Quickstart

Build a knowledge-powered agent in under 5 minutes.

Build an agent that answers questions about your documents.

Create an Agent with Knowledge

knowledge_agent.py

from agno.agent import Agent
from agno.knowledge.embedder.google import GeminiEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.google import Gemini
from agno.vectordb.chroma import ChromaDb
from agno.vectordb.search import SearchType

# Create a knowledge base with ChromaDB
knowledge = Knowledge(
    vector_db=ChromaDb(
        collection="docs",
        path="tmp/chromadb",
        persistent_client=True,
        search_type=SearchType.hybrid,
        embedder=GeminiEmbedder(id="gemini-embedding-001"),
    ),
)

# Load content into the knowledge base
knowledge.insert(url="https://docs.agno.com/introduction.md", skip_if_exists=True)

# Create an agent that searches the knowledge base
agent = Agent(
    model=Gemini(id="gemini-3-flash-preview"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

agent.print_response("What is Agno?", stream=True)

Setup

1. Create virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno chromadb google-genai
   
3. Export your API key

   Mac

   export GOOGLE_API_KEY=your-google-api-key
   

   setx GOOGLE_API_KEY your-google-api-key
   
4. Run the agent

   python knowledge_agent.py
   

The agent searches the knowledge base, finds relevant content, and answers based on what it found.

Load Different Content Types

Files

  knowledge.insert(path="docs/product-guide.pdf")
  knowledge.insert(path="data/")  # Entire directory

URLs

  knowledge.insert(url="https://example.com/docs.pdf")

Text

  knowledge.insert(text_content="Your content here...")

Agno detects file types automatically and uses the appropriate reader for PDFs, DOCX, CSV, Markdown, and more.

What’s Happening

1. Insert: Content is chunked, embedded with Gemini, and stored in ChromaDB
2. Query: The agent receives your question and decides to search the knowledge base using the search_knowledge_base tool
3. Response: The agent uses the retrieved content to answer, grounding its response in your data

This is Agentic RAG. The agent decides when to search rather than blindly injecting context on every query.

Next Steps

Agents with Knowledge

Agentic RAG, traditional RAG, reranking

Teams with Knowledge

Distributed search, coordinated RAG

Vector Stores

PgVector, Pinecone, Weaviate, and 20+ more

Search & Retrieval

Vector, keyword, and hybrid search