> ## 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.

# Agentic RAG With Lightrag

> Ingest a PDF, a Wikipedia topic, and a URL into a LightRag-backed knowledge base and query it with an async agent.

Load a PDF, a Wikipedia topic, and a URL into a LightRAG-backed knowledge base and query it with an agent.

```python agentic_rag_with_lightrag.py theme={null}
"""
Agentic Rag With Lightrag
=============================

Demonstrates an agentic RAG flow backed by LightRAG (relocated integration example).
"""

import asyncio
from os import getenv

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.wikipedia_reader import WikipediaReader
from agno.vectordb.lightrag import LightRag

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
vector_db = LightRag(api_key=getenv("LIGHTRAG_API_KEY"))

knowledge = Knowledge(
    name="My LightRag Knowledge Base",
    description="Knowledge base using a LightRag vector database",
    vector_db=vector_db,
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    read_chat_history=False,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            path="cookbook/07_knowledge/testing_resources/cv_1.pdf",
            metadata={"doc_type": "recipe_book"},
        )
    )

    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            topics=["Manchester United"],
            reader=WikipediaReader(),
        )
    )

    asyncio.run(
        knowledge.ainsert(
            name="Recipes",
            url="https://en.wikipedia.org/wiki/Manchester_United_F.C.",
        )
    )

    asyncio.run(
        agent.aprint_response("What skills does Jordan Mitchell have?", markdown=True)
    )

    asyncio.run(
        agent.aprint_response(
            "In what year did Manchester United change their name?", markdown=True
        )
    )
```

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno beautifulsoup4 openai pypdf wikipedia
```

**2. Export your OpenAI API key**

```bash Mac/Linux theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
```

```bash Windows theme={null}
$Env:OPENAI_API_KEY="your_openai_api_key_here"
```

**3. Clone Agno**

Clone the pinned Agno source and run the remaining commands from its root:

```bash theme={null}
git clone https://github.com/agno-agi/agno.git
cd agno
git checkout v3.0.4
```

**4. Start LightRAG**

Start a LightRAG server at `http://localhost:9621` before running the example. Set `LIGHTRAG_API_KEY` only if the server requires authentication.

**5. Run the example**

Run the example from the repository root:

```bash theme={null}
python cookbook/07_knowledge/05_integrations/rag/agentic_rag_with_lightrag.py
```

Full source: [cookbook/07\_knowledge/05\_integrations/rag/agentic\_rag\_with\_lightrag.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/07_knowledge/05_integrations/rag/agentic_rag_with_lightrag.py)


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