fix(flo-ai): adding pre-commit

flo_ai/examples/agent_builder_usage.py

@@ -18,7 +18,15 @@ async def example_simple_agent():
         .build()
     )
 
-    response = await agent.run([UserMessage(TextMessageContent(type='text', text='What is the formula for the area of a circle?'))])
+    response = await agent.run(
+        [
+            UserMessage(
+                TextMessageContent(
+                    type='text', text='What is the formula for the area of a circle?'
+                )
+            )
+        ]
+    )
     print(f'Simple Agent Response: {response}')
 
 
@@ -68,10 +76,14 @@ async def calculate(operation: str, x: float, y: float) -> float:
         .build()
     )
 
-    response = await agent_openai.run([UserMessage(TextMessageContent(type='text', text='Calculate 5 plus 3'))])
+    response = await agent_openai.run(
+        [UserMessage(TextMessageContent(type='text', text='Calculate 5 plus 3'))]
+    )
     print(f'OpenAI Tool Agent Response: {response}')
 
-    response = await agent_claude.run([UserMessage(TextMessageContent(type='text', text='Calculate 5 plus 3'))])
+    response = await agent_claude.run(
+        [UserMessage(TextMessageContent(type='text', text='Calculate 5 plus 3'))]
+    )
     print(f'Claude Tool Agent Response: {response}')
 
 

flo_ai/examples/arium_examples.py

@@ -11,24 +11,33 @@
 from flo_ai.tool.base_tool import Tool
 from flo_ai.arium.memory import MessageMemory
 
+
 @flo_tool(
-    description="Calculate mathematical expressions",
+    description='Calculate mathematical expressions',
     parameter_descriptions={
-        "result": "The result to print",
-
-    }
+        'result': 'The result to print',
+    },
 )
 async def print_result(result: str) -> str:
-    print(f"Result: {result}")
+    print(f'Result: {result}')
     return result
-
+
+
 # Example 1: Simple Linear Workflow
 async def example_linear_workflow():
     """Example of a simple linear workflow: Agent -> Tool -> Agent"""
 
     # Create some example agents and tools (these would be your actual implementations)
-    analyzer_agent = Agent(name='analyzer', system_prompt='Analyze the input', llm=OpenAI(model='gpt-4o-mini'))
-    summarizer_agent = Agent(name='summarizer', system_prompt='Summarize the results', llm=OpenAI(model='gpt-4o-mini'))
+    analyzer_agent = Agent(
+        name='analyzer',
+        system_prompt='Analyze the input',
+        llm=OpenAI(model='gpt-4o-mini'),
+    )
+    summarizer_agent = Agent(
+        name='summarizer',
+        system_prompt='Summarize the results',
+        llm=OpenAI(model='gpt-4o-mini'),
+    )
 
     # Build and run the workflow
     result = await (
@@ -40,7 +49,9 @@ async def example_linear_workflow():
         .connect(analyzer_agent, print_result.tool)
         .connect(print_result.tool, summarizer_agent)
         .end_with(summarizer_agent)
-        .build_and_run([UserMessage(TextMessageContent(type='text', text='Analyze this text'))])
+        .build_and_run(
+            [UserMessage(TextMessageContent(type='text', text='Analyze this text'))]
+        )
     )
 
     return result
@@ -132,14 +143,10 @@ async def example_convenience_function():
     """Example using the create_arium convenience function"""
 
     agent1 = Agent(
-        name='agent1', 
-        system_prompt='First agent', 
-        llm=OpenAI(model='gpt-4o-mini')
+        name='agent1', system_prompt='First agent', llm=OpenAI(model='gpt-4o-mini')
     )
     agent2 = Agent(
-        name='agent2', 
-        system_prompt='Second agent',
-        llm=OpenAI(model='gpt-4o-mini')
+        name='agent2', system_prompt='Second agent', llm=OpenAI(model='gpt-4o-mini')
     )
 
