chore: remove formatted files outside of scope

chore: remove formatted files outside of scope

cookbook/agent_concepts/hybrid_search/lancedb/agent.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import typer
 from agno.agent import Agent
 from agno.knowledge.pdf_url import PDFUrlKnowledgeBase

cookbook/agent_concepts/hybrid_search/pinecone/agent.py

@@ -1,4 +1,5 @@
 import os
+from typing import Optional
 
 import nltk  # type: ignore
 import typer

cookbook/agent_concepts/multimodal/audio_input_output.py

@@ -1,3 +1,5 @@
+import base64
+
 import requests
 from agno.agent import Agent
 from agno.media import Audio

cookbook/examples/apps/agentic_rag/utils.py

@@ -66,7 +66,7 @@ def display_tool_calls(tool_calls_container, tools):
                     st.markdown("**Results:**")
                     try:
                         st.json(_content)
-                    except Exception:
+                    except Exception as e:
                         st.markdown(_content)
 
                 if _metrics:

cookbook/examples/apps/answer_engine/utils.py

@@ -74,7 +74,7 @@ def display_tool_calls(tool_calls_container, tools):
                         st.markdown("**Results:**")
                         try:
                             st.json(content)
-                        except Exception:
+                        except Exception as e:
                             st.markdown(content)
 
     except Exception as e:

cookbook/examples/apps/chess_team/chess_board.py

@@ -56,7 +56,7 @@ def get_board_state(self) -> str:
 
         for i, row in enumerate(self.board):
             # Add rank number and row
-            html_output.append('<div class="chess-row">')
+            html_output.append(f'<div class="chess-row">')
             html_output.append(f'<div class="chess-rank">{8 - i}</div>')
 
             for piece in row:

cookbook/examples/apps/llm_os/app.py

@@ -183,7 +183,7 @@ def main() -> None:
             st.session_state["llm_os_run_id"] = None
         else:
             st.session_state["llm_os_run_id"] = llm_os.new_session()
-    except Exception:
+    except Exception as e:
         st.session_state["llm_os_run_id"] = None
 
     # Modify the chat history loading to work without storage

cookbook/examples/apps/parallel_world_builder/app.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import streamlit as st
 from agents import World, get_world_builder
 from agno.agent import Agent

cookbook/examples/apps/parallel_world_builder/utils.py

@@ -91,7 +91,7 @@ def display_tool_calls(tool_calls_container, tools):
                     st.markdown("**Results:**")
                     try:
                         st.json(_content)
-                    except Exception:
+                    except Exception as e:
                         st.markdown(_content)
 
                 if _metrics:

cookbook/examples/apps/sql_agent/agents.py

@@ -178,7 +178,7 @@ def get_sql_agent(
         - Race strategy evaluation
 
         You combine deep F1 knowledge with advanced SQL expertise to uncover insights from decades of racing data."""),
-        instructions=dedent("""\
+        instructions=dedent(f"""\
         You are a SQL expert focused on writing precise, efficient queries.
 
         When a user messages you, determine if you need query the database or can respond directly.

cookbook/examples/apps/sql_agent/utils.py

@@ -81,7 +81,7 @@ def display_tool_calls(tool_calls_container, tools):
                     st.markdown("**Results:**")
                     try:
                         st.json(_content)
-                    except Exception:
+                    except Exception as e:
                         st.markdown(_content)
 
                 if _metrics:

cookbook/models/anthropic/memory.py

@@ -10,6 +10,7 @@
 from agno.memory.db.postgres import PgMemoryDb
 from agno.models.anthropic import Claude
 from agno.storage.agent.postgres import PostgresAgentStorage
+from rich.pretty import pprint
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 agent = Agent(

cookbook/models/huggingface/llama_essay_writer.py

@@ -1,3 +1,6 @@
+import os
+from getpass import getpass
+
 from agno.agent import Agent
 from agno.models.huggingface import HuggingFace
 

cookbook/models/mistral/basic_stream.py

@@ -1,3 +1,5 @@
+import os
+
 from agno.agent import Agent, RunResponse  # noqa
 from agno.models.mistral import MistralChat
 

cookbook/models/mistral/structured_output.py

@@ -1,3 +1,4 @@
+import os
 from typing import List
 
 from agno.agent import Agent, RunResponse  # noqa

cookbook/models/nvidia/tool_use.py

@@ -1,5 +1,7 @@
 """Run `pip install duckduckgo-search` to install dependencies."""
 
