fix: update LLM model in Ollama sequential agent from qwen3 to llama3.2 for improved performance

Author: MervinPraison

src/praisonai-agents/agentic_parallelisation.py

@@ -0,0 +1,105 @@
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Example tools (replace with real implementations as needed)
+def fetch_favorite_article():
+    # Simulate fetching your favorite morning article
+    return "Your favorite morning article: 'How to Start Your Day Right'"
+
+def search_trending_kenya():
+    # Simulate searching for trending news in Kenya
+    return "Trending in Kenya: 'Kenya launches new tech hub in Nairobi'"
+
+def fetch_twitter_feed():
+    # Simulate fetching Twitter feed
+    return "Latest tweet: 'AI is transforming the world!'"
+
+# Agents for each unique task
+article_agent = Agent(
+    name="ArticleAgent",
+    role="Morning Article Fetcher",
+    goal="Fetch the user's favorite morning article",
+    tools=[fetch_favorite_article],
+    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+    verbose=True
+)
+
+news_agent = Agent(
+    name="KenyaNewsAgent",
+    role="Kenya News Searcher",
+    goal="Search for trending news in Kenya",
+    tools=[search_trending_kenya],
+    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+    verbose=True
+)
+
+twitter_agent = Agent(
+    name="TwitterAgent",
+    role="Twitter Feed Fetcher",
+    goal="Fetch the latest Twitter feed",
+    tools=[fetch_twitter_feed],
+    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+    verbose=True
+)
+
+aggregator = Agent(
+    name="Aggregator",
+    role="Result Aggregator",
+    goal="Aggregate and summarize all results",
+    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+    verbose=True
+)
+
+# Tasks for each agent
+article_task = Task(
+    name="fetch_article",
+    description="Fetch the user's favorite morning article.",
+    expected_output="The favorite morning article.",
+    agent=article_agent,
+    is_start=True,
+    async_execution=True
+)
+
+news_task = Task(
+    name="search_kenya_news",
+    description="Search for trending news in Kenya.",
+    expected_output="Trending news in Kenya.",
+    agent=news_agent,
+    is_start=True,
+    async_execution=True
+)
+
+twitter_task = Task(
+    name="fetch_twitter",
+    description="Fetch the latest Twitter feed.",
+    expected_output="Latest Twitter feed.",
+    agent=twitter_agent,
+    is_start=True,
+    async_execution=True
+)
+
+# Aggregator task that depends on the above tasks
+aggregate_task = Task(
+    name="aggregate_results",
+    description="Summarize the article, news, and Twitter feed results.",
+    expected_output="A summary of all fetched information.",
+    agent=aggregator,
+    context=[article_task, news_task, twitter_task]
+)
+
+async def main():
+    workflow = PraisonAIAgents(
+        agents=[article_agent, news_agent, twitter_agent, aggregator],
+        tasks=[article_task, news_task, twitter_task, aggregate_task],
+        process="workflow",
+        verbose=True
+    )
+    results = await workflow.astart()
+
+    print("\nParallel Processing Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/praisonai-agents/ollama-sequential.py

@@ -22,7 +22,7 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="ollama/qwen3",
+    llm="ollama/llama3.2",
     tools=[get_stock_price, multiply]
 )
 

src/praisonai-agents/praisonai-gemini.py

@@ -0,0 +1,25 @@
+from praisonai import Agent
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    Args:
+        company_name (str): The name of the company
+    Returns:
+        str: The stock price of the company
+    """
+    return f"The stock price of {company_name} is 100"
+
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers
+    """
+    return a * b
+
+agent = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
+    llm="gemini/gemini-2.5-pro",
+    tools=[get_stock_price, multiply]
+)
+
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")

src/praisonai-agents/praisonai-openai.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    
+    Args:
+        company_name (str): The name of the company
+        
+    Returns:
+        str: The stock price of the company
+    """
+    return f"The stock price of {company_name} is 100"
+
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers
+    """
+    return a * b
+
+
+
+agent = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
+    llm="gpt-4o",
+    tools=[get_stock_price, multiply]
+)
+
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
+print(result)