     # Fix: Use proper InputMessage format for consistency

flo_ai/examples/arium_yaml_example.py

@@ -321,16 +321,21 @@ async def run_simple_example():
     # Run the workflow
     result = await builder.build_and_run(
         [
-            UserMessage(TextMessageContent(type='text', text='Machine learning is transforming healthcare by enabling predictive analytics, '
-            'personalized treatment recommendations, and automated medical imaging analysis. '
-            'However, challenges include data privacy concerns, the need for regulatory approval, '
-            'and ensuring AI systems are transparent and unbiased in their decision-making.')),
+            UserMessage(
+                TextMessageContent(
+                    type='text',
+                    text='Machine learning is transforming healthcare by enabling predictive analytics, '
+                    'personalized treatment recommendations, and automated medical imaging analysis. '
+                    'However, challenges include data privacy concerns, the need for regulatory approval, '
+                    'and ensuring AI systems are transparent and unbiased in their decision-making.',
+                )
+            ),
         ]
     )
 
     print('Result:')
     for i, message in enumerate(result):
-      print(f'{i+1}. {message}')
+        print(f'{i+1}. {message}')
 
     return result
 
@@ -371,8 +376,18 @@ async def run_complex_example():
 
     result2 = await builder.build_and_run(
         [
-            UserMessage(TextMessageContent(type='text', text="Please analyze this text and process it: 'The quick brown fox jumps over the lazy dog. ")),
-            UserMessage(TextMessageContent(type='text', text="This sentence contains every letter of the alphabet at least once.'")),
+            UserMessage(
+                TextMessageContent(
+                    type='text',
+                    text="Please analyze this text and process it: 'The quick brown fox jumps over the lazy dog. ",
+                )
+            ),
+            UserMessage(
+                TextMessageContent(
+                    type='text',
+                    text="This sentence contains every letter of the alphabet at least once.'",
+                )
+            ),
         ]
     )
 
@@ -395,10 +410,15 @@ async def run_mixed_config_example():
     # Run the workflow
     result = await builder.build_and_run(
         [
-            UserMessage(TextMessageContent(type='text', text='Please analyze this business report: Our Q3 revenue increased by 15% compared to Q2, '
-            'driven primarily by strong performance in the software division. However, hardware sales '
-            'declined by 8%. Customer satisfaction scores improved to 4.2/5.0. We recommend focusing '
-            'on digital transformation initiatives and reconsidering the hardware product line.')),
+            UserMessage(
+                TextMessageContent(
+                    type='text',
+                    text='Please analyze this business report: Our Q3 revenue increased by 15% compared to Q2, '
+                    'driven primarily by strong performance in the software division. However, hardware sales '
+                    'declined by 8%. Customer satisfaction scores improved to 4.2/5.0. We recommend focusing '
+                    'on digital transformation initiatives and reconsidering the hardware product line.',
+                )
+            ),
         ]
     )
 
@@ -449,7 +469,7 @@ async def run_prebuilt_agents_example():
         .with_name('summarizer')
         .with_role('Executive Summarizer')
         .with_prompt(
-           'Create a concise executive summary from the content analysis. Focus on actionable insights and key recommendations.'
+            'Create a concise executive summary from the content analysis. Focus on actionable insights and key recommendations.'
         )
         .with_llm(llm)
         .with_reasoning(ReasoningPattern.DIRECT)

flo_ai/examples/chat_history.py

@@ -1,9 +1,10 @@
 import asyncio
 from typing import Any
 from flo_ai.builder.agent_builder import AgentBuilder
-from flo_ai.llm import  Gemini
+from flo_ai.llm import Gemini
 from flo_ai.models.agent import Agent
-from flo_ai.models import   AssistantMessage, UserMessage, TextMessageContent
+from flo_ai.models import AssistantMessage, UserMessage, TextMessageContent
+
 
 async def main() -> None:
     # Create a simple conversational agent
@@ -16,28 +17,38 @@ async def main() -> None:
     )
 
