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‎cookbook/examples/apps/mcp_agent/README.md

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@@ -30,6 +30,8 @@ UAgI (Universal Agent Interface) is a powerful agent application that leverages
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- Only works with 1 MCP server at a time
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- Changing MCP servers resets the agent
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- Only supports 2 MCP servers at the moment
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- Chat history is broken
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- MCP Cleanup is not working, so memory leaks are possible
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## 🚀 Quick Start
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‎cookbook/models/openai/.gitignore ‎cookbook/models/.gitignore

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‎cookbook/models/anthropic/image_agent.py ‎cookbook/models/anthropic/image_input_url.py

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‎cookbook/models/anthropic/pdf_input_local.py

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from pathlib import Path
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from agno.agent import Agent
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from agno.media import File
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from agno.models.anthropic import Claude
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from agno.utils.media import download_file
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pdf_path = Path(__file__).parent.joinpath("ThaiRecipes.pdf")
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# Download the file using the download_file function
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download_file(
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"https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", str(pdf_path)
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)
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agent = Agent(
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model=Claude(id="claude-3-5-sonnet-20241022"),
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markdown=True,
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)
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agent.print_response(
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"Summarize the contents of the attached file.",
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files=[
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File(
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filepath=pdf_path,
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),
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],
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stream=True,
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)

‎cookbook/models/anthropic/pdf_input_url.py

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from agno.agent import Agent
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from agno.media import File
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from agno.models.anthropic import Claude
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agent = Agent(
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model=Claude(id="claude-3-5-sonnet-20241022"),
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markdown=True,
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)
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agent.print_response(
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"Summarize the contents of the attached file.",
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files=[
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File(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"),
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],
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stream=True,
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)

‎cookbook/models/aws/bedrock/image_agent_bytes.py

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from agno.media import Image
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from agno.models.aws import AwsBedrock
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from agno.tools.duckduckgo import DuckDuckGoTools
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from agno.utils.media import download_image
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agent = Agent(
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model=AwsBedrock(id="amazon.nova-pro-v1:0"),
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image and give me the latest news about it.",

‎cookbook/models/azure/ai_foundry/image_agent_bytes.py

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from agno.agent import Agent
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from agno.media import Image
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from agno.models.azure import AzureAIFoundry
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from agno.utils.media import download_image
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agent = Agent(
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model=AzureAIFoundry(id="Llama-3.2-11B-Vision-Instruct"),
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image.",

‎cookbook/models/cohere/image_agent.py

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from agno.agent import Agent
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from agno.media import Image
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from agno.models.cohere import Cohere
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agent = Agent(
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model=Cohere(id="c4ai-aya-vision-8b"),
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markdown=True,
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)
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agent.print_response(
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"Tell me about this image.",
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images=[
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Image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
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)
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],
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stream=True,
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)

‎cookbook/models/cohere/image_agent_bytes.py

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from pathlib import Path
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from agno.agent import Agent
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from agno.media import Image
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from agno.models.cohere.chat import Cohere
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from agno.utils.media import download_image
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agent = Agent(
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model=Cohere(id="c4ai-aya-vision-8b"),
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markdown=True,
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)
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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# Read the image file content as bytes
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image.",
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images=[
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Image(content=image_bytes),
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],
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stream=True,
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)

‎cookbook/models/cohere/image_agent_local_file.py

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from pathlib import Path
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from agno.agent import Agent
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from agno.media import Image
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from agno.models.cohere.chat import Cohere
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from agno.utils.media import download_image
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agent = Agent(
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model=Cohere(id="c4ai-aya-vision-8b"),
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markdown=True,
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)
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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agent.print_response(
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"Tell me about this image.",
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images=[
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Image(filepath=image_path),
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],
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stream=True,
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)

‎cookbook/models/google/gemini/pdf_input_local.py

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from pathlib import Path
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from agno.agent import Agent
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from agno.media import File
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from agno.models.google import Gemini
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from agno.utils.media import download_file
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pdf_path = Path(__file__).parent.joinpath("ThaiRecipes.pdf")
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# Download the file using the download_file function
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download_file(
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"https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", str(pdf_path)
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)
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agent = Agent(
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model=Gemini(id="gemini-2.0-flash-exp"),
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markdown=True,
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add_history_to_messages=True,
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)
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agent.print_response(
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"Summarize the contents of the attached file.",
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files=[File(filepath=pdf_path)],
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)
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agent.print_response("Suggest me a recipe from the attached file.")

