Update all provider examples to use new clean format with praisonaiagents.Agent

Author: Dhivya-Bharathy

examples/python/providers/anthropic/anthropic_claude_example.py

@@ -2,46 +2,30 @@
 Basic example of using Anthropic Claude with PraisonAI
 """
 
-from praisonai import PraisonAI
+from praisonaiagents import Agent
 
-def main():
-    # Initialize PraisonAI with Anthropic Claude
-    praison = PraisonAI(
-        model="claude-3-5-sonnet-20241022",
-        provider="anthropic",
-        api_key="your-anthropic-api-key-here"  # Replace with your actual Anthropic API key
-    )
-    
-    # Create a simple agent
-    agent = praison.create_agent(
-        name="Anthropic Claude Agent",
-        description="A basic agent using Anthropic Claude model"
-    )
-    
-    # Example conversation
-    response = agent.run("Hello! Can you help me with a writing task?")
-    print("Agent Response:", response)
-    
-    # Example with creative writing
-    writing_task = """
-    Write a short story about a time traveler who discovers 
-    they can only travel to moments of great historical significance.
-    Make it engaging and about 200 words.
-    """
-    
-    response = agent.run(writing_task)
-    print("\nCreative Writing Response:")
-    print(response)
-    
-    # Example with reasoning
-    reasoning_task = """
-    Explain the concept of quantum entanglement in simple terms,
-    and then discuss its potential applications in quantum computing.
-    """
-    
-    response = agent.run(reasoning_task)
-    print("\nReasoning Task Response:")
-    print(response)
+# Initialize Agent with Anthropic Claude
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="anthropic/claude-3-5-sonnet-20241022",
+)
 
-if __name__ == "__main__":
-    main() 
+# Example conversation
+response = agent.start("Hello! Can you help me with a writing task?")
+
+# Example with creative writing
+writing_task = """
+Write a short story about a time traveler who discovers 
+they can only travel to moments of great historical significance.
+Make it engaging and about 200 words.
+"""
+
+response = agent.start(writing_task)
+
+# Example with reasoning
+reasoning_task = """
+Explain the concept of quantum entanglement in simple terms,
+and then discuss its potential applications in quantum computing.
+"""
+
+response = agent.start(reasoning_task) 

examples/python/providers/cohere/cohere_example.py

@@ -2,51 +2,35 @@
 Basic example of using Cohere with PraisonAI
 """
 
-from praisonai import PraisonAI
-
-def main():
-    # Initialize PraisonAI with Cohere
-    praison = PraisonAI(
-        model="command-r-plus",
-        provider="cohere",
-        api_key="your-cohere-api-key-here"  # Replace with your actual Cohere API key
-    )
-    
-    # Create a simple agent
-    agent = praison.create_agent(
-        name="Cohere Agent",
-        description="A basic agent using Cohere model"
-    )
-    
-    # Example conversation
-    response = agent.run("Hello! Can you help me with a business analysis task?")
-    print("Agent Response:", response)
-    
-    # Example with business analysis
-    business_task = """
-    Analyze the potential market opportunities for a new AI-powered 
-    productivity tool targeting remote workers. Include market size, 
-    competitive landscape, and go-to-market strategy recommendations.
-    """
-    
-    response = agent.run(business_task)
-    print("\nBusiness Analysis Response:")
-    print(response)
-    
-    # Example with document summarization
-    summary_task = """
-    Summarize the key points from this business proposal:
-    
-    Our company proposes to develop an AI-powered customer service chatbot
-    that can handle 80% of common customer inquiries automatically. The system
-    will integrate with existing CRM platforms and provide 24/7 support.
-    Expected ROI is 300% within the first year, with implementation taking
-    6 months and requiring a team of 5 developers.
-    """
-    
-    response = agent.run(summary_task)
-    print("\nDocument Summarization Response:")
-    print(response)
-
-if __name__ == "__main__":
-    main() 
+from praisonaiagents import Agent
+
+# Initialize Agent with Cohere
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="cohere/command-r-plus",
+)
+
+# Example conversation
+response = agent.start("Hello! Can you help me with a business analysis task?")
+
+# Example with business analysis
+business_task = """
+Analyze the potential market opportunities for a new AI-powered 
+productivity tool targeting remote workers. Include market size, 
+competitive landscape, and go-to-market strategy recommendations.
+"""
+
+response = agent.start(business_task)
+
+# Example with document summarization
+summary_task = """
+Summarize the key points from this business proposal:
+
+Our company proposes to develop an AI-powered customer service chatbot
+that can handle 80% of common customer inquiries automatically. The system
+will integrate with existing CRM platforms and provide 24/7 support.
+Expected ROI is 300% within the first year, with implementation taking
+6 months and requiring a team of 5 developers.
+"""
+
+response = agent.start(summary_task) 

examples/python/providers/deepseek/deepseek_example.py

@@ -2,52 +2,36 @@
 Basic example of using DeepSeek with PraisonAI
 """
 
