> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Give agents access to documents, databases, and domain expertise.

**Knowledge** gives agents access to information beyond their training data. Load files, URLs, or raw text, and agents can ground responses in retrieved content.

```python theme={null}
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb

# Create a knowledge base
knowledge = Knowledge(
    vector_db=ChromaDb(
        collection="docs",
        path="tmp/chromadb",
        persistent_client=True,
    ),
)

# Load content
knowledge.insert(url="https://docs.agno.com/introduction.md")

# Create an agent that searches the knowledge base
agent = Agent(knowledge=knowledge, search_knowledge=True)
agent.print_response("What is Agno?")
```

The agent searches its knowledge base and grounds its response in the content.

## How It Works

Knowledge combines three components:

1. **Content ingestion**: Read documents from files, URLs, cloud storage, or raw text. Agno includes readers for PDF, DOCX, CSV, Markdown, and more.

2. **Chunking and embedding**: Documents are split into searchable chunks and converted to vector embeddings that capture semantic meaning.

3. **Search and retrieval**: When an agent needs information, it searches the vector database for relevant chunks and includes them in its context.

You can use **Agentic RAG** (agent decides when to search) or **Traditional RAG** (always inject context). Agentic RAG is the default and works well for most use cases.

## Why Knowledge Matters

Language models have broad general knowledge but lack context about your specific domain. Knowledge bridges this gap by providing relevant information at runtime.

**Start with your content.** Load company documentation, database schemas, product specs, support FAQs, or research papers. The agent retrieves relevant passages and uses them as context for its response.

**Then let agents learn.** Agents can write to knowledge as well as search it: save insights they discover and retrieve them later, building expertise across conversations.

```python theme={null}
def save_learning(title: str, insight: str) -> str:
    """Save a reusable insight to the knowledge base."""
    knowledge.insert(name=title, text_content=insight)
    return f"Saved: {title}"

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    tools=[save_learning],  # Agent can write to knowledge
)
```

The persistent collection keeps saved insights available to later runs.

## Examples

> **[Quick Start](https://agno-v2.mintlify.site/knowledge/quickstart.md)**
>
> Build an agent with knowledge in 5 minutes

> **[Knowledge for Agents](https://agno-v2.mintlify.site/knowledge/agents/overview.md)**
>
> Agentic RAG and traditional RAG

> **[Knowledge for Teams](https://agno-v2.mintlify.site/knowledge/teams/overview.md)**
>
> Shared knowledge bases for multi-agent teams

## Concepts

> **[Vector DB](https://agno-v2.mintlify.site/knowledge/concepts/vector-db.md)**
>
> Store and search embeddings

> **[Content DB](https://agno-v2.mintlify.site/knowledge/concepts/contents-db.md)**
>
> Track knowledge contents

> **[Search & Retrieval](https://agno-v2.mintlify.site/knowledge/concepts/search-and-retrieval/overview.md)**
>
> Vector, keyword, and hybrid search

> **[Readers](https://agno-v2.mintlify.site/knowledge/concepts/readers/overview.md)**
>
> Ingest from various sources

> **[Chunkers](https://agno-v2.mintlify.site/knowledge/concepts/chunking/overview.md)**
>
> Control document splitting

> **[Embedders](https://agno-v2.mintlify.site/knowledge/concepts/embedder/overview.md)**
>
> Convert text to vectors

> **[Filtering](https://agno-v2.mintlify.site/knowledge/concepts/filters/overview.md)**
>
> Filter results by metadata

## Vector Stores

Agno supports 19 vector databases, from local options like LanceDB and ChromaDB to managed services like Pinecone and Weaviate.

> **[All Vector Stores](https://agno-v2.mintlify.site/knowledge/vector-stores/index.md)**
>
> See supported databases


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.