{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-open-notebook","slug":"open-notebook","name":"Open Notebook","type":"repo","url":"https://www.open-notebook.ai","page_url":"https://unfragile.ai/open-notebook","categories":["automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-open-notebook__cap_0","uri":"capability://text.generation.language.document.to.audio.synthesis.with.multi.voice.support","name":"document-to-audio-synthesis-with-multi-voice-support","description":"Converts uploaded documents (PDFs, text files, web content) into natural-sounding audio narration using text-to-speech synthesis with support for multiple voice profiles, speaking rates, and language detection. The system processes document content through a TTS pipeline that handles formatting preservation, paragraph segmentation, and voice assignment rules to generate coherent multi-voice audio outputs suitable for podcast-style consumption.","intents":["I want to convert my research papers into audio format for commute listening","I need to generate podcast-style narration from my documentation with different voices for different sections","I want to make my long-form content accessible as audio without manual recording"],"best_for":["researchers and students consuming academic papers on-the-go","content creators producing podcast-adjacent audio from written materials","accessibility-focused teams making documentation audio-first"],"limitations":["TTS quality varies by language and voice model availability","Complex formatting (tables, equations, code blocks) may not render naturally in audio","Audio generation latency scales with document length (typically 1-2 minutes per 10,000 words)","Voice switching overhead adds processing time for multi-voice outputs"],"requires":["Document input (PDF, TXT, DOCX, or URL)","TTS API access (likely Google Cloud TTS, Azure Speech, or similar)","Audio codec support for MP3/WAV output"],"input_types":["PDF documents","plain text files","web URLs","markdown content"],"output_types":["MP3 audio files","WAV audio files","M4A audio files"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_1","uri":"capability://code.generation.editing.interactive.notebook.generation.from.source.documents","name":"interactive-notebook-generation-from-source-documents","description":"Automatically generates structured, interactive notebooks from uploaded documents by parsing content into sections, extracting key concepts, and creating executable cells with explanations. Uses LLM-based content understanding to identify logical breakpoints, generate markdown documentation, and suggest code examples or visualizations that correspond to document concepts, creating a Jupyter-like interface without manual cell creation.","intents":["I want to convert my research paper into an interactive notebook with executable examples","I need to create a structured learning resource from unstructured documentation","I want to generate a notebook outline with pre-populated cells from my source material"],"best_for":["educators converting course materials into interactive notebooks","researchers documenting methodologies with executable code examples","data scientists creating reproducible analysis notebooks from reports"],"limitations":["Generated code examples may require manual validation and debugging","Complex domain-specific content may not translate accurately to executable cells","Notebook structure quality depends on source document clarity and organization","No automatic dependency resolution for generated code examples"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Jupyter or compatible notebook environment","Document parsing library (PyPDF2, pdfplumber, or similar)"],"input_types":["PDF documents","markdown files","plain text","web content"],"output_types":["Jupyter notebooks (.ipynb)","markdown with code blocks","structured JSON notebook format"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_2","uri":"capability://search.retrieval.semantic.search.across.document.collections","name":"semantic-search-across-document-collections","description":"Indexes uploaded documents using vector embeddings and enables semantic search queries that find relevant content by meaning rather than keyword matching. Implements a RAG (Retrieval-Augmented Generation) pipeline where documents are chunked, embedded using a transformer model, stored in a vector database, and retrieved based on cosine similarity to query embeddings, with optional re-ranking for result quality.","intents":["I want to search across my research library for concepts, not just keywords","I need to find all mentions of a topic across multiple documents efficiently","I want to ask natural language questions and get relevant document excerpts as answers"],"best_for":["researchers managing large document collections with complex queries","knowledge workers building internal search systems over proprietary documents","teams implementing RAG systems for domain-specific Q&A"],"limitations":["Embedding quality depends on model choice; domain-specific