+import os
+
 from agno.agent import Agent
 from agno.models.nvidia import Nvidia
 from agno.tools.duckduckgo import DuckDuckGoTools

cookbook/models/ollama/demo_phi4.py

@@ -1,5 +1,6 @@
 from agno.agent import Agent, RunResponse  # noqa
 from agno.models.ollama import Ollama
+from agno.tools.duckduckgo import DuckDuckGoTools
 
 agent = Agent(model=Ollama(id="phi4"), markdown=True)
 

cookbook/models/openai/audio_input_agent.py

@@ -1,3 +1,5 @@
+import base64
+
 import requests
 from agno.agent import Agent, RunResponse  # noqa
 from agno.media import Audio

cookbook/models/openai/audio_input_output_output.py

@@ -1,3 +1,5 @@
+import base64
+
 import requests
 from agno.agent import Agent
 from agno.media import Audio

evals/performance/instantiation.py

@@ -3,10 +3,8 @@
 from agno.agent import Agent
 from agno.eval.perf import PerfEval
 
-
 def instantiate_agent():
-    return Agent(system_message="Be concise, reply with one sentence.")
-
+    return Agent(system_message='Be concise, reply with one sentence.')
 
 instantiation_perf = PerfEval(func=instantiate_agent, num_iterations=1000)
 

evals/performance/instantiation_with_tool.py

@@ -6,7 +6,6 @@
 from agno.models.openai import OpenAIChat
 from agno.eval.perf import PerfEval
 
-
 def get_weather(city: Literal["nyc", "sf"]):
     """Use this to get weather information."""
     if city == "nyc":
@@ -16,13 +15,10 @@ def get_weather(city: Literal["nyc", "sf"]):
     else:
         raise AssertionError("Unknown city")
 
-
 tools = [get_weather]
 
-
 def instantiate_agent():
-    return Agent(model=OpenAIChat(id="gpt-4o"), tools=tools)
-
+    return Agent(model=OpenAIChat(id='gpt-4o'), tools=tools)
 
 instantiation_perf = PerfEval(func=instantiate_agent, num_iterations=1000)
 

evals/performance/other/crewai_instantiation.py

@@ -1,5 +1,4 @@
 """Run `pip install openai memory_profiler crewai crewai[tools]` to install dependencies."""
-
 from typing import Literal
 
 from crewai.agent import Agent
@@ -17,19 +16,14 @@ def get_weather(city: Literal["nyc", "sf"]):
     else:
         raise AssertionError("Unknown city")
 
-
 tools = [get_weather]
 
-
 def instantiate_agent():
-    return Agent(
-        llm="gpt-4o",
-        role="Test Agent",
-        goal="Be concise, reply with one sentence.",
-        tools=tools,
-        backstory="Test",
-    )
-
+    return Agent(llm='gpt-4o',
+                 role='Test Agent',
+                 goal='Be concise, reply with one sentence.',
+                 tools=tools,
+                 backstory='Test')
 
 crew_instantiation = PerfEval(func=instantiate_agent, num_iterations=1000)
 

evals/performance/other/langgraph_instantiation.py

@@ -8,7 +8,6 @@
 
 from agno.eval.perf import PerfEval
 
-
 @tool
 def get_weather(city: Literal["nyc", "sf"]):
     """Use this to get weather information."""
@@ -19,14 +18,11 @@ def get_weather(city: Literal["nyc", "sf"]):
     else:
         raise AssertionError("Unknown city")
 
