llama-parse
Parse files into RAG-Optimized formats.
Decision gist · record as of 2026-08-14
Yes, but with a strong caveat: the package is deprecated and will be maintained only until May 1, 2026. If you need a document parser for RAG workflows today and can commit to migrating to llama-cloud>=1.0 before that date, it works well and has low install friction. For new projects with longer horizons, migrate directly to the recommended llama-cloud package instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a valid LLAMA_CLOUD_API_KEY from https://cloud.llamaindex.ai/api-key; free tier limited to 1000 pages per day.
- Low install friction with a single runtime dependency (llama-cloud-services).
- However, the package is deprecated as of the description and will be maintained only until May 1, 2026; users are advised to migrate to llama-cloud>=1.0 for ongoing support and active development.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without licensing concerns.
last release 2026-02-13 (182 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 94,374,576 downloads/mo, #360 on PyPI
Alternatives
Verify before relying
pip install llama-parse
from llama_parse import LlamaParse
parser = LlamaParse(
api_key="llx-...",
result_type="markdown"
)
documents = parser.load_data("./my_file.pdf")- Whether the deprecation timeline (May 1, 2026) affects your project's long-term maintenance needs.
- Specific performance or accuracy metrics for table and image extraction compared to alternatives.
- Pricing details for production use beyond the free tier (0.3c per additional page mentioned but not fully specified).
What it is and what it does
LlamaParse is a document parsing service that converts unstructured files (PDFs, Word, Excel, PowerPoint, HTML) into structured formats suitable for RAG pipelines and LLM consumption. It specializes in handling complex layouts, embedded tables, and visual elements, extracting them into text, markdown, or JSON representations. The parser can accept custom instructions to shape output and supports batch processing across multiple files.
The package wraps a cloud API and integrates directly with LlamaIndex workflows. It offers both synchronous and asynchronous parsing methods, file path or file object input, and can be plugged into LlamaIndex's SimpleDirectoryReader for seamless directory-level document loading. A free tier provides 1000 pages per day; paid tiers offer higher volume with per-page pricing.
Use it for
- Build RAG systems that ingest PDFs and complex documents by parsing them into clean markdown or text for vector embedding.
- Extract tables from financial reports, spreadsheets, or presentations into structured formats for downstream analysis.
- Batch-process large document collections with custom parsing rules to standardize output for LLM fine-tuning or retrieval.
- Integrate document parsing into LlamaIndex pipelines using SimpleDirectoryReader with LlamaParse as the PDF handler.
- Parse multimodal documents (PDFs with diagrams, images, tables) and extract visual elements as structured chunks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, but with a strong caveat: the package is deprecated and will be maintained only until May 1, 2026.
If you need a document parser for RAG workflows today and can commit to migrating to llama-cloud>=1.0 before that date, it works well and has low install friction. For new projects with longer horizons, migrate directly to the recommended llama-cloud package instead.
Install
llama-parse on PyPI
Before you install
Low install friction with a single runtime dependency (llama-cloud-services). However, the package is deprecated as of the description and will be maintained only until May 1, 2026; users are advised to migrate to llama-cloud>=1.0 for ongoing support and active development.
Requires a valid LLAMA_CLOUD_API_KEY from https://cloud.llamaindex.ai/api-key; free tier limited to 1000 pages per day.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without licensing concerns.
Quickstart
pip install llama-parse
from llama_parse import LlamaParse
parser = LlamaParse(
api_key="llx-...",
result_type="markdown"
)
documents = parser.load_data("./my_file.pdf")Verify before relying
- Whether the deprecation timeline (May 1, 2026) affects your project's long-term maintenance needs.
- Specific performance or accuracy metrics for table and image extraction compared to alternatives.
- Pricing details for production use beyond the free tier (0.3c per additional page mentioned but not fully specified).
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagellama-cloud-services |
| Maintenance | Aging 182 days since the last release |
| First released | |
| Downloads | 94,374,576 / month, #360 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_parse-0.6.94-py3-none-any.whl
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See also llama-index-readers-llama-parse · llama-cloud-services · llama-cloud · llama-index-readers-file · llama-index · pymupdf4llm · llama-index-indices-managed-llama-cloud · liteparse · unstructured · llama-index-retrievers-bm25