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Agno, https://docs.agno.com/introduction , is an open-source Python library for building multimodal AI agents powered by large language models (LLMs). It requires coding to define agents, their tools (e.g., web search, financial data APIs), and behaviors (e.g., memory, reasoning). It’s less about connecting existing services and more about creating intelligent, autonomous entities that can reason, use tools, and handle complex tasks.

from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.duckduckgo import DuckDuckGoTools agent = Agent( model=OpenAIChat(id="gpt-4o"), description="You are an enthusiastic news reporter with a flair for storytelling!", tools=[DuckDuckGoTools()], markdown=True ) agent.print_response("Tell me about a breaking news story from New York.", stream=True)

from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.duckduckgo import DuckDuckGoTools from agno.knowledge.pdf_url import PDFUrlKnowledgeBase from agno.vectordb.lancedb import LanceDb, SearchType from agno.embedder.openai import OpenAIEmbedder agent = Agent( model=OpenAIChat(id="gpt-4o"), description="You are a Thai cuisine expert!", instructions=["Prefer knowledge base info, use web to fill gaps"], knowledge=PDFUrlKnowledgeBase( urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"], vector_db=LanceDb( uri="tmp/lancedb", table_name="recipes", search_type=SearchType.hybrid, embedder=OpenAIEmbedder(id="text-embedding-3-small") ) ), tools=[DuckDuckGoTools()], markdown=True ) agent.knowledge.load() # Load knowledge base once agent.print_response("How do I make chicken and galangal in coconut milk soup?", stream=True)