missing `instructions` parameter to routellm_example.py Agent

examples/observability/basic_usage.py

@@ -24,13 +24,13 @@ obs.init()
 # Create an agent
 agent = Agent(
     instructions="You are a helpful assistant.",
-    model="gpt-4o-mini",
+    llm="gpt-4o-mini",
 )
 
 # Use trace context manager for complete workflow tracing
 with obs.trace("chat-session", session_id="user-123"):
     # Use span context manager for individual operations
-    with obs.span("user-query", kind=obs.SpanKind.AGENT):
+    with obs.span("user-query"):
         response = agent.chat("What is the capital of France?")
         print(response)
 

examples/python/agents/image-agent.py

@@ -40,7 +40,6 @@ agents = Agents(
     agents=[image_agent],
     tasks=[task1, task2],
     process="sequential",
-    verbose=1
 )
 
 # Run all tasks

examples/python/agents/image-to-text-agent.py

@@ -40,7 +40,6 @@ agents = Agents(
     agents=[image_agent],
     tasks=[task1, task2],
     process="sequential",
-    verbose=1
 )
 
 # Run all tasks

examples/python/agents/video-agent.py

@@ -29,7 +29,6 @@ agents = Agents(
     agents=[video_agent],
     tasks=[task1],
     process="sequential",
-    verbose=1
 )
 
 # Run all tasks

examples/python/camera/camera-basic.py

@@ -62,7 +62,6 @@ def capture_and_analyze():
                 agents=[vision_agent],
                 tasks=[task],
                 process="sequential",
-                verbose=1
             )
             
             result = agents.start()

examples/python/camera/camera-multi-agent.py

@@ -118,7 +118,6 @@ def analyze_with_multiple_agents():
         agents=[security_agent, object_detector, scene_analyst],
         tasks=[security_task, object_task, scene_task],
         process="parallel",  # Run all analyses in parallel
-        verbose=1
     )
     
     result = agents.start()

examples/python/camera/camera-video-analysis.py

@@ -135,7 +135,6 @@ class CameraVideoAnalyzer:
             agents=[self.video_agent],
             tasks=[task],
             process="sequential",
-            verbose=1
         )
         
         return agents.start()

examples/python/general/async_example.py

@@ -99,7 +99,6 @@ sync_agent = Agent(
     backstory="Expert in sync operations and data organization",
     tools=[sync_search_tool],
     reflection=False,
-    markdown=True
 )
 
 async_agent = Agent(
@@ -109,7 +108,6 @@ async_agent = Agent(
     backstory="Expert in async operations and data organization",
     tools=[async_search_tool],
     reflection=False,
-    markdown=True
 )
 
 # 5. Create tasks with different configurations
@@ -161,7 +159,6 @@ def run_sync_example():
     agents = Agents(
         agents=[sync_agent],
         tasks=[sync_task],
-        verbose=1,
         process="sequential"
     )
     result = agents.start()
@@ -173,7 +170,6 @@ async def run_async_example():
     agents = Agents(
         agents=[async_agent],
         tasks=[async_task],
-        verbose=1,
         process="sequential"
     )
     result = await agents.astart()
@@ -185,7 +181,6 @@ async def run_mixed_example():
     agents = Agents(
         agents=[sync_agent, async_agent],
         tasks=[sync_task, async_task],
-        verbose=1,
         process="sequential"
     )
     result = await agents.astart()
@@ -197,7 +192,6 @@ async def run_workflow_example():
     agents = Agents(
         agents=[sync_agent, async_agent],
         tasks=[workflow_sync_task, workflow_async_task],
-        verbose=1,
         process="workflow"
     )
     result = await agents.astart()

examples/python/general/async_example_full.py

@@ -54,7 +54,6 @@ async_agent = Agent(
     backstory="Expert in efficient data retrieval and parallel search operations",
     tools=[async_search_tool],
     reflection=False,
-    markdown=True
 )
 
 summary_agent = Agent(
@@ -63,7 +62,6 @@ summary_agent = Agent(
     goal="Create concise summaries from multiple search results",
     backstory="Expert in analyzing and synthesizing information from multiple sources",
     reflection=True,
-    markdown=True
 )
 
 # 5. Create async tasks
@@ -116,7 +114,6 @@ async def run_parallel_tasks():
     agents = Agents(
         agents=[async_agent, summary_agent],
         tasks=parallel_tasks + [summary_task],
-        verbose=1,
         process="sequential"
     )
     

examples/python/general/async_example_full_multigroups.py

@@ -61,7 +61,6 @@ async_agent = Agent(
     backstory="Expert in parallel search operations and data retrieval",
     tools=[async_search_tool],
     reflection=False,
-    markdown=True
 )
 
 summary_agent = Agent(
@@ -72,7 +71,6 @@ summary_agent = Agent(
 Skilled at identifying patterns, trends, and connections between different topics.
 Specializes in creating clear, structured summaries that highlight key insights.""",
     reflection=True,  # Enable self-reflection for better summary quality
-    markdown=True
 )
 
