> Source: https://txtfetch.com/migrate/llamaparse
> Plain-text twin — every page on txtfetch.com has one. https://txtfetch.com/text

---

# Migrate from LlamaParse to txtfetch

Credit-metered tiers and a job to poll, for parsing you may not need. Here's what your code looks like before and after.

## Where you are today

The call below is the current llama-cloud Parsing API, checked against LlamaParse's current docs.

Python

```python
import os

from llama_cloud import LlamaCloud

client = LlamaCloud(api_key=os.environ["LLAMA_CLOUD_API_KEY"])

file = client.files.create(file="report.pdf", purpose="parse")
result = client.parsing.parse(
    file_id=file.id,
    tier="agentic",
    version="latest",
    expand=["markdown"],
)

text = result.markdown.pages[0].markdown
```

Checked against [LlamaParse's docs](https://developers.llamaindex.ai/python/cloud/llamaparse/getting_started) on 2026-09-10, using llama-cloud 2.16.0.

the call that replaces it

One HTTP call. No SDK to install, and no job to poll.

curl

```curl
curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@report.pdf
```

Python

```python
import os

import requests

with open("report.pdf", "rb") as f:
    r = requests.post(
        "https://api.txtfetch.com/v1/extract",
        headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
        files={"file": f},
    )

print(r.json()["extracted_text"])
```

JavaScript

```javascript
import { readFile } from "node:fs/promises";

const file = new Blob([await readFile("report.pdf")]);
const form = new FormData();
form.append("file", file, "report.pdf");

const res = await fetch("https://api.txtfetch.com/v1/extract", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
  body: form,
});

const { extracted_text } = await res.json();
console.log(extracted_text);
```

drop-in adapter

A short function standing in for `result.markdown.pages[n].markdown`. Change one function, not your whole pipeline.

Python

```python
import os

import requests


def parse_like_pages(file_path: str) -> list[str]:
    """Drop-in swap for result.markdown.pages[n].markdown.

    txtfetch has no per-page split, so this returns a one-item list.
    Downstream code that joins all pages into one string keeps working
    unmodified; code that indexes a specific page needs a rewrite.
    """
    with open(file_path, "rb") as f:
        r = requests.post(
            "https://api.txtfetch.com/v1/extract",
            headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
            files={"file": f},
        )
    r.raise_for_status()
    return [r.json()["extracted_text"]]
```

JavaScript

```javascript
import { readFile } from "node:fs/promises";

// Drop-in swap for result.markdown.pages[n].markdown. txtfetch has no
// per-page split, so this returns a one-item array.
async function parseLikePages(filePath) {
  const file = new Blob([await readFile(filePath)]);
  const form = new FormData();
  form.append("file", file, filePath);

  const res = await fetch("https://api.txtfetch.com/v1/extract", {
    method: "POST",
    headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
    body: form,
  });

  const { extracted_text } = await res.json();
  return [extracted_text];
}
```

field mapping

| LlamaParse field | txtfetch equivalent | Note |
| --- | --- | --- |
| result.markdown.pages\[n\].markdown | extracted\_text (?format=markdown) | Ask for markdown output to keep heading and list structure. LlamaParse splits by page; txtfetch does not. |
| result.items (per-element JSON) | ?format=json element list | txtfetch's element JSON is coarser than LlamaParse's agentic-tier item breakdown. |
| tier (fast / cost\_effective / agentic / agentic\_plus) | not applicable | txtfetch runs one extraction path. There is no fidelity dial to set per request. |
| take\_screenshot=True (page images) | not returned | txtfetch returns text only, never a page image. |

what you lose

-   The agentic and agentic-plus tiers' fidelity on multi-column layouts, embedded tables, and math notation.
-   Per-page screenshots and structured item JSON.
-   A pinnable parser version for reproducible output across runs.
-   output\_tables\_as\_HTML and other tier-specific output options.

See the full capability table on [txtfetch vs LlamaParse](https://txtfetch.com/compare/llamaparse).

what you gain

-   Hundreds of formats through one endpoint (see /formats/coverage), not a PDF-first parser.
-   One flat call, no credits to track and no job tier to pick per request.
-   Billing per document, not per credit. See the pricing math.

The pricing math behind per-document billing lives on [why per-page pricing punishes long documents](https://txtfetch.com/compare/per-page-pricing).

## Cutover

Run both calls on the same PDF while you switch over. Diff the two outputs, or clean up formatting differences first on the pages where LlamaParse's fidelity mattered most.

-   [Compare outputs side by side](https://txtfetch.com/diff), to dual-run both calls on the same file.
-   [Clean up extracted text](https://txtfetch.com/tools/clean-extracted-text), if the two outputs disagree on formatting.

sources

-   [LlamaIndex: LlamaParse getting started (llama-cloud)](https://developers.llamaindex.ai/python/cloud/llamaparse/getting_started) Accessed 2026-09-10
-   [PyPI: llama-cloud](https://pypi.org/project/llama-cloud/) Accessed 2026-09-10
-   [PyPI: llama-cloud-services (deprecated May 1, 2026)](https://pypi.org/project/llama-cloud-services/) Accessed 2026-09-10 directional

frequently asked questions

**Do I need the agentic tier's fidelity, or is plain text enough?**: Depends on the document. If your pipeline only ever reads plain text out of LlamaParse's markdown, txtfetch is a straight swap. If it reads structured items or table HTML, keep LlamaParse for those files.

**Does txtfetch support LlamaParse's parsing tiers?**: No. txtfetch runs one extraction path per document. There is no fast/agentic/agentic_plus tier to choose.

**What happened to the llama-cloud-services package?**: LlamaIndex deprecated it in favor of llama-cloud. The snippet above uses the current package, checked against LlamaIndex's own docs.

other migrations

-   [Migrate from Unstructured.io →](https://txtfetch.com/migrate/unstructured)
-   [Migrate from AWS Textract →](https://txtfetch.com/migrate/aws-textract)
-   [Migrate from Azure AI Document Intelligence →](https://txtfetch.com/migrate/azure-document-intelligence)
-   [Migrate from Mindee →](https://txtfetch.com/migrate/mindee)
-   [All migrations →](https://txtfetch.com/migrate)

## Swap the call. Keep the pipeline.

Point your LlamaParse code at txtfetch, then diff the output before you cut over.

[Get an API key →](https://app.txtfetch.com/signup)

[See the migration guide →](https://txtfetch.com/migrate)