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

# Quickstart

> Build a knowledge-powered agent in under 5 minutes.

Build an agent that answers questions about your documents.

## Create an Agent with Knowledge

```python knowledge_agent.py theme={null}
from agno.agent import Agent
from agno.knowledge.embedder.google import GeminiEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.google import Gemini
from agno.vectordb.chroma import ChromaDb
from agno.vectordb.search import SearchType

# Create a knowledge base with ChromaDB
knowledge = Knowledge(
    vector_db=ChromaDb(
        collection="docs",
        path="tmp/chromadb",
        persistent_client=True,
        search_type=SearchType.hybrid,
        embedder=GeminiEmbedder(id="gemini-embedding-001"),
    ),
)

# Load content into the knowledge base
knowledge.insert(url="https://docs.agno.com/introduction.md", skip_if_exists=True)

# Create an agent that searches the knowledge base
agent = Agent(
    model=Gemini(id="gemini-3-flash-preview"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

agent.print_response("What is Agno?", stream=True)
```

## Setup

<Steps>
  <Step title="Create virtual environment">
    <CodeGroup>
      ```bash Mac theme={null}
      uv venv --python 3.12
      source .venv/bin/activate
      ```

      ```bash Windows theme={null}
      uv venv --python 3.12
      .venv\Scripts\activate
      ```
    </CodeGroup>
  </Step>

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

  <Step title="Export your API key">
    <CodeGroup>
      ```bash Mac theme={null}
      export GOOGLE_API_KEY=your-google-api-key
      ```

      ```bash Windows theme={null}
      setx GOOGLE_API_KEY your-google-api-key
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the agent">
    ```bash theme={null}
    python knowledge_agent.py
    ```
  </Step>
</Steps>

The agent searches the knowledge base, finds relevant content, and answers based on what it found.

## Load Different Content Types

<Tabs>
  <Tab title="Files">
    ```python theme={null}
      knowledge.insert(path="docs/product-guide.pdf")
      knowledge.insert(path="data/")  # Entire directory
    ```
  </Tab>

  <Tab title="URLs">
    ```python theme={null}
      knowledge.insert(url="https://example.com/docs.pdf")
    ```
  </Tab>

  <Tab title="Text">
    ```python theme={null}
      knowledge.insert(text_content="Your content here...")
    ```
  </Tab>
</Tabs>

Agno detects file types automatically and uses the appropriate reader for PDFs, DOCX, CSV, Markdown, and more.

## What's Happening

1. **Insert**: Content is chunked, embedded with Gemini, and stored in ChromaDB
2. **Query**: The agent receives your question and decides to search the knowledge base using the `search_knowledge_base` tool
3. **Response**: The agent uses the retrieved content to answer, grounding its response in your data

This is **Agentic RAG**. The agent decides when to search rather than blindly injecting context on every query.

## Next Steps

<CardGroup cols={2}>
  <Card title="Agents with Knowledge" icon="robot" href="/knowledge/agents/overview">
    Agentic RAG, traditional RAG, reranking
  </Card>

  <Card title="Teams with Knowledge" icon="users" href="/knowledge/teams/overview">
    Distributed search, coordinated RAG
  </Card>

  <Card title="Vector Stores" icon="database" href="/knowledge/vector-stores/pgvector/overview">
    PgVector, Pinecone, Weaviate, and 20+ more
  </Card>

  <Card title="Search & Retrieval" icon="magnifying-glass" href="/knowledge/concepts/search-and-retrieval/overview">
    Vector, keyword, and hybrid search
  </Card>
</CardGroup>