Merge

Author: dirkbrnd

cookbook/examples/apps/mcp_agent/README.md

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

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

@@ -0,0 +1,28 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import File
+from agno.models.anthropic import Claude
+from agno.utils.media import download_file
+
+pdf_path = Path(__file__).parent.joinpath("ThaiRecipes.pdf")
+
+# Download the file using the download_file function
+download_file(
+    "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", str(pdf_path)
+)
+
+agent = Agent(
+    model=Claude(id="claude-3-5-sonnet-20241022"),
+    markdown=True,
+)
+
+agent.print_response(
+    "Summarize the contents of the attached file.",
+    files=[
+        File(
+            filepath=pdf_path,
+        ),
+    ],
+    stream=True,
+)

cookbook/models/anthropic/pdf_input_local.py

@@ -0,0 +1,16 @@
+from agno.agent import Agent
+from agno.media import File
+from agno.models.anthropic import Claude
+
+agent = Agent(
+    model=Claude(id="claude-3-5-sonnet-20241022"),
+    markdown=True,
+)
+
+agent.print_response(
+    "Summarize the contents of the attached file.",
+    files=[
+        File(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"),
+    ],
+    stream=True,
+)

cookbook/models/anthropic/pdf_input_url.py

@@ -4,6 +4,7 @@
 from agno.media import Image
 from agno.models.aws import AwsBedrock
 from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.utils.media import download_image
 
 agent = Agent(
     model=AwsBedrock(id="amazon.nova-pro-v1:0"),
@@ -13,9 +14,13 @@
 
 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
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image and give me the latest news about it.",

cookbook/models/aws/bedrock/image_agent_bytes.py

@@ -3,6 +3,7 @@
 from agno.agent import Agent
 from agno.media import Image
 from agno.models.azure import AzureAIFoundry
+from agno.utils.media import download_image
 
 agent = Agent(
     model=AzureAIFoundry(id="Llama-3.2-11B-Vision-Instruct"),
@@ -11,9 +12,13 @@
 
 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
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image.",

cookbook/models/azure/ai_foundry/image_agent_bytes.py

@@ -0,0 +1,18 @@
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.cohere import Cohere
+
+agent = Agent(
+    model=Cohere(id="c4ai-aya-vision-8b"),
+    markdown=True,
+)
+
+agent.print_response(
+    "Tell me about this image.",
+    images=[
+        Image(
+            url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
+        )
+    ],
+    stream=True,
+)

cookbook/models/cohere/image_agent.py

@@ -0,0 +1,29 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.cohere.chat import Cohere
+from agno.utils.media import download_image
+
+agent = Agent(
+    model=Cohere(id="c4ai-aya-vision-8b"),
+    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.",
+    images=[
+        Image(content=image_bytes),
+    ],
+    stream=True,
+)

cookbook/models/cohere/image_agent_bytes.py

@@ -0,0 +1,26 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.cohere.chat import Cohere
+from agno.utils.media import download_image
+
+agent = Agent(
+    model=Cohere(id="c4ai-aya-vision-8b"),
+    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),
+)
+
+agent.print_response(
+    "Tell me about this image.",
+    images=[
+        Image(filepath=image_path),
+    ],
+    stream=True,
+)

cookbook/models/cohere/image_agent_local_file.py

@@ -0,0 +1,25 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import File
+from agno.models.google import Gemini
+from agno.utils.media import download_file
+
+pdf_path = Path(__file__).parent.joinpath("ThaiRecipes.pdf")
+
+# Download the file using the download_file function
+download_file(
+    "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", str(pdf_path)
+)
+
+agent = Agent(
+    model=Gemini(id="gemini-2.0-flash-exp"),
+    markdown=True,
+    add_history_to_messages=True,
+)
+
+agent.print_response(
+    "Summarize the contents of the attached file.",
+    files=[File(filepath=pdf_path)],
+)
+agent.print_response("Suggest me a recipe from the attached file.")

