> ## 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.

# Markdown Chunking

> Split Markdown documents by heading structure or an approximate character target.

`MarkdownChunking` returns content at or below `chunk_size` as one chunk in its default mode. For longer content, it uses Unstructured to partition Markdown and group the resulting elements toward `chunk_size`. Set `split_on_headings=True` to split on every ATX heading from H1 through H6. Set an integer from `1` to `6` to split on ATX headings through that level.

<Steps>
  <Step title="Create a Python file">
    ```python markdown_chunking.py theme={null}
    from agno.agent import Agent
    from agno.knowledge.chunking.markdown import MarkdownChunking
    from agno.knowledge.knowledge import Knowledge
    from agno.knowledge.reader.markdown_reader import MarkdownReader
    from agno.vectordb.pgvector import PgVector

    db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

    knowledge = Knowledge(
        vector_db=PgVector(table_name="recipes_markdown_chunking", db_url=db_url),
    )

    knowledge.insert(
        url="https://raw.githubusercontent.com/agno-agi/agno/v2.7.2/README.md",
        reader=MarkdownReader(
            name="Markdown Chunking Reader",
            chunking_strategy=MarkdownChunking(chunk_size=1000),
        ),
    )

    agent = Agent(
        knowledge=knowledge,
        search_knowledge=True,
    )

    agent.print_response("What is Agno?", markdown=True)
    ```
  </Step>

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno sqlalchemy psycopg pgvector "unstructured<0.18.31" markdown openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <Snippet file="set-openai-key.mdx" />
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

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

## Choose a Mode

| `split_on_headings` | Behavior                                                                              |
| ------------------- | ------------------------------------------------------------------------------------- |
| `False`             | Keep short input as one chunk; partition and group longer input with Unstructured     |
| `True`              | Start a chunk at every ATX heading from H1 through H6, regardless of document size    |
| `1` to `6`          | Start a chunk at ATX headings through the selected level, regardless of document size |

In heading mode, sections longer than `chunk_size` are split by paragraphs, then by words, and repeat an existing heading. A long indivisible word or prepended overlap can make the final chunk longer than `chunk_size`. Overlap is prepended without an added separator.

## Markdown Chunking Params

<Snippet file="chunking-markdown.mdx" />

## Developer Resources

* [Chunking overview](/knowledge/concepts/chunking/overview)
* [Chunking strategies examples](/examples/knowledge/building-blocks/chunking-strategies)
* [Markdown reader](/knowledge/concepts/readers/markdown-reader)