 # 5. Create async tasks
@@ -104,7 +102,6 @@ async def run_single_task():
     agents = Agents(
         agents=[async_agent],
         tasks=[async_task],
-        verbose=1,
         process="sequential"
     )
     result = await agents.astart()
@@ -188,7 +185,6 @@ Present the summary in a clear, structured format with sections for findings, pa
     agents = Agents(
         agents=[async_agent],
         tasks=parallel_tasks,  # Only run search tasks first
-        verbose=1,
         process="sequential"
     )
     search_results = await agents.astart()
@@ -213,7 +209,6 @@ Present the summary in a clear, structured format with sections for findings, pa
     summary_agents = Agents(
         agents=[summary_agent],  # Use summary agent for synthesis
         tasks=[summary_task],
-        verbose=1,
         process="sequential"
     )
     summary_result = await summary_agents.astart()

examples/python/general/example_callback.py

@@ -127,10 +127,6 @@ def main():
         allow_delegation=False,
         tools=[internet_search_tool],
         llm="gpt-4o-mini",
-        markdown=True,
-        
-        
-        
     )
     
     writer = Agent(
@@ -142,7 +138,6 @@ def main():
         allow_delegation=True,
         llm="gpt-4o-mini",
         tools=[],
-        markdown=True
     )
 
     # Create tasks with callbacks

examples/python/general/example_custom_tools.py

@@ -144,7 +144,6 @@ validate_task = Task(
 agents = Agents(
     agents=[data_agent],
     tasks=[collect_task, validate_task],
-    verbose=1,
     process="workflow"
 )
 

examples/python/general/example_sequential.py

@@ -39,10 +39,6 @@ researcher = Agent(
     allow_delegation=False,
     tools=[internet_search_tool],
     llm="gpt-4o-mini",
-    markdown=True,
-    
-    
-    
 )
 writer = Agent(
     name="Writer",
@@ -53,7 +49,6 @@ writer = Agent(
     allow_delegation=True,
     llm="gpt-4o-mini",
     tools=[],
-    markdown=True
 )
 
 # Create tasks

examples/python/general/multimodal.py

@@ -46,8 +46,7 @@ task3 = Task(
 agents = Agents(
     agents=[vision_agent],
     tasks=[task1, task2, task3],
-    process="sequential",
-    verbose=1
+    process="sequential"
 )
 
 # Run all tasks

examples/python/general/structured_response_example.py

@@ -32,9 +32,7 @@ def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
         role="Research Analyst",
         goal="Analyze topics and provide structured reports",
         backstory="Expert at gathering information and providing structured analysis",
-        verbose=verbose,
         reflection=True,  # Enable self-reflection for better quality
-        markdown=True,
         llm="gpt-4o-mini"
     )
     

examples/python/general/workflow_example_basic.py

@@ -67,7 +67,6 @@ validate_task = Task(
 agents = Agents(
     agents=[data_agent],
     tasks=[collect_task, validate_task],
-    verbose=1,
     process="workflow"
 )
 

examples/python/general/workflow_example_detailed.py

@@ -228,7 +228,6 @@ process_task = Task(
 workflow = Agents(
     agents=[data_agent, process_agent],
     tasks=[collect_task, validate_task, process_task],
-    verbose=1,
     process="workflow"
 )
 

examples/python/memory/graph-memory-agent.py

@@ -152,7 +152,6 @@ def main():
     agents_system = Agents(
         agents=[researcher],
         tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
-        verbose=1,
         memory=True,
         memory_config=memory_config
     )

examples/python/stateful/workflow-state-example.py

@@ -91,7 +91,6 @@ def main():
         memory=True,
         process="workflow",
         user_id="research_project_001",
-        verbose=1
     )
     
     print(f"🤖 Created workflow with {len(workflow.agents)} agents and {len(workflow.tasks)} tasks")

examples/rag/basic_retrieval.py

@@ -51,7 +51,6 @@ def basic_rag_example():
         
         COMPANY DATABASE:
         {context}""",
-        verbose=False
     )
     
     # Test queries

examples/routing/routellm_example.py

@@ -18,6 +18,7 @@ from praisonaiagents import Agent
 
 # Basic usage with RouteLLM routing
 agent = Agent(
+    instructions="You are a helpful assistant.",
     llm="router-mf-0.5",
     base_url="http://localhost:6060/v1"
 )

examples/thinking/basic_thinking.py

@@ -45,8 +45,9 @@ def main():
     agent = Agent(
         name="DeepThinker",
         instructions="You are a problem-solving assistant that thinks step by step.",
-        thinking_budget=ThinkingBudget.high()  # 16,000 tokens for reasoning
     )
+    # Set thinking budget via property setter (not constructor param)
+    agent.thinking_budget = ThinkingBudget.high()  # 16,000 tokens for reasoning
     
     print(f"Agent: {agent.name}")
     print(f"Thinking budget: {agent.thinking_budget.max_tokens:,} tokens")
@@ -67,8 +68,9 @@ def main():
     agent_custom = Agent(
         name="CustomThinker",
         instructions="You solve complex problems.",
-        thinking_budget=custom_budget
     )
+    # Set thinking budget via property setter (not constructor param)
+    agent_custom.thinking_budget = custom_budget
     
     print(f"Agent: {agent_custom.name}")
     print(f"Max tokens: {agent_custom.thinking_budget.max_tokens:,}")