‎cookbook/models/google/gemini/pdf_input_url.py

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from agno.agent import Agent
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from agno.media import File
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from agno.models.google import Gemini
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agent = Agent(
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model=Gemini(id="gemini-2.0-flash-exp"),
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markdown=True,
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)
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agent.print_response(
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"Summarize the contents of the attached file.",
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files=[File(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")],
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)

‎cookbook/models/ibm/watsonx/image_agent_bytes.py

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from agno.agent import Agent
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from agno.media import Image
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from agno.models.ibm import WatsonX
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from agno.tools.duckduckgo import DuckDuckGoTools
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agent = Agent(
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model=WatsonX(id="meta-llama/llama-3-2-11b-vision-instruct"),
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image and and give me the latest news about it.",

‎cookbook/models/openai/image_agent_bytes.py

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from agno.media import Image
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from agno.models.openai import OpenAIChat
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from agno.tools.duckduckgo import DuckDuckGoTools
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from agno.utils.media import download_image
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agent = Agent(
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model=OpenAIChat(id="gpt-4o"),
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image and give me the latest news about it.",

‎cookbook/models/together/image_agent_bytes.py

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image_path = Path(__file__).parent.joinpath("sample.jpg")
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image",

‎cookbook/models/xai/image_agent_bytes.py

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from agno.media import Image
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from agno.models.xai import xAI
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from agno.tools.duckduckgo import DuckDuckGoTools
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from agno.utils.media import download_image
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agent = Agent(
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model=xAI(id="grok-2-vision-latest"),
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image_path = Path(__file__).parent.joinpath("sample.jpg")
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download_image(
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url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
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output_path=str(image_path),
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)
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# Read the image file content as bytes
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with open(image_path, "rb") as img_file:
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image_bytes = img_file.read()
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image_bytes = image_path.read_bytes()
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agent.print_response(
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"Tell me about this image and give me the latest news about it.",

‎cookbook/tools/agentql_tools.py

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"""
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AgentQL Tools for scraping websites.
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Prerequisites:
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- Set the environment variable `AGENTQL_API_KEY` with your AgentQL API key.
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You can obtain the API key from the AgentQL website:
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https://agentql.com/
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- Run `playwright install` to install a browser extension for playwright.
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AgentQL will open up a browser instance (don't close it) and do scraping on the site.
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"""
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.agentql import AgentQLTools
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# Create agent with default AgentQL tool
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agent = Agent(
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model=OpenAIChat(id="gpt-4o"), tools=[AgentQLTools()], show_tool_calls=True
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)
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agent.print_response("https://docs.agno.com/introduction", markdown=True)
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# Define custom AgentQL query for specific data extraction (see https://docs.agentql.com/concepts/query-language)
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custom_query = """
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{
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title
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text_content[]
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}
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"""
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# Create AgentQL tool with custom query
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custom_scraper = AgentQLTools(agentql_query=custom_query, custom_scrape=True)
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# Create agent with custom AgentQL tool
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custom_agent = Agent(
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model=OpenAIChat(id="gpt-4o"), tools=[custom_scraper], show_tool_calls=True
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)
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custom_agent.print_response("https://docs.agno.com/introduction", markdown=True)

‎cookbook/tools/browserbase_tools.py

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from os import getenv
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from agno.agent import Agent
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from agno.tools.browserbase import BrowserbaseTools
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# Browserbase Configuration
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# -------------------------------
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# These environment variables are required for the BrowserbaseTools to function properly.
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# You can set them in your .env file or export them directly in your terminal.
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# BROWSERBASE_API_KEY: Your API key from Browserbase dashboard
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# - Required for authentication
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# - Format: Starts with "bb_live_" or "bb_test_" followed by a unique string
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# BROWSERBASE_PROJECT_ID: The project ID from your Browserbase dashboard
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# - Required to identify which project to use for browser sessions
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# - Format: UUID string (8-4-4-4-12 format)
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# BROWSERBASE_BASE_URL: The Browserbase API endpoint
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# - Optional: Defaults to https://api.browserbase.com if not specified
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# - Only change this if you're using a custom API endpoint or proxy
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agent = Agent(
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name="Web Automation Assistant",
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tools=[BrowserbaseTools()],
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instructions=[
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"You are a web automation assistant that can help with:",
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"1. Capturing screenshots of websites",
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"2. Extracting content from web pages",
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"3. Monitoring website changes",
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"4. Taking visual snapshots of responsive layouts",
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"5. Automated web testing and verification",
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],
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show_tool_calls=True,
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markdown=True,
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debug_mode=True,
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)
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# Content Extraction and SS
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# agent.print_response("""
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# Go to https://news.ycombinator.com and extract:
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# 1. The page title
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# 2. Take a screenshot of the top stories section
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# """)
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agent.print_response("""
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Visit https://quotes.toscrape.com and:
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1. Extract the first 5 quotes and their authors
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2. Navigate to page 2
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3. Extract the first 5 quotes from page 2
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""")

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