Docling

aid: docling
url: https://raw.githubusercontent.com/api-evangelist/docling/refs/heads/main/apis.yml
apis:
  - aid: docling:docling-python-library
    name: Docling Python Library
    tags:
      - Documents
      - Parsing
      - Python
      - SDK
      - PDF
      - OCR
      - LLM
      - RAG
    humanURL: https://docling-project.github.io/docling/
    properties:
      - url: https://docling-project.github.io/docling/
        type: Documentation
      - url: https://docling-project.github.io/docling/getting_started/quickstart/
        type: GettingStarted
      - url: https://github.com/docling-project/docling
        type: SourceCode
      - url: https://pypi.org/project/docling/
        type: SDK
      - url: openapi/docling-cli-openapi.yml
        type: OpenAPI
    description: >-
      The core Docling Python library and `docling` CLI. Parses PDFs, DOCX, PPTX, XLSX, HTML, images (PNG/TIFF/JPEG),
      audio (WAV/MP3), WebVTT, LaTeX, and plain text into a unified `DoclingDocument` representation that can be
      exported to Markdown, HTML, lossless JSON, DocTags, and WebVTT. Implements advanced PDF understanding — page
      layout, reading order, table structure (TableFormer), code and formula recognition, picture classification — plus
      OCR (EasyOCR, Tesseract, RapidOCR, Mac OCR) and the GraniteDocling visual language model pipeline. Runs locally
      for air-gapped and sensitive-data use.
  - aid: docling:docling-serve-rest-api
    name: Docling Serve REST API
    tags:
      - Documents
      - Parsing
      - REST
      - PDF
      - OCR
      - Async
      - WebSocket
    humanURL: https://github.com/docling-project/docling-serve
    properties:
      - url: https://github.com/docling-project/docling-serve
        type: Documentation
      - url: https://raw.githubusercontent.com/docling-project/docling-serve/main/docs/usage.md
        name: Docling Serve Usage
        type: Documentation
      - url: https://github.com/docling-project/docling-serve
        type: SourceCode
      - url: openapi/docling-serve-openapi.yml
        type: OpenAPI
      - url: json-schema/docling-document-schema.json
        type: JSONSchema
      - url: json-schema/docling-convert-request-schema.json
        type: JSONSchema
      - url: json-ld/docling-context.jsonld
        type: JSONLD
    description: >-
      Docling Serve exposes the Docling pipeline as an HTTP service. Synchronous endpoints `POST /v1/convert/source` and
      `POST /v1/convert/file` accept URL- or upload-sourced documents and return converted JSON, Markdown, HTML, or a
      zipped bundle. Asynchronous variants (`/v1/convert/source/async`, `/v1/convert/file/async`) return a task handle
      that can be polled at `/v1/status/poll/{task_id}`, streamed via WebSocket `/v1/status/ws/{task_id}`, and retrieved
      at `/v1/result/{task_id}`. Container images ship CPU, CUDA 12.8/13.0, and AMD ROCm 6.3 variants; Kubernetes
      deployment is supported via the Docling Operator.
  - aid: docling:docling-mcp-server
    name: Docling MCP Server
    tags:
      - MCP
      - Agents
      - Documents
      - Parsing
    humanURL: https://github.com/docling-project/docling-mcp
    properties:
      - url: https://github.com/docling-project/docling-mcp
        type: Documentation
      - url: https://github.com/docling-project/docling-mcp
        type: SourceCode
    description: >-
      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 into structured `DoclingDocument` output without
      bespoke integration code.
  - aid: docling:docling-core
    name: Docling Core Types
    tags:
      - Documents
      - Schema
      - Python
      - SDK
    humanURL: https://github.com/docling-project/docling-core
    properties:
      - url: https://github.com/docling-project/docling-core
        type: Documentation
      - url: https://github.com/docling-project/docling-core
        type: SourceCode
      - url: https://pypi.org/project/docling-core/
        type: SDK
    description: >-
      Canonical `DoclingDocument` data model and serialization primitives — text, tables, pictures, layout, hierarchy,
      bounding boxes, provenance — shared by the Docling library, Docling Serve, the Java port, and the TypeScript
      bindings.