embeddings may require fine-tuning","Vector database scaling requires careful indexing strategy for 100k+ documents","Semantic search may miss exact phrase matches that keyword search would catch","Chunk size selection impacts both retrieval precision and context window usage"],"requires":["Embedding model (OpenAI, Hugging Face, or local transformer)","Vector database (Pinecone, Weaviate, Milvus, or in-memory alternative)","Document chunking strategy (typically 512-2048 token chunks with overlap)"],"input_types":["PDF documents","text files","markdown content","web URLs"],"output_types":["ranked document excerpts","relevance scores","source document references","structured search results JSON"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_3","uri":"capability://text.generation.language.ai.powered.content.summarization.with.extraction","name":"ai-powered-content-summarization-with-extraction","description":"Generates concise summaries of documents using LLM-based abstractive summarization that understands semantic meaning and extracts key facts, entities, and relationships. Implements multi-level summarization (document-level, section-level, paragraph-level) with configurable summary length and style, optionally extracting structured data like key concepts, citations, and metadata using prompt engineering or few-shot examples.","intents":["I want a quick summary of a long research paper without reading the full text","I need to extract key findings and citations from multiple documents","I want to generate executive summaries with different detail levels for different audiences"],"best_for":["busy professionals filtering large document streams","researchers synthesizing literature reviews from multiple papers","content teams generating summaries for knowledge bases or documentation"],"limitations":["Summarization quality depends on LLM capability; smaller models may miss nuance","Hallucination risk when extracting specific facts or citations","Very long documents may exceed context windows, requiring hierarchical summarization","Domain-specific terminology may not be preserved accurately in summaries"],"requires":["LLM API access (OpenAI GPT-4, Claude, or local model)","Document parsing to extract text content","Prompt templates for summary generation and extraction"],"input_types":["PDF documents","text files","markdown content","web articles"],"output_types":["text summaries","structured JSON with extracted entities","bullet-point summaries","key-value metadata"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_4","uri":"capability://text.generation.language.interactive.q.and.a.with.document.context","name":"interactive-q-and-a-with-document-context","description":"Enables conversational Q&A where users ask questions about uploaded documents and receive answers grounded in document content. Implements a retrieval-augmented generation (RAG) loop that retrieves relevant document excerpts via semantic search, passes them as context to an LLM, and generates answers with citations back to source documents. Maintains conversation history for multi-turn interactions with context carryover.","intents":["I want to ask questions about my research papers and get answers with citations","I need to have a conversation about document content without reading the full text","I want to verify claims in a document by asking follow-up questions"],"best_for":["researchers exploring document content through natural conversation","students learning from materials through interactive Q&A","teams building internal knowledge assistants over proprietary documents"],"limitations":["Answer accuracy limited by retrieval quality; poor semantic search results lead to hallucinations","Context window limits prevent using entire documents for very long files","Citation accuracy depends on proper source tracking through retrieval pipeline","Multi-turn conversations may accumulate context drift without explicit memory management"],"requires":["Semantic search capability (vector embeddings + database)","LLM API access for answer generation","Document indexing and chunking infrastructure","Conversation state management for multi-turn interactions"],"input_types":["natural language questions","follow-up queries","clarification requests"],"output_types":["natural language answers","source document citations","relevance scores","conversation history"],"categories":["text-generation-language","search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_5","uri":"capability://text.generation.language.multi.document.synthesis.and.comparison","name":"multi-document-synthesis-and-comparison","description":"Analyzes relationships and differences across multiple documents by performing semantic comparison, identifying contradictions, and synthesizing insights across sources. Uses LLM-based analysis to create cross-document summaries, comparison matrices, and synthesis reports that highlight agreements, disagreements, and complementary information across the document collection. Implements document clustering and relationship