Openai model responses

cookbook/examples/apps/geobuddy/app.py

@@ -2,10 +2,9 @@
 from pathlib import Path
 
 import streamlit as st
+from geography_buddy import analyze_image
 from PIL import Image
 
-from cookbook.use_cases.apps.geobuddy.geography_buddy import analyze_image
-
 # Streamlit App Configuration
 st.set_page_config(
     page_title="Geography Location Buddy",

cookbook/models/openai/responses/async_basic.py

@@ -0,0 +1,13 @@
+import asyncio
+
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIResponses
+
+agent = Agent(model=OpenAIResponses(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run: RunResponse = agent.run("Share a 2 sentence horror story")
+# print(run.content)
+
+# Print the response in the terminal
+asyncio.run(agent.aprint_response("Share a 2 sentence horror story"))

cookbook/models/openai/responses/async_basic_stream.py

@@ -0,0 +1,15 @@
+import asyncio
+from typing import Iterator  # noqa
+
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIResponses
+
+agent = Agent(model=OpenAIResponses(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)
+# for chunk in run_response:
+#     print(chunk.content)
+
+# Print the response in the terminal
+asyncio.run(agent.aprint_response("Share a 2 sentence horror story", stream=True))

cookbook/models/openai/responses/async_tool_use.py

@@ -0,0 +1,15 @@
+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+import asyncio
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+from agno.tools.duckduckgo import DuckDuckGoTools
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[DuckDuckGoTools()],
+    show_tool_calls=True,
+    markdown=True,
+)
+asyncio.run(agent.aprint_response("Whats happening in France?", stream=True))

cookbook/models/openai/responses/basic.py

@@ -0,0 +1,13 @@
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIResponses
+
+agent = Agent(model=OpenAIResponses(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run: RunResponse = agent.run("Share a 2 sentence horror story")
+# print(run.content)
+
+# Print the response in the terminal
+agent.print_response("Share a 2 sentence horror story")
+
+agent.run_response.metrics

cookbook/models/openai/responses/basic_stream.py

@@ -0,0 +1,13 @@
+from typing import Iterator  # noqa
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIResponses
+
+agent = Agent(model=OpenAIResponses(id="gpt-4o"), markdown=True)
+
+# Get the response in a variable
+# run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)
+# for chunk in run_response:
+#     print(chunk.content)
+
+# Print the response in the terminal
+agent.print_response("Share a 2 sentence horror story", stream=True)

cookbook/models/openai/responses/image_agent.py

@@ -0,0 +1,20 @@
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.openai import OpenAIResponses
+from agno.tools.googlesearch import GoogleSearchTools
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[GoogleSearchTools()],
+    markdown=True,
+)
+
+agent.print_response(
+    "Tell me about this image and give me the latest news about it.",
+    images=[
+        Image(
+            url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
+        )
+    ],
+    stream=True,
+)

cookbook/models/openai/responses/image_agent_bytes.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.openai import OpenAIResponses
+from agno.tools.googlesearch import GoogleSearchTools
+from agno.utils.media import download_image
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[GoogleSearchTools()],
+    markdown=True,
+)
+
+image_path = Path(__file__).parent.joinpath("sample.jpg")
+
+download_image(
+    url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
+    output_path=str(image_path),
+)
+
+# Read the image file content as bytes
+image_bytes = image_path.read_bytes()
+
+agent.print_response(
+    "Tell me about this image and give me the latest news about it.",
+    images=[
+        Image(content=image_bytes),
+    ],
+    stream=True,
+)

cookbook/models/openai/responses/image_agent_with_memory.py

@@ -0,0 +1,23 @@
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.openai import OpenAIResponses
+from agno.tools.googlesearch import GoogleSearchTools
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[GoogleSearchTools()],
+    markdown=True,
+    add_history_to_messages=True,
+    num_history_responses=3,
+)
+
+agent.print_response(
+    "Tell me about this image and give me the latest news about it.",
+    images=[
+        Image(
+            url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
+        )
+    ],
+)
+
+agent.print_response("Tell me where I can get more images?")

cookbook/models/openai/responses/knowledge.py

@@ -0,0 +1,21 @@
+"""Run `pip install duckduckgo-search sqlalchemy pgvector pypdf openai` to install dependencies."""
+
+from agno.agent import Agent
+from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+from agno.models.openai import OpenAIResponses
+from agno.vectordb.pgvector import PgVector
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=PgVector(table_name="recipes", db_url=db_url),
+)
+knowledge_base.load(recreate=True)  # Comment out after first run
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    knowledge=knowledge_base,
+    show_tool_calls=True,
+)
+agent.print_response("How to make Thai curry?", markdown=True)

cookbook/models/openai/responses/memory.py

@@ -0,0 +1,56 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
+2. Run: `pip install openai sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/agents/personalized_memories_and_summaries.py` to run the agent
+"""
+
+from agno.agent import Agent, AgentMemory
+from agno.memory.db.postgres import PgMemoryDb
+from agno.models.openai import OpenAIResponses
+from agno.storage.agent.postgres import PostgresAgentStorage
+from rich.pretty import pprint
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    # Store the memories and summary in a database
+    memory=AgentMemory(
+        db=PgMemoryDb(table_name="agent_memory", db_url=db_url),
+        create_user_memories=True,
+        create_session_summary=True,
+    ),
+    # Store agent sessions in a database
+    storage=PostgresAgentStorage(
+        table_name="personalized_agent_sessions", db_url=db_url
+    ),
+    # Show debug logs so, you can see the memory being created
+    # debug_mode=True,
+)
+
+# -*- Share personal information
+agent.print_response("My name is john billings?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# Ask about the conversation
+agent.print_response(
+    "What have we been talking about, do you know my name?", stream=True
+)