  - aid: docling:docling-parse
    name: Docling Parse PDF Extractor
    tags:
      - PDF
      - Parsing
      - C++
    humanURL: https://github.com/docling-project/docling-parse
    properties:
      - url: https://github.com/docling-project/docling-parse
        type: Documentation
      - url: https://github.com/docling-project/docling-parse
        type: SourceCode
    description: >-
      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.
  - aid: docling:docling-ibm-models
    name: Docling IBM Models
    tags:
      - AI
      - Documents
      - Layout
      - TableFormer
      - VLM
    humanURL: https://github.com/docling-project/docling-ibm-models
    properties:
      - url: https://github.com/docling-project/docling-ibm-models
        type: Documentation
      - url: https://github.com/docling-project/docling-ibm-models
        type: SourceCode
    description: >-
      Open-weight IBM Research models that power Docling's understanding pipeline — DocLayout (layout detection and
      reading order), TableFormer (table structure), code- and formula-recognition heads, picture classifier, and
      GraniteDocling-258M VLM. Distributed through Hugging Face.
  - aid: docling:docling-eval
    name: Docling Eval
    tags:
      - Evaluation
      - Documents
      - Benchmarks
    humanURL: https://github.com/docling-project/docling-eval
    properties:
      - url: https://github.com/docling-project/docling-eval
        type: Documentation
      - url: https://github.com/docling-project/docling-eval
        type: SourceCode
    description: >-
      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 benchmark Docling — and competing parsers — apples
      to apples.
  - aid: docling:docling-sdg
    name: Docling Synthetic Data Generation
    tags:
      - Synthetic Data
      - Training
      - Documents
    humanURL: https://github.com/docling-project/docling-sdg
    properties:
      - url: https://github.com/docling-project/docling-sdg
        type: Documentation
      - url: https://github.com/docling-project/docling-sdg
        type: SourceCode
    description: >-
      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.
  - aid: docling:docling-graph
    name: Docling Graph
    tags:
      - Knowledge Graph
      - RAG
      - Documents
    humanURL: https://github.com/docling-project/docling-graph
    properties:
      - url: https://github.com/docling-project/docling-graph
        type: Documentation
      - url: https://github.com/docling-project/docling-graph
        type: SourceCode
    description: >-
      Transform unstructured documents — once normalized to `DoclingDocument` — into validated, rich, queryable
      knowledge graphs. Intended for GraphRAG and entity-extraction workflows on top of Docling output.
  - aid: docling:docling-agent
    name: Docling Agent
    tags:
      - Agents
      - Documents
      - LLM
    humanURL: https://github.com/docling-project/docling-agent
    properties:
      - url: https://github.com/docling-project/docling-agent
        type: Documentation
      - url: https://github.com/docling-project/docling-agent
        type: SourceCode
    description: >-
      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.
  - aid: docling:docling-operator
    name: Docling Kubernetes Operator
    tags:
      - Kubernetes
      - Operator
      - Documents
    humanURL: https://github.com/docling-project/docling-operator
    properties:
      - url: https://github.com/docling-project/docling-operator
        type: Documentation
      - url: https://github.com/docling-project/docling-operator
        type: SourceCode
    description: >-
      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 single CR.
  - aid: docling:docling-java
    name: Docling Java Bindings
    tags:
      - Java
      - SDK
    humanURL: https://github.com/docling-project/docling-java
    properties:
      - url: https://github.com/docling-project/docling-java
        type: Documentation
      - url: https://github.com/docling-project/docling-java
        type: SourceCode
    description: >-
      A Java API for Docling that lets JVM applications call into the Docling pipeline. Complementary to `docling4j`,
      which targets Java-native document understanding integrations.