-from praisonai import PraisonAI
-
-def main():
-    # Initialize PraisonAI with DeepSeek
-    praison = PraisonAI(
-        model="deepseek-chat",
-        provider="deepseek",
-        api_key="your-deepseek-api-key-here"  # Replace with your actual DeepSeek API key
-    )
-    
-    # Create a simple agent
-    agent = praison.create_agent(
-        name="DeepSeek Agent",
-        description="A basic agent using DeepSeek model"
-    )
-    
-    # Example conversation
-    response = agent.run("Hello! Can you help me with a mathematical problem?")
-    print("Agent Response:", response)
-    
-    # Example with mathematical reasoning
-    math_task = """
-    Solve this calculus problem step by step:
-    Find the derivative of f(x) = x^3 * e^(2x) using the product rule.
-    """
-    
-    response = agent.run(math_task)
-    print("\nMathematical Reasoning Response:")
-    print(response)
-    
-    # Example with code optimization
-    code_task = """
-    Optimize this Python function for better performance:
-    
-    def find_duplicates(arr):
-        duplicates = []
-        for i in range(len(arr)):
-            for j in range(i+1, len(arr)):
-                if arr[i] == arr[j] and arr[i] not in duplicates:
-                    duplicates.append(arr[i])
-        return duplicates
-    """
-    
-    response = agent.run(code_task)
-    print("\nCode Optimization Response:")
-    print(response)
-
-if __name__ == "__main__":
-    main() 
+from praisonaiagents import Agent
+
+# Initialize Agent with DeepSeek
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="deepseek/deepseek-chat",
+)
+
+# Example conversation
+response = agent.start("Hello! Can you help me with a mathematical problem?")
+
+# Example with mathematical reasoning
+math_task = """
+Solve this calculus problem step by step:
+Find the derivative of f(x) = x^3 * e^(2x) using the product rule.
+"""
+
+response = agent.start(math_task)
+
+# Example with code optimization
+code_task = """
+Optimize this Python function for better performance:
+
+def find_duplicates(arr):
+    duplicates = []
+    for i in range(len(arr)):
+        for j in range(i+1, len(arr)):
+            if arr[i] == arr[j] and arr[i] not in duplicates:
+                duplicates.append(arr[i])
+    return duplicates
+"""
+
+response = agent.start(code_task) 

examples/python/providers/google/google_gemini_example.py

@@ -2,45 +2,29 @@
 Basic example of using Google Gemini with PraisonAI
 """
 
-from praisonai import PraisonAI
+from praisonaiagents import Agent
 
-def main():
-    # Initialize PraisonAI with Google Gemini
-    praison = PraisonAI(
-        model="gemini-1.5-pro",
-        provider="google",
-        api_key="your-google-api-key-here"  # Replace with your actual Google API key
-    )
-    
-    # Create a simple agent
-    agent = praison.create_agent(
-        name="Google Gemini Agent",
-        description="A basic agent using Google Gemini model"
-    )
-    
-    # Example conversation
-    response = agent.run("Hello! Can you help me with a research task?")
-    print("Agent Response:", response)
-    
-    # Example with research and analysis
-    research_task = """
-    Research and provide insights on the latest developments in 
-    renewable energy technology, focusing on solar and wind power innovations.
-    """
-    
-    response = agent.run(research_task)
-    print("\nResearch Task Response:")
-    print(response)
-    
-    # Example with multimodal capabilities (text-based for now)
-    multimodal_task = """
-    Describe how you would analyze an image of a city skyline 
-    and provide insights about urban development patterns.
-    """
-    
-    response = agent.run(multimodal_task)
-    print("\nMultimodal Analysis Response:")
-    print(response)
+# Initialize Agent with Google Gemini
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="google/gemini-1.5-pro",
+)
 
-if __name__ == "__main__":
-    main() 
+# Example conversation
+response = agent.start("Hello! Can you help me with a research task?")
+
+# Example with research and analysis
+research_task = """
+Research and provide insights on the latest developments in 
+renewable energy technology, focusing on solar and wind power innovations.
+"""
+
+response = agent.start(research_task)
+
+# Example with multimodal capabilities (text-based for now)
+multimodal_task = """
+Describe how you would analyze an image of a city skyline 
+and provide insights about urban development patterns.
+"""
+
+response = agent.start(multimodal_task) 

examples/python/providers/groq/kimi_with_groq_example.py

@@ -2,35 +2,21 @@
 Basic example of using Kimi model with Groq provider in PraisonAI
 """
 
-from praisonai import PraisonAI
+from praisonaiagents import Agent
 
-def main():
-    # Initialize PraisonAI with Kimi model using Groq provider
-    praison = PraisonAI(
-        model="kimi",
-        provider="groq",
-        api_key="your-groq-api-key-here"  # Replace with your actual Groq API key
-    )
-    
-    # Create a simple agent
-    agent = praison.create_agent(
-        name="Kimi Groq Agent",
-        description="A basic agent using Kimi model with Groq provider"
-    )
-    
-    # Example conversation
-    response = agent.run("Hello! Can you help me with a coding task?")
-    print("Agent Response:", response)
-    
-    # Example with more complex task
-    coding_task = """
-    Write a Python function that calculates the factorial of a number.
-    Include error handling and documentation.
-    """
-    
-    response = agent.run(coding_task)
-    print("\nCoding Task Response:")
-    print(response)
+# Initialize Agent with Kimi model using Groq provider
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="groq/kimi",
+)
 
-if __name__ == "__main__":
-    main() 
+# Example conversation
+response = agent.start("Hello! Can you help me with a coding task?")
+
+# Example with more complex task
+coding_task = """
+Write a Python function that calculates the factorial of a number.
+Include error handling and documentation.
+"""
+
+response = agent.start(coding_task)