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Agno

Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.

Agno and Weaviate​

Weaviate is a supported vector database in Agno. You first create the vector store with:

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
)

Then you'll create the knowledge base for your agents with:

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

Our Resources​

Hands on Learning: Build your technical understanding with end-to-end tutorials.

Hands on Learning​

TopicDescriptionResource
Weaviate Query Agent with AgnoThis notebook will show you how to define the Weaviate Query Agent as a tool through Agno.Notebook