Quickstart

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-3.5-flash"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

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

Setup

Create virtual environment

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

uv venv --python 3.12
.venv\Scripts\activate

Install dependencies

uv pip install -U agno chromadb google-genai

Export your API key

export GOOGLE_API_KEY=your-google-api-key

$env:GOOGLE_API_KEY = "your-google-api-key"

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
URLs
Text

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

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

  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

Insert: Content is chunked, embedded with Gemini, and stored in ChromaDB
Query: The agent receives your question and decides to search the knowledge base using the search_knowledge_base tool
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 and traditional RAG

Teams with Knowledge

Shared knowledge bases for multi-agent teams

Vector Stores

PgVector, Pinecone, Weaviate, and more

Search & Retrieval

Vector, keyword, and hybrid search

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Create an Agent with Knowledge

Setup

Load Different Content Types

What’s Happening

Next Steps

Agents with Knowledge

Teams with Knowledge

Vector Stores

Search & Retrieval

​Create an Agent with Knowledge

​Setup

​Load Different Content Types

​What’s Happening

​Next Steps

Agents with Knowledge

Teams with Knowledge

Vector Stores

Search & Retrieval

Create an Agent with Knowledge

Setup

Load Different Content Types

What’s Happening

Next Steps