Readers

Convert files, URLs, and text into searchable documents.

Readers transform raw content into Document objects that can be chunked, embedded, and stored in your knowledge base. Each reader handles a specific format (PDF, CSV, Markdown, etc.) and extracts text and metadata.

from agno.knowledge.reader.pdf_reader import PDFReader

reader = PDFReader(chunk=True, chunk_size=5000)
documents = reader.read("company_handbook.pdf")

How Readers Work

1. Parse: Read the raw content using format-specific logic
2. Extract: Pull out text and metadata (page numbers, authors, etc.)
3. Chunk: Split large content into smaller pieces (if enabled)
4. Return: Provide a list of Document objects ready for embedding

# Output structure
Document(
    content="The extracted text...",
    id="unique_id",
    name="document_name",
    meta_data={"page": 1, "source": "handbook.pdf"},
)

Supported Readers

Reader	Description
PDFReader	Extract text from PDF files
DoclingReader	Process multiple formats via Docling
TextReader	Plain text files
MarkdownReader	Markdown files
CSVReader	CSV files (rows become documents)
FieldLabeledCSVReader	CSV rows as field-labeled text
JSONReader	JSON files
PPTXReader	PowerPoint presentations
ArxivReader	Academic papers from arXiv
WikipediaReader	Wikipedia articles
YouTubeReader	YouTube transcripts
WebsiteReader	Crawl websites recursively
WebSearchReader	Web search results
FirecrawlReader	Web scraping via Firecrawl API

Using Readers with Knowledge

Pass a reader to knowledge.insert() to override automatic format detection:

from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader

knowledge = Knowledge(vector_db=vector_db)

# Use custom reader configuration
reader = PDFReader(chunk_size=3000, split_on_pages=True)
knowledge.insert(path="documents/", reader=reader)

Auto-Selection

Agno automatically selects the right reader based on file extension or URL:

from agno.knowledge.reader.reader_factory import ReaderFactory

# By file extension
reader = ReaderFactory.get_reader_for_extension(".pdf")  # PDFReader
reader = ReaderFactory.get_reader_for_extension(".csv")  # CSVReader

# By URL
reader = ReaderFactory.get_reader_for_url("https://youtube.com/watch?v=...")  # YouTubeReader

When using knowledge.insert(), this happens automatically.

Configuration

Chunking

reader = PDFReader(
    chunk=True,           # Enable chunking (default: True)
    chunk_size=5000,      # Characters per chunk
)

Format-Specific Options

# PDF with encryption and OCR
reader = PDFReader(
    password="secret",
    read_images=True,     # OCR for images
    split_on_pages=True,  # One document per page
)

# CSV with custom encoding
reader = CSVReader(
    encoding="latin-1",
)

# Text with encoding override
reader = TextReader(
    encoding="utf-8",
)

Runtime Options

Override settings when calling read():

documents = reader.read(
    "file.pdf",
    name="custom_document_name",  # Override default naming
    password="runtime_password",  # Password at read time
)

Async Processing

All readers support async for better performance with I/O operations:

import asyncio

# Single file
documents = await reader.async_read("file.pdf")

# Batch processing
tasks = [reader.async_read(file) for file in files]
all_documents = await asyncio.gather(*tasks)

Custom Chunking Strategy

Override the default chunking behavior:

from agno.knowledge.chunking.semantic_chunking import SemanticChunking

reader = PDFReader(
    chunk=True,
    chunking_strategy=SemanticChunking(),
)

See Chunking for available strategies.

Error Handling

Readers return an empty list when processing fails. Check logs for debugging information:

documents = reader.read("corrupted.pdf")
if not documents:
    print("Failed to read file, check logs for details")

Next Steps

PDF Reader

Extract text from PDFs

Website Reader

Crawl and index websites

Chunking

Control how content is split

Vector DB

Store processed documents