Knowledge | Agno

Examples for giving agents searchable knowledge, ordered from a first RAG agent to production deployment.

Getting Started

Example	Description
Basic RAG	Context injection, the simplest way to give an agent access to documents.
Agentic RAG	The agent gets a `search_knowledge_base` tool and decides when to query.
Website Per Page	Ingest sitemap pages with per-page content records.
Loading Content	Load from local files, URLs, raw text, topics, and batch operations.

Building Blocks

Example	Description
Chunking Strategies	Side-by-side comparison of document splitting strategies.
Search Types	Vector, keyword, and hybrid search compared.
Reranking	Score and reorder retrieved candidates with Cohere.
Filtering	Narrow search results with document metadata.
Agentic Filtering	The agent builds metadata filters from the user query.
Embedders	Choose and configure embedding models.

Production

Example	Description
Multi-Source RAG	Combine PDFs, web pages, and text in one knowledge base.
Knowledge Lifecycle	Insert, skip existing content, and remove vectors; track records separately.
Multi-Tenant	Isolate per-tenant data with `isolate_vector_search`.
AgentOS	Serve agents and knowledge as API endpoints.
Error Handling	Ingestion patterns that survive bad files and failed loads.
SSRF Hardening	Restrict URL-fetching readers with `allowed_hosts`.

Advanced

Example	Description
Custom Retriever	Full control over retrieval logic, bypassing the Knowledge class.
Custom Chunking	Implement your own chunking strategy.
Graph RAG	Build a knowledge graph from documents with LightRAG.
Knowledge Tools	Think, search, and analyze tools for reasoning over knowledge.
Knowledge Protocol	Custom knowledge sources via the `KnowledgeProtocol` interface.
Prefix Search	Search-as-you-type matching on partial words.

Integrations

Cloud

Example	Description
AWS	Load files and folders from S3 buckets.
Azure	Load from Azure Blob Storage containers.
Azure (SAS Token)	Blob Storage access with SAS token authentication.
GCP	Load from Google Cloud Storage buckets.
SharePoint	Load from SharePoint document libraries.
GitHub Dynamic Repo	Override the source repository per request.
Multi-Source	Combine multiple remote sources in one Knowledge instance.

RAG

Example	Description
Agentic RAG with Infinity Reranker	Agentic RAG with an Infinity reranker backend.
Agentic RAG with LightRAG	Agentic RAG backed by LightRAG.
Local RAG with LangChain and Qdrant	Local inference and storage after source and model downloads.

Readers

Example	Description
Documents	PDF auto-detection and explicit Excel reading.
Data	CSV and JSON readers.
Web	Website crawling and a PDF URL.
Docling: PDF	PDF processing with different output formats.
Docling: Office Documents	Microsoft Office document processing.
Docling: Images	OCR for image files.
Docling: Audio	Speech-to-text transcription for audio files.
Docling: Markup	Markup and structured document formats.
Docling: Data Files	Spreadsheet and data file processing.
Docling: Input Types	Paths, URL strings, and file-like input.

Vector Databases

Example	Description
Qdrant	The recommended vector database for production.
Local	Embedded ChromaDB and LanceDB for prototyping.
Managed	Serverless Pinecone with zero infrastructure management.
PgVector	Vector search inside PostgreSQL.

On this page

Getting Started Building Blocks Production Advanced Integrations Cloud RAG Readers Vector Databases