Docling
Docling is an open-source toolkit for parsing diverse document formats — PDF, DOCX, PPTX, XLSX, HTML, images, audio, LaTeX, plain text — into a unified, lossless DoclingDocument representation that downstream generative AI and RAG systems can consume directly. It pairs IBM Research's DocLayout and TableFormer models with the GraniteDocling visual language model and pluggable OCR engines, runs entirely locally for air-gapped use, and ships as a Python library and CLI, a FastAPI HTTP service (docling-serve), an MCP server (docling-mcp), and a Kubernetes operator. Originally created by IBM Research Zurich; now hosted by the LF AI and Data Foundation under the MIT license.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Docling the way a machine reads it — 49 machine-readable artifacts across 18 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Docling scores 48.3/100 (developing), with a separate agent-readiness read of 32/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Docling
Each block below is one kind of artifact we hold for Docling. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 18
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Docling MCP Server
Model Context Protocol server that exposes Docling document parsing as MCP tools so Claude, Cursor, Gemini, and other MCP-aware agents can convert PDFs, Office files, and images...
Docling Core Types
Canonical `DoclingDocument` data model and serialization primitives — text, tables, pictures, layout, hierarchy, bounding boxes, provenance — shared by the Docling library, Docl...
Docling Parse PDF Extractor
Native C++ PDF parsing engine used by Docling to extract text with precise coordinates from programmatic (non-scanned) PDF files. Distributed as a Python extension.
Docling IBM Models
Open-weight IBM Research models that power Docling's understanding pipeline — DocLayout (layout detection and reading order), TableFormer (table structure), code- and formula-re...
Docling Eval
End-to-end evaluation framework for document parsing models and services. Provides standard datasets and metrics for layout, tables, OCR, and reading-order quality so teams can ...
Docling Synthetic Data Generation
Tools for synthesizing labeled document data from real corpora — useful for fine-tuning layout, table, and reading-order models, and for stress-testing downstream RAG pipelines.
Docling Graph
Transform unstructured documents — once normalized to `DoclingDocument` — into validated, rich, queryable knowledge graphs. Intended for GraphRAG and entity-extraction workflows...
Docling Agent
Reference agent that reads, writes, and edits documents using Docling as the IO layer. Demonstrates how Docling output composes with tool-using LLMs to produce structured edits.
Docling Kubernetes Operator
Go-based Kubernetes operator that deploys and manages Docling Serve workloads — model cache PVCs, GPU/CPU pools, RQ workers, replica sets with sticky sessions, OAuth — from a si...
Docling Java Bindings
A Java API for Docling that lets JVM applications call into the Docling pipeline. Complementary to `docling4j`, which targets Java-native document understanding integrations.
Docling4j
Brings Docling document understanding into Java projects with idiomatic Java APIs over the Docling serialization format.
Docling TypeScript
TypeScript/JavaScript types and helpers for consuming Docling output (DoclingDocument JSON, DocTags) in Node.js and browser applications.
Docling LangChain Integration
First-party LangChain document loader and chunker for Docling. Drops Docling output directly into LangChain retrieval pipelines.
Docling Jobkit
Shared job-runner primitives used by Docling Serve and the Docling Operator to dispatch conversion work across RQ workers and Ray.
Docling Async API
Asynchronous conversion submission.
Docling Convert API
Document conversion operations.
Docling System API
Health and metadata.
Docling Tasks API
Task status, results, and streaming.
Scroll within the panel for all 18 ·
Open Collections 2
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Docling CLI as REST
OPEN COLLECTIONDocling Serve REST API
OPEN COLLECTIONFeatures 19
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Scroll within the panel for all 19 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Docling Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Docling API Rules
SPECTRALDocling API Rules
SPECTRALJSON Schema 2
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
DoclingConvertRequest
JSON SCHEMADoclingDocument
JSON SCHEMAJSON Structure 1
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Docling Document Structure
JSON STRUCTUREExamples 3
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Docling — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 2
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 10
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 10 ·
Operate 4
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
Other 8
Properties that don't map to a standard resource type
Scroll within the panel for all 8 ·
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