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

# Graph RAG: LightRAG Integration

> LightRAG is a managed knowledge backend that builds a knowledge graph from your documents.

LightRAG is a managed knowledge backend that builds a knowledge graph from your documents. It handles its own ingestion and retrieval, providing graph-based RAG capabilities.

```python graph_rag.py theme={null}
"""
Graph RAG: LightRAG Integration
=================================
LightRAG is a managed knowledge backend that builds a knowledge graph
from your documents. It handles its own ingestion and retrieval,
providing graph-based RAG capabilities.

Unlike standard vector-based RAG, LightRAG:
- Extracts entities and relationships from documents
- Builds a knowledge graph for multi-hop reasoning
- Supports graph-traversal queries

Requirements: pip install lightrag-agno
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

try:
    from agno.vectordb.lightrag import LightRag

    knowledge = Knowledge(
        vector_db=LightRag(
            server_url="http://localhost:9621",
        ),
    )

    agent = Agent(
        model=OpenAIResponses(id="gpt-5.2"),
        knowledge=knowledge,
        search_knowledge=True,
        markdown=True,
    )

except ImportError:
    knowledge = None
    agent = None
    print("LightRAG not installed. Run: pip install lightrag-agno")

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        if knowledge and agent:
            await knowledge.ainsert(
                url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
            )

            print("\n" + "=" * 60)
            print("Graph RAG: knowledge graph-based retrieval")
            print("=" * 60 + "\n")

            agent.print_response(
                "What ingredients are commonly shared across Thai recipes?",
                stream=True,
            )

    asyncio.run(main())
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```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"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `graph_rag.py`, then run:

    ```bash theme={null}
    python graph_rag.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/04\_advanced/03\_graph\_rag.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/04_advanced/03_graph_rag.py)