-
 tools = [get_weather]
 
-
 def instantiate_agent():
     return create_react_agent(model=ChatOpenAI(model="gpt-4o"), tools=tools)
 
-
 langgraph_instantiation = PerfEval(func=instantiate_agent, num_iterations=1000)
 
 if __name__ == "__main__":

evals/performance/other/pydantic_ai_instantiation.py

@@ -1,13 +1,12 @@
 """Run `pip install openai pydantic-ai` to install dependencies."""
-
 from typing import Literal
 
 from pydantic_ai import Agent
 from agno.eval.perf import PerfEval
 
 
 def instantiate_agent():
-    agent = Agent("openai:gpt-4o", system_prompt="Be concise, reply with one sentence.")
+    agent =  Agent('openai:gpt-4o', system_prompt='Be concise, reply with one sentence.')
 
     @agent.tool_plain
     def get_weather(city: Literal["nyc", "sf"]):
@@ -21,7 +20,6 @@ def get_weather(city: Literal["nyc", "sf"]):
 
     return agent
 
-
 pydantic_instantiation = PerfEval(func=instantiate_agent, num_iterations=1000)
 
 if __name__ == "__main__":

evals/performance/other/smolagents_instantiation.py

@@ -1,4 +1,5 @@
 """Run `pip install memory_profiler smolagents` to install dependencies."""
+from typing import Literal
 
 from agno.eval.perf import PerfEval
 from smolagents import ToolCallingAgent, HfApiModel, Tool
@@ -26,12 +27,9 @@ def forward(self, city: str):
             raise AssertionError("Unknown city")
 
 
-def instantiate_agent():
-    return ToolCallingAgent(
-        tools=[WeatherTool()],
-        model=HfApiModel(model_id="meta-llama/Llama-3.3-70B-Instruct"),
-    )
 
+def instantiate_agent():
+    return ToolCallingAgent(tools=[WeatherTool()], model=HfApiModel(model_id="meta-llama/Llama-3.3-70B-Instruct"))
 
 smolagents_instantiation = PerfEval(func=instantiate_agent, num_iterations=1000)
 

evals/performance/simple_response.py

@@ -4,17 +4,12 @@
 from agno.models.openai import OpenAIChat
 from agno.eval.perf import PerfEval
 
-
 def simple_response():
-    agent = Agent(
-        model=OpenAIChat(id="gpt-4o-mini"),
-        system_message="Be concise, reply with one sentence.",
-    )
-    response = agent.run("What is the capital of France?")
+    agent = Agent(model=OpenAIChat(id='gpt-4o-mini'), system_message='Be concise, reply with one sentence.')
+    response = agent.run('What is the capital of France?')
     print(response.content)
     return response
 
-
 simple_response_perf = PerfEval(func=simple_response, num_iterations=10)
 
 if __name__ == "__main__":

evals/reliability/multiple_tool_calls/openai/calculator.py

@@ -8,13 +8,12 @@
 
 
 def multiply_and_exponentiate():
-    agent = Agent(
+
+    agent=Agent(
         model=OpenAIChat(id="gpt-4o-mini"),
         tools=[CalculatorTools(add=True, multiply=True, exponentiate=True)],
     )
-    response: RunResponse = agent.run(
-        "What is 10*5 then to the power of 2? do it step by step"
-    )
+    response: RunResponse = agent.run("What is 10*5 then to the power of 2? do it step by step")
     evaluation = ReliabilityEval(
         agent_response=response,
         expected_tool_calls=["multiply", "exponentiate"],

evals/reliability/single_tool_calls/openai/calculator.py

@@ -8,7 +8,8 @@
 
 
 def factorial():
-    agent = Agent(
+
+    agent=Agent(
         model=OpenAIChat(id="gpt-4o-mini"),
         tools=[CalculatorTools(factorial=True)],
     )