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Agentic chunking is an intelligent method of splitting documents into smaller chunks by using a model to determine natural breakpoints in the text. Rather than splitting text at fixed character counts, it analyzes the content to find semantically meaningful boundaries like paragraph breaks and topic transitions.
1

Create a Python file

agentic_chunking.py
import asyncio
from agno.agent import Agent
from agno.knowledge.chunking.agentic import AgenticChunking
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(table_name="recipes_agentic_chunking", db_url=db_url),
)

asyncio.run(knowledge.add_content_async(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        name="Agentic Chunking Reader",
        chunking_strategy=AgenticChunking(),
    ),
))

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

agent.print_response("How to make Thai curry?", markdown=True)
2

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
3

Install dependencies

uv pip install -U agno sqlalchemy psycopg pgvector
4

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agno/pgvector:16
5

Run the script

python agentic_chunking.py

​
Agentic Chunking Params

ParameterTypeDefaultDescription
modelModelOpenAIChatThe model to use for chunking.
max_chunk_sizeint5000The maximum size of each chunk.