cookbook/models/openai/responses/reasoning_o3_mini.py

@@ -0,0 +1,13 @@
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+from agno.tools.yfinance import YFinanceTools
+
+agent = Agent(
+    model=OpenAIResponses(id="o3-mini", reasoning_effort="high"),
+    tools=[YFinanceTools(enable_all=True)],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Print the response in the terminal
+agent.print_response("Write a report on the NVDA, is it a good buy?", stream=True)

cookbook/models/openai/responses/storage.py

@@ -0,0 +1,17 @@
+"""Run `pip install duckduckgo-search sqlalchemy openai` to install dependencies."""
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+from agno.storage.agent.postgres import PostgresAgentStorage
+from agno.tools.duckduckgo import DuckDuckGoTools
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    storage=PostgresAgentStorage(table_name="agent_sessions", db_url=db_url),
+    tools=[DuckDuckGoTools()],
+    add_history_to_messages=True,
+)
+agent.print_response("How many people live in Canada?")
+agent.print_response("What is their national anthem called?")

cookbook/models/openai/responses/structured_output.py

@@ -0,0 +1,53 @@
+from typing import List
+
+from agno.agent import Agent, RunResponse  # noqa
+from agno.models.openai import OpenAIChat
+from agno.models.openai.responses import OpenAIResponses  # noqa
+from pydantic import BaseModel, Field
+from rich.pretty import pprint
+
+
+class MovieScript(BaseModel):
+    setting: str = Field(
+        ..., description="Provide a nice setting for a blockbuster movie."
+    )
+    ending: str = Field(
+        ...,
+        description="Ending of the movie. If not available, provide a happy ending.",
+    )
+    genre: str = Field(
+        ...,
+        description="Genre of the movie. If not available, select action, thriller or romantic comedy.",
+    )
+    name: str = Field(..., description="Give a name to this movie")
+    characters: List[str] = Field(..., description="Name of characters for this movie.")
+    storyline: str = Field(
+        ..., description="3 sentence storyline for the movie. Make it exciting!"
+    )
+
+
+# Agent that uses JSON mode
+json_mode_agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    description="You write movie scripts.",
+    response_model=MovieScript,
+    structured_outputs=True,
+)
+
+# Agent that uses structured outputs
+structured_output_agent = Agent(
+    model=OpenAIResponses(id="gpt-4o-2024-08-06"),
+    description="You write movie scripts.",
+    response_model=MovieScript,
+    structured_outputs=True,
+)
+
+
+# Get the response in a variable
+# json_mode_response: RunResponse = json_mode_agent.run("New York")
+# pprint(json_mode_response.content)
+# structured_output_response: RunResponse = structured_output_agent.run("New York")
+# pprint(structured_output_response.content)
+
+json_mode_agent.print_response("New York")
+structured_output_agent.print_response("New York")

cookbook/models/openai/responses/tool_use.py

@@ -0,0 +1,13 @@
+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+from agno.tools.duckduckgo import DuckDuckGoTools
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[DuckDuckGoTools()],
+    show_tool_calls=True,
+    markdown=True,
+)
+agent.print_response("Whats happening in France?")

cookbook/models/openai/responses/tool_use_stream.py

@@ -0,0 +1,13 @@
+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+from agno.tools.duckduckgo import DuckDuckGoTools
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o"),
+    tools=[DuckDuckGoTools()],
+    show_tool_calls=True,
+    markdown=True,
+)
+agent.print_response("Whats happening in France?", stream=True)

cookbook/models/openai/responses/websearch_builtin_tool.py

@@ -0,0 +1,10 @@
+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIResponses
+
+agent = Agent(
+    model=OpenAIResponses(id="gpt-4o", web_search=True),
+    markdown=True,
+)
+agent.print_response("Whats happening in France?")

libs/agno/agno/agent/agent.py

@@ -31,7 +31,6 @@
 from agno.memory.agent import AgentMemory, AgentRun
 from agno.models.base import Model
 from agno.models.message import Message, MessageReferences
-from agno.models.openai.like import OpenAILike
 from agno.models.response import ModelResponse, ModelResponseEvent
 from agno.reasoning.step import NextAction, ReasoningStep, ReasoningSteps
 from agno.run.messages import RunMessages
@@ -2849,6 +2848,8 @@ def get_audio(self) -> Optional[List[AudioArtifact]]:
     ###########################################################################
 
     def reason(self, run_messages: RunMessages) -> Iterator[RunResponse]:
+        from agno.models.openai.like import OpenAILike
+
         # Yield a reasoning started event
         if self.stream_intermediate_steps:
             yield self.create_run_response(content="Reasoning started", event=RunEvent.reasoning_started)
@@ -3030,6 +3031,8 @@ def reason(self, run_messages: RunMessages) -> Iterator[RunResponse]:
             )
 
     async def areason(self, run_messages: RunMessages) -> Any:
+        from agno.models.openai.like import OpenAILike
+
         # Yield a reasoning started event
         if self.stream_intermediate_steps:
             yield self.create_run_response(content="Reasoning started", event=RunEvent.reasoning_started)

libs/agno/agno/models/openai/__init__.py

@@ -1,2 +1,3 @@
 from agno.models.openai.chat import OpenAIChat
 from agno.models.openai.like import OpenAILike
+from agno.models.openai.responses import OpenAIResponses