  - aid: docling:docling4j
    name: Docling4j
    tags:
      - Java
      - SDK
    humanURL: https://github.com/docling-project/docling4j
    properties:
      - url: https://github.com/docling-project/docling4j
        type: Documentation
      - url: https://github.com/docling-project/docling4j
        type: SourceCode
    description: >-
      Brings Docling document understanding into Java projects with idiomatic Java APIs over the Docling serialization
      format.
  - aid: docling:docling-ts
    name: Docling TypeScript
    tags:
      - TypeScript
      - JavaScript
      - SDK
    humanURL: https://github.com/docling-project/docling-ts
    properties:
      - url: https://github.com/docling-project/docling-ts
        type: Documentation
      - url: https://github.com/docling-project/docling-ts
        type: SourceCode
    description: >-
      TypeScript/JavaScript types and helpers for consuming Docling output (DoclingDocument JSON, DocTags) in Node.js
      and browser applications.
  - aid: docling:docling-langchain
    name: Docling LangChain Integration
    tags:
      - LangChain
      - RAG
      - Documents
    humanURL: https://github.com/docling-project/docling-langchain
    properties:
      - url: https://github.com/docling-project/docling-langchain
        type: Documentation
      - url: https://github.com/docling-project/docling-langchain
        type: SourceCode
    description: >-
      First-party LangChain document loader and chunker for Docling. Drops Docling output directly into LangChain
      retrieval pipelines.
  - aid: docling:docling-jobkit
    name: Docling Jobkit
    tags:
      - Jobs
      - Async
      - Documents
    humanURL: https://github.com/docling-project/docling-jobkit
    properties:
      - url: https://github.com/docling-project/docling-jobkit
        type: Documentation
      - url: https://github.com/docling-project/docling-jobkit
        type: SourceCode
    description: >-
      Shared job-runner primitives used by Docling Serve and the Docling Operator to dispatch conversion work across RQ
      workers and Ray.
name: Docling
tags:
  - Documents
  - Parsing
  - PDF
  - OCR
  - Layout
  - Tables
  - RAG
  - LLM
  - Open Source
  - IBM Research
  - LF AI and Data
  - MCP
  - Knowledge Graph
  - Generative AI
kind: contract
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
access: Open Source
common:
  - type: Portal
    url: https://docling-project.github.io/docling/
  - type: Documentation
    url: https://docling-project.github.io/docling/
  - type: GettingStarted
    url: https://docling-project.github.io/docling/getting_started/quickstart/
  - type: SourceCode
    url: https://github.com/docling-project/docling
  - type: GitHubOrganization
    url: https://github.com/docling-project
  - type: License
    url: https://github.com/docling-project/docling/blob/main/LICENSE
  - type: SDK
    url: https://pypi.org/project/docling/
    name: docling on PyPI
  - type: SDK
    url: https://pypi.org/project/docling-core/
    name: docling-core on PyPI
  - type: SDK
    url: https://pypi.org/project/docling-serve/
    name: docling-serve on PyPI
  - type: SDK
    url: https://github.com/docling-project/docling-java
    name: Java bindings
  - type: SDK
    url: https://github.com/docling-project/docling4j
    name: Docling4j
  - type: SDK
    url: https://github.com/docling-project/docling-ts
    name: TypeScript / JavaScript
  - type: CLI
    url: https://docling-project.github.io/docling/reference/cli/
  - type: ReleaseNotes
    url: https://github.com/docling-project/docling/releases
  - type: ChangeLog
    url: https://github.com/docling-project/docling/blob/main/CHANGELOG.md
  - type: Issues
    url: https://github.com/docling-project/docling/issues
  - type: Discussions
    url: https://github.com/docling-project/docling/discussions
  - type: ContributionGuide
    url: https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md
  - type: CodeOfConduct
    url: https://github.com/docling-project/docling/blob/main/CODE_OF_CONDUCT.md
  - type: Governance
    url: https://lfaidata.foundation/projects/docling/
    name: LF AI and Data Foundation project page
  - type: Foundation
    url: https://lfaidata.foundation/
    name: LF AI and Data Foundation
  - type: Models
    url: https://huggingface.co/ds4sd