mapping to visualize how documents relate to each other.","intents":["I want to compare findings across multiple research papers on the same topic","I need to identify contradictions or agreements between different sources","I want to synthesize insights from a collection of documents into a unified view"],"best_for":["literature review researchers synthesizing multiple papers","policy analysts comparing different regulatory documents","competitive intelligence teams analyzing multiple competitor documents"],"limitations":["Synthesis quality depends on document relevance and LLM reasoning capability","Identifying true contradictions requires semantic understanding that may fail on nuanced topics","Scaling to 100+ documents requires careful batching and aggregation strategies","Relationship mapping can become visually complex and difficult to interpret"],"requires":["Semantic similarity computation across documents","LLM API for synthesis and comparison analysis","Graph or matrix visualization library for relationship display","Document embedding and clustering infrastructure"],"input_types":["multiple PDF documents","text files","web content","document collections"],"output_types":["comparison matrices","synthesis reports","relationship graphs","contradiction lists","structured JSON analysis"],"categories":["text-generation-language","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_6","uri":"capability://automation.workflow.notebook.export.and.format.conversion","name":"notebook-export-and-format-conversion","description":"Exports generated notebooks and content to multiple formats including Jupyter (.ipynb), markdown, PDF, HTML, and custom formats. Implements format-specific rendering pipelines that preserve code executability, formatting, and interactivity where applicable. Supports batch export of multiple notebooks with consistent styling and optional template application for branded output.","intents":["I want to export my interactive notebook as a PDF for sharing with non-technical stakeholders","I need to convert my notebook to markdown for version control and collaboration","I want to generate HTML slides from my notebook content for presentations"],"best_for":["educators sharing notebooks across different platforms and audiences","researchers publishing reproducible analysis in multiple formats","teams managing notebook distribution across different tools"],"limitations":["Interactive elements may not render in static formats (PDF, markdown)","Code execution state is lost in non-Jupyter exports","Complex visualizations may degrade in quality during format conversion","Custom notebook extensions may not be supported in all export formats"],"requires":["Notebook parsing library (nbconvert, nbformat)","Format-specific renderers (pandoc for markdown/PDF, plotly for HTML)","Template system for styling and branding"],"input_types":["Jupyter notebooks (.ipynb)","internal notebook format"],"output_types":["Jupyter notebooks (.ipynb)","markdown files","PDF documents","HTML pages","custom formats"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_7","uri":"capability://automation.workflow.collaborative.notebook.sharing.and.versioning","name":"collaborative-notebook-sharing-and-versioning","description":"Enables sharing of generated notebooks with team members through shareable links, collaborative editing, and version history tracking. Implements a version control layer that tracks changes to notebooks, allows reverting to previous versions, and supports branching for experimental modifications. Integrates with Git or similar systems for source control and enables commenting/annotation on specific cells or sections.","intents":["I want to share my notebook with colleagues for feedback and collaborative editing","I need to track changes to my notebook and revert to previous versions if needed","I want to create branches of my notebook to experiment without affecting the main version"],"best_for":["research teams collaborating on analysis notebooks","educational institutions managing shared learning resources","data science teams maintaining reproducible analysis pipelines"],"limitations":["Real-time collaborative editing may have latency or conflict resolution issues","Version history storage scales with notebook size and edit frequency","Merging conflicting changes in notebooks is more complex than text files","Execution state is not preserved across versions"],"requires":["Version control backend (Git, custom database)","Collaborative editing infrastructure (WebSocket, operational transformation)","User authentication and permission management","Notebook diff/merge algorithms"],"input_types":["Jupyter notebooks","internal notebook format"],"output_types":["shareable links","version history","diff views","merged notebooks"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_8","uri":"capability://text.generation.language.custom.prompt.and.template.management","name":"custom-prompt-and-template-management","description":"Allows users to define custom prompts and templates for document