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 - type: NaftikoCapability url: capabilities/docling-cli-convert.yaml 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 - type: NaftikoCapability url: capabilities/docling-serve-convert.yaml - type: NaftikoCapability url: capabilities/docling-serve-tasks.yaml 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-productions2.s3.amazonaws.com/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'