    name: IBM DS4SD on Hugging Face
  - type: Models
    url: https://huggingface.co/ibm-granite/granite-docling-258M
    name: GraniteDocling-258M
  - type: Blog
    url: https://research.ibm.com/blog/docling-generative-AI
    name: IBM Research blog — Docling
  - type: AcademicPaper
    url: https://arxiv.org/abs/2408.09869
    name: Docling Technical Report
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/langchain/
    name: LangChain
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/llamaindex/
    name: LlamaIndex
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/haystack/
    name: Haystack
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/crewai/
    name: Crew AI
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/txtai/
    name: txtai
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/spacy/
    name: spaCy
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/apify/
    name: Apify
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/nvidia/
    name: NVIDIA NIM / NeMo Retriever
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/instructlab/
    name: InstructLab
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/bee/
    name: Bee Agent Framework
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/weaviate/
    name: Weaviate
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/qdrant/
    name: Qdrant
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/milvus/
    name: Milvus
  - type: Integration
    url: https://docling-project.github.io/docling/integrations/opensearch/
    name: OpenSearch
  - type: ContainerImage
    url: https://quay.io/repository/docling-project/docling-serve
    name: docling-serve container (Quay)
  - type: ContainerImage
    url: https://github.com/docling-project/docling-serve/pkgs/container/docling-serve
    name: docling-serve container (GHCR)
  - type: KubernetesOperator
    url: https://github.com/docling-project/docling-operator
  - type: Features
    data:
      - Parses PDF, DOCX, PPTX, XLSX, HTML, PNG/TIFF/JPEG, WAV/MP3, WebVTT, LaTeX, and plain text
      - Unified DoclingDocument representation with lossless JSON, Markdown, HTML, DocTags, and WebVTT exports
      - Advanced PDF understanding — page layout, reading order, table structure, code, formulas, image classification
      - TableFormer model for accurate table structure recognition
      - GraniteDocling-258M visual language model pipeline for image-first document understanding
      - OCR engines — EasyOCR, Tesseract, RapidOCR, Mac OCR — with per-language configuration
      - Automatic Speech Recognition (ASR) for audio inputs (WAV, MP3) producing WebVTT
      - Local, air-gapped execution — no data leaves the host
      - MCP server (docling-mcp) exposes parsing as agent tools for Claude, Cursor, Gemini and other clients
      - Docling Serve HTTP API with sync and async endpoints, WebSocket task streaming, and zip-bundle output
      - >-
        Kubernetes-native deployment via the Docling Operator (model-cache PVCs, RQ workers, GPU pools, OAuth, sticky
        sessions)
      - Plug-and-play integrations with LangChain, LlamaIndex, Haystack, Crew AI, txtai, Bee, spaCy
      - Application-specific XML schemas (USPTO, JATS, XBRL)
      - Knowledge-graph extraction via docling-graph
      - Synthetic data generation via docling-sdg for fine-tuning
      - End-to-end evaluation framework (docling-eval) with standard datasets and metrics
      - Java, Java-native, TypeScript, and Swift (docling-snap) bindings
      - Open-source MIT license, governed by the LF AI and Data Foundation
      - Originated at IBM Research Zurich (AI for Knowledge team)
    sources:
      - https://docling-project.github.io/docling/
      - https://github.com/docling-project/docling
      - https://github.com/docling-project/docling-serve
      - https://github.com/docling-project/docling-mcp
      - https://lfaidata.foundation/projects/docling/
      - https://arxiv.org/abs/2408.09869
    updated: '2026-05-25'
created: '2026-05-25T00:00:00.000Z'
modified: '2026-05-25'
position: Consuming
description: >-
  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.
maintainers:
  - FN: Kin Lane
    email: info@apievangelist.com
    X: apievangelist
    url: https://apievangelist.com
specificationVersion: '0.16'