EU region: Set the base URL to api.cloud.eu.llamaindex.ai. In the Python SDK: LlamaCloud(api_key="llx-...", base_url="api.cloud.eu.llamaindex.ai"). See Regions.
BYOC: Set the base URL to your custom deployment endpoint. Ensure your OIDC provider is configured correctly (audience, scopes, SSL).
API keys are region-specific — a key created in NA cannot be used with the EU endpoint.
Complex documents with many images take longer, especially with OCR.
Check job status via the API or dashboard.
If using Agentic or Agentic Plus tiers, try Cost-effective (3 credits/page) first — it still uses AI and produces markdown, but processes faster.
Use target_pages (e.g., "1,3,5-8") to parse only the pages you need instead of the full document.
Disable features you don’t need (e.g., image extraction, specialized chart parsing) to speed up processing.
Poor parsing quality or missing content
Upgrade tier gradually: Cost-effective (3 credits) → Agentic (10 credits) → Agentic Plus (45 credits). Each tier uses more sophisticated AI models. Note: Fast tier (1 credit) outputs spatial text only, not markdown.
Add layout extraction (+3 credits/page) for documents with columns, complex headers, or mixed structure.
Use parsing instructions (parsing_instruction in v1, or agentic_options.system_prompt / user_prompt in v2) to guide the model. This only applies to AI-powered tiers (not Fast).
For scanned documents, ensure image quality is sufficient for OCR. You can specify OCR language via processing_options.ocr_parameters.languages in v2.
Content beyond 64 KB per page is silently truncated. Only the 35 largest images per page are processed. If your document hits these limits, content will be missing without an error. See Limitations.
Parse job timed out
By default, parse jobs have no explicit application-level timeout — they run until completion. You can set timeouts via processing_control.timeouts in v2 (max: 30 min base + 5 min per page).
Use target_pages to reduce the number of pages processed.
Use Cost-effective tier instead of Agentic/Agentic Plus for faster processing with markdown output.
Disable unnecessary features (image extraction, chart parsing) to reduce processing time.
Review your schema — field names and descriptions guide the model’s extraction. Be specific and unambiguous in descriptions.
Verify your extraction target (PER_DOC, PER_PAGE, PER_TABLE_ROW) matches your document structure.
If using Fast or Balanced mode, try Multimodal or Premium for visually complex documents. The default mode is Premium (highest accuracy).
Check that the document actually contains the data you’re trying to extract.
For pre-parsed files, extraction uses the cached parse result — if the parse quality was poor, extraction will be too.
The root of your schema must be type: object with properties.
Schema validation error
The schema root must be type: object with properties.
Default values (other than null) are not supported.
There are limits on total properties, nesting depth, character counts, and raw schema size. See Schema design and restrictions for the full list of constraints.
Write specific, detailed rule descriptions (10-2000 characters). Precision matters more than length — include examples of what should and shouldn’t match within the description.
Multimodal mode (2 credits/page) uses vision models on page screenshots instead of parsed text, which helps when visual layout matters. Note: Multimodal is only available in the v1 classify API.
Ensure rule types are distinct — overlapping or vague descriptions cause ambiguity.
Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/for-agents/mcp/
- Other LlamaIndex tooling for agents — the LlamaParse Platform MCP server, agent skills and plugins, and the n8n node — is mapped at https://developers.llamaindex.ai/for-agents/