cookbook/models/google/gemini/pdf_input_local.py

@@ -0,0 +1,13 @@
+from agno.agent import Agent
+from agno.media import File
+from agno.models.google import Gemini
+
+agent = Agent(
+    model=Gemini(id="gemini-2.0-flash-exp"),
+    markdown=True,
+)
+
+agent.print_response(
+    "Summarize the contents of the attached file.",
+    files=[File(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")],
+)

cookbook/models/google/gemini/pdf_input_url.py

@@ -3,7 +3,6 @@
 from agno.agent import Agent
 from agno.media import Image
 from agno.models.ibm import WatsonX
-from agno.tools.duckduckgo import DuckDuckGoTools
 
 agent = Agent(
     model=WatsonX(id="meta-llama/llama-3-2-11b-vision-instruct"),
@@ -13,8 +12,7 @@
 image_path = Path(__file__).parent.joinpath("sample.jpg")
 
 # Read the image file content as bytes
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image and and give me the latest news about it.",

cookbook/models/ibm/watsonx/image_agent_bytes.py

@@ -0,0 +1,14 @@
+from pathlib import Path
+
+from agno.agent import Agent
+from agno.media import Image
+from agno.models.ollama import Ollama
+
+agent = Agent(model=Ollama(id="gemma3:12b"), markdown=True)
+
+image_path = Path(__file__).parent.joinpath("super-agents.png")
+agent.print_response(
+    "Write a 3 sentence fiction story about the image",
+    images=[Image(filepath=image_path)],
+    stream=True,
+)

cookbook/models/ollama/demo_gemma.py

@@ -4,6 +4,7 @@
 from agno.media import Image
 from agno.models.openai import OpenAIChat
 from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.utils.media import download_image
 
 agent = Agent(
     model=OpenAIChat(id="gpt-4o"),
@@ -13,9 +14,13 @@
 
 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
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image and give me the latest news about it.",

cookbook/models/openai/image_agent_bytes.py

@@ -12,8 +12,7 @@
 image_path = Path(__file__).parent.joinpath("sample.jpg")
 
 # Read the image file content as bytes
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image",

cookbook/models/together/image_agent_bytes.py

@@ -4,6 +4,7 @@
 from agno.media import Image
 from agno.models.xai import xAI
 from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.utils.media import download_image
 
 agent = Agent(
     model=xAI(id="grok-2-vision-latest"),
@@ -13,9 +14,13 @@
 
 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
-with open(image_path, "rb") as img_file:
-    image_bytes = img_file.read()
+image_bytes = image_path.read_bytes()
 
 agent.print_response(
     "Tell me about this image and give me the latest news about it.",

cookbook/models/xai/image_agent_bytes.py

@@ -0,0 +1,38 @@
+"""
+AgentQL Tools for scraping websites.
+
+Prerequisites:
+- Set the environment variable `AGENTQL_API_KEY` with your AgentQL API key.
+  You can obtain the API key from the AgentQL website:
+  https://agentql.com/
+- Run `playwright install` to install a browser extension for playwright.
+
+AgentQL will open up a browser instance (don't close it) and do scraping on the site.
+"""
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.agentql import AgentQLTools
+
+# Create agent with default AgentQL tool
+agent = Agent(
+    model=OpenAIChat(id="gpt-4o"), tools=[AgentQLTools()], show_tool_calls=True
+)
+agent.print_response("https://docs.agno.com/introduction", markdown=True)
+
+# Define custom AgentQL query for specific data extraction (see https://docs.agentql.com/concepts/query-language)
+custom_query = """
+{
+    title
+    text_content[]
+}
+"""
+
+# Create AgentQL tool with custom query
+custom_scraper = AgentQLTools(agentql_query=custom_query, custom_scrape=True)
+
+# Create agent with custom AgentQL tool
+custom_agent = Agent(
+    model=OpenAIChat(id="gpt-4o"), tools=[custom_scraper], show_tool_calls=True
+)
+custom_agent.print_response("https://docs.agno.com/introduction", markdown=True)