processing tasks including summarization, extraction, and notebook generation. Implements a prompt library system where users can create, test, and version prompts, with variable substitution for dynamic content. Supports few-shot example management and prompt chaining for complex multi-step analysis workflows.","intents":["I want to customize how my documents are summarized for my specific domain","I need to create extraction templates for structured data from documents","I want to build a multi-step analysis workflow with custom prompts"],"best_for":["domain experts optimizing document processing for specialized content","teams building custom document analysis pipelines","researchers experimenting with different prompting strategies"],"limitations":["Prompt quality requires expertise in prompt engineering; poor prompts degrade results","Testing and iteration on prompts requires multiple API calls and costs","Complex prompt chains may accumulate errors through multiple LLM calls","No built-in validation that prompts produce expected output structure"],"requires":["Prompt template engine (Jinja2, Handlebars, or similar)","LLM API access for prompt testing","Prompt versioning and storage system","Few-shot example management infrastructure"],"input_types":["prompt templates","few-shot examples","variable definitions"],"output_types":["rendered prompts","LLM responses","prompt execution logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-notebook__cap_9","uri":"capability://automation.workflow.batch.document.processing.and.automation","name":"batch-document-processing-and-automation","description":"Processes multiple documents in batch mode with configurable workflows that apply consistent transformations across all files. Implements job queuing, progress tracking, and error handling for large-scale document processing. Supports scheduling batch jobs to run on a schedule and integrates with external storage systems (S3, Google Drive) for input/output management.","intents":["I want to process 100 research papers through the same analysis pipeline","I need to automatically convert all PDFs in a folder to notebooks","I want to schedule daily batch processing of new documents from a shared drive"],"best_for":["teams managing large document libraries requiring consistent processing","organizations automating document ingestion and analysis workflows","researchers processing datasets of documents at scale"],"limitations":["Batch processing costs scale with document volume; large batches can be expensive","Error handling in one document may require reprocessing entire batch","Progress tracking and cancellation may be limited for very large batches","Resource constraints may require careful job scheduling to avoid overload"],"requires":["Job queue system (Celery, Bull, or similar)","Cloud storage integration (S3, GCS, Azure Blob)","LLM API with batch processing support or rate limiting","Monitoring and logging infrastructure"],"input_types":["document collections","folder paths","cloud storage URLs"],"output_types":["processed documents","batch results","processing logs","error reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Document input (PDF, TXT, DOCX, or URL)","TTS API access (likely Google Cloud TTS, Azure Speech, or similar)","Audio codec support for MP3/WAV output","LLM API access (OpenAI, Anthropic, or local model)","Jupyter or compatible notebook environment","Document parsing library (PyPDF2, pdfplumber, or similar)","Embedding model (OpenAI, Hugging Face, or local transformer)","Vector database (Pinecone, Weaviate, Milvus, or in-memory alternative)","Document chunking strategy (typically 512-2048 token chunks with overlap)","LLM API access (OpenAI GPT-4, Claude, or local model)"],"failure_modes":["TTS quality varies by language and voice model availability","Complex formatting (tables, equations, code blocks) may not render naturally in audio","Audio generation latency scales with document length (typically 1-2 minutes per 10,000 words)","Voice switching overhead adds processing time for multi-voice outputs","Generated code examples may require manual validation and debugging","Complex domain-specific content may not translate accurately to executable cells","Notebook structure quality depends on source document clarity and organization","No automatic dependency resolution for generated code examples","Embedding quality depends on model choice; domain-specific embeddings may require fine-tuning","Vector database scaling requires careful indexing strategy for 100k+ documents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=open-notebook","compare_url":"https://unfragile.ai/compare?artifact=open-notebook"}},"signature":"O72KQKlbGtu9d0+EBZGsG9Le1kphmGD8BBVsJy+0hlKUwkLpOpICZUs0NTWHs1aFh6M7yQ2QTilaIlbX9OtEAA==","signedAt":"2026-06-21T02:29:40.351Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open-notebook","artifact":"https://unfragile.ai/open-notebook","verify":"https://unfragile.ai/api/v1/verify?slug=open-notebook","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}