> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Website Knowledge Base

> Learn how to use websites in your knowledge base.

The **WebsiteKnowledgeBase** reads websites, converts them into vector embeddings and loads them to a `vector_db`.

## Usage

<Note>
  We are using a local PgVector database for this example. [Make sure it's running](https://docs-v1.agno.com/vectordb/pgvector)
</Note>

```shell theme={null}
pip install bs4
```

```python knowledge_base.py theme={null}
from agno.knowledge.website import WebsiteKnowledgeBase
from agno.vectordb.pgvector import PgVector

knowledge_base = WebsiteKnowledgeBase(
    urls=["https://docs-v1.agno.com/introduction"],
    # Number of links to follow from the seed URLs
    max_links=10,
    # Table name: ai.website_documents
    vector_db=PgVector(
        table_name="website_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
```

Then use the `knowledge_base` with an `Agent`:

```python agent.py theme={null}
from agno.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")
```

#### WebsiteKnowledgeBase also supports async loading.

```shell theme={null}
pip install qdrant-client
```

We are using a local Qdrant database for this example. [Make sure it's running](https://docs-v1.agno.com/vectordb/qdrant)

```python async_knowledge_base.py theme={null}
import asyncio

import asyncio

from agno.agent import Agent
from agno.knowledge.website import WebsiteKnowledgeBase
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "website-content"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")


# Create a knowledge base with the seed URLs
knowledge_base = WebsiteKnowledgeBase(
    urls=["https://docs-v1.agno.com/introduction"],
    # Number of links to follow from the seed URLs
    max_links=5,
    # Table name: ai.website_documents
    vector_db=vector_db,
)

# Create an agent with the knowledge base
agent = Agent(knowledge=knowledge_base, search_knowledge=True, debug_mode=True)

if __name__ == "__main__":
    # Comment out after first run
    asyncio.run(knowledge_base.aload(recreate=False))

    # Create and use the agent
    asyncio.run(agent.aprint_response("How does agno work?", markdown=True))
```

## Params

| Parameter   | Type                      | Default | Description                                                                                       |
| ----------- | ------------------------- | ------- | ------------------------------------------------------------------------------------------------- |
| `urls`      | `List[str]`               | `[]`    | URLs to read                                                                                      |
| `reader`    | `Optional[WebsiteReader]` | `None`  | A `WebsiteReader` that reads the urls and converts them into `Documents` for the vector database. |
| `max_depth` | `int`                     | `3`     | Maximum depth to crawl.                                                                           |
| `max_links` | `int`                     | `10`    | Number of links to crawl.                                                                         |

`WebsiteKnowledgeBase` is a subclass of the [AgentKnowledge](/reference/knowledge/base) class and has access to the same params.

## Developer Resources

* View [Sync loading Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/website_kb.py)
* View [Async loading Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/website_kb_async.py)