# Docling vs LandingAI Agentic Document Extraction (slim)

> LandingAI Agentic Document Extraction scores 69.1 (B) to Docling's 64.3 (B) for document parsing. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/docling-vs-landingai-agentic-document-extraction.md (~4,250 tokens) · this version ~1,830 tokens · JSON https://www.anchorterminal.com/compare/docling-vs-landingai-agentic-document-extraction.json · canonical https://www.anchorterminal.com/compare/docling-vs-landingai-agentic-document-extraction
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-07
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

LandingAI Agentic Document Extraction scores 69.1 (B) on agent readiness against Docling's 64.3 (B), and leads in 1 of 7 scored categories. Docling leads on security & auth and payments & pricing. Both do document parsing.

- Docling: B 64.3, rank #475 of 1486 · https://www.anchorterminal.com/tools/docling.min.md
- LandingAI Agentic Document Extraction: B 69.1, rank #258 of 1486 · https://www.anchorterminal.com/tools/landingai-agentic-document-extraction.min.md
- Docling, good for Self-hosted PDF and Office conversion to Markdown or JSON for RAG and agents that can't send documents out. Ahead on Security & auth, 53 against 43; Payments & pricing, 60 against 40. Also No key needed to call it; Runs on your own machine; Open source.
- LandingAI Agentic Document Extraction, good for Agents that need Markdown with block-level page coordinates and schema extraction with citations, in the US or EU. Ahead on Schema & documentation, 88 against 77. Also A hosted endpoint, with nothing to install; No incidents deducted, where Docling loses 8 points for them.

| Category | Docling | LandingAI Agentic Document Extraction |
| --- | --- | --- |
| Reliability (16%) | 85 | 85 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 77 | 88 |
| Agent ergonomics (13%) | 77 | 75 |
| Security & auth (14%) | 53 | 43 |
| Payments & pricing (10%) | 60 | 40 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 89 | 85 |
| Transparency & trust (7%) | 65 | 64 |

| Fact (where they differ) | Docling | LandingAI Agentic Document Extraction |
| --- | --- | --- |
| Kind | SDK + MCP | HTTP API |
| Vendor | Docling Project (LF AI & Data Foundation, started at IBM Research Zurich) | LandingAI, Inc. |
| Hosted endpoint | no (local only) | `https://api.ade.landing.ai` |
| Transports | stdio, Streamable HTTP, HTTP | HTTP |
| Auth | None | API key |
| Pricing | Free | Pay per use |
| Price for document parsing | free | $15 per 1,000 pages |
| Licence | MIT | Proprietary service under LandingAI's terms for Agentic Document Extraction. The Python and TypeScript libraries and the CLI are Apache-2.0, and the skills repository is MIT |
| Tools exposed | 25 | none |
| llms.txt | no | yes |
| MCP registry | `io.github.docling-project/docling-mcp` | not listed |
| Last release | 2026-11-05 | 2026-10-16 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no date given |
| Customer content may train models | not read yet | yes |
| Terms restrict automated access | not read yet | yes |
| Terms restrict benchmarking | not read yet | yes |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Popularity | 70k stars, 947k PyPI/wk | none |
| Agent reviews | none | 3.8/5 (4) |

Why the edge:
- Payments & pricing, Docling +20: Docling 60 (Self-hosted rule. Free MIT software with nothing to buy, so 20 + 20 + 20, and no payment protocol (0). 60.); LandingAI Agentic Document Extraction 40 (No x402, MPP or L402 (0). Credit price and per-page and per-character credit rates are public (20). 1,000 free credits on signup, and the pricing FAQ says no card is needed (20). A person signs up in a browser and copies the key from the web app (0).)
- Schema & documentation, LandingAI Agentic Document Extraction +11: Docling 77 (No OpenAPI file is published; docling-serve is a FastAPI app that serves its schema when run, and docling-mcp's tools carry typed inputs with Field descriptions from Pydantic (20 of 25). No llms.txt on the docs site; Markdown docs and AGENTS.md in the repository (5 of 10). MCP tool docstrings say what each tool returns and which other tool supplies its key, with little on when not to use one (14 of 20). Pydantic types with descriptions, few enums (12 of 15). Many runnable examples in docs/examples, with errors raised as Python exceptions that name the fix, such as OperationNotAllowed and unknown document keys listing valid ones (11 of 15). Changelog per release (15). 20+5+14+12+11+15 = 77.); LandingAI Agentic Document Extraction 88 (Public OpenAPI 3.1 description of the nine Gen2 operations, and a second for Gen1 (25). `llms.txt`, `llms-full.txt` and a Markdown twin of every page (10). The overview says when to use Parse, Extract and Ground, and a separate guide compares synchronous calls with jobs on each tier. Operation descriptions in the description file are short, and one list operation has none (15 of 20). Parameters carry defaults, enums and ranges, but the job status filter is a plain string, the description file declares no security scheme, and parse options travel as a form field (10 of 15). Code samples in cURL, Python and TypeScript, and troubleshooting pages for every status code and 422 message. The description file itself lists only 200, 202, 206, 404 and 422 (13 of 15). Versioned `/v2` path, dated model snapshots and a dated changelog with an RSS feed (15). We read the description file and Markdown twins, not the rendered reference.)
- Security & auth, Docling +10: Docling 53 (Framework reading. No credentials for local use; docling-serve has one optional shared key, off by default (20 of 30). Remote services are off unless explicitly enabled, local file fetch is guarded by flags, and MCP tools carry read-only and destructive annotations; manipulation tools can delete items and save files (12 of 20). Converted documents are untrusted text and no prompt-injection guidance was found (3 of 15). Logging only, no audit trail (3 of 15). A disclosure policy with private reporting, 28 advisories published in public with fixed versions, under the LF AI & Data Foundation; no bug bounty (15 of 20). 20+12+3+3+15 = 53. The advisories themselves are the deduction.); LandingAI Agentic Document Extraction 43 (Bearer key in a header, never in the URL. On the Explore plan there is one key that cannot be deleted or revoked, and a leaked key is replaced by emailing support. Team and Enterprise create several named keys and revoke them. No scopes on any plan (15 of 30, between the one-key and revocable-key lines). No read-only or restricted keys. Admin and Developer roles limit who may revoke keys, and an IP allowlist and SSO are Enterprise only (5 of 20). The API returns text from untrusted documents, and no prompt-injection guidance was found in the reviewed documentation (0 of 15). The Usage page logs every job with timestamp, API key, endpoint and credits, searchable and downloadable as CSV (10 of 15). The security page claims SOC 2 Type II, HIPAA with a BAA, third-party penetration tests and immutable audit logs. The libraries' SECURITY.md takes reports through GitHub private reporting. No security.txt and no bug bounty were found, and the trust centre could not be read (13 of 20).)