     response: Any = await agent.run(
-        [   
+        [
             UserMessage(
-                TextMessageContent(type='text', text='What is the formula for the area of a circle?'),
+                TextMessageContent(
+                    type='text', text='What is the formula for the area of a circle?'
+                ),
             ),
             AssistantMessage(
-               TextMessageContent(type='text', text='The formula for the area of a circle is πr^2.'),
+                TextMessageContent(
+                    type='text', text='The formula for the area of a circle is πr^2.'
+                ),
             ),
             UserMessage(
-                TextMessageContent(type='text', text='What is the formula for the area of a rectangle?')
+                TextMessageContent(
+                    type='text', text='What is the formula for the area of a rectangle?'
+                )
             ),
             AssistantMessage(
-               TextMessageContent(type='text', text='The formula for the area of a rectangle is length * width.'),
+                TextMessageContent(
+                    type='text',
+                    text='The formula for the area of a rectangle is length * width.',
+                ),
             ),
-
-           UserMessage(
-                TextMessageContent(type='text', text='What is the area of a rectable of length <length> and breadth <breadth>'),
+            UserMessage(
+                TextMessageContent(
+                    type='text',
+                    text='What is the area of a rectable of length <length> and breadth <breadth>',
+                ),
             ),
         ],
-        variables={
-            "length":10,
-            "breadth":70
-        },
+        variables={'length': 10, 'breadth': 70},
     )
     print(f'Response: {response}')
+
+
 asyncio.run(main())

flo_ai/examples/cot_agent_example.py

@@ -77,8 +77,17 @@ async def main():
     # Test questions
     questions = [
         UserMessage(TextMessageContent(type='text', text='What is 15 + 27?')),
-        UserMessage(TextMessageContent(type='text', text='If I have 100 apples and I give away 23, then buy 15 more, how many do I have?')),
-        UserMessage(TextMessageContent(type='text', text='Calculate 8 * 7 and then add 12 to the result.')),
+        UserMessage(
+            TextMessageContent(
+                type='text',
+                text='If I have 100 apples and I give away 23, then buy 15 more, how many do I have?',
+            )
+        ),
+        UserMessage(
+            TextMessageContent(
+                type='text', text='Calculate 8 * 7 and then add 12 to the result.'
+            )
+        ),
     ]
 
     print('=== Chain of Thought (CoT) Reasoning Demo ===\n')

flo_ai/examples/cot_conversational_example.py

@@ -33,9 +33,24 @@ async def main():
 
     # Test questions that require step-by-step reasoning
     questions = [
-        UserMessage(TextMessageContent(type='text', text='If a train leaves station A at 2 PM traveling 60 mph and another train leaves station B at 3 PM traveling 80 mph, and the stations are 300 miles apart, when will they meet?')),
-        UserMessage(TextMessageContent(type='text', text='A store has a 20% discount on all items. If a customer buys 3 items that originally cost $50, $30, and $20, what is the final total after the discount?')),
-        UserMessage(TextMessageContent(type='text', text='Explain why the sky appears blue during the day but red during sunset.')),
+        UserMessage(
+            TextMessageContent(
+                type='text',
+                text='If a train leaves station A at 2 PM traveling 60 mph and another train leaves station B at 3 PM traveling 80 mph, and the stations are 300 miles apart, when will they meet?',
+            )
+        ),
+        UserMessage(
+            TextMessageContent(
+                type='text',
+                text='A store has a 20% discount on all items. If a customer buys 3 items that originally cost $50, $30, and $20, what is the final total after the discount?',
+            )
+        ),
+        UserMessage(
+            TextMessageContent(
+                type='text',
+                text='Explain why the sky appears blue during the day but red during sunset.',
+            )
+        ),
     ]
 
     print('=== Conversational Chain of Thought (CoT) Reasoning Demo ===\n')

flo_ai/examples/custom_plan_execute_demo.py

@@ -99,7 +99,12 @@ async def main():
     )
 
     # Execute task
-    task = UserMessage(TextMessageContent(type='text', text='Research the impact of AI on software development productivity'))
+    task = UserMessage(
+        TextMessageContent(
+            type='text',
+            text='Research the impact of AI on software development productivity',
+        )
+    )
     print(f'📋 Task: {task}')
     print('🔄 Executing custom research workflow...\n')