{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-presenton--presenton","slug":"presenton--presenton","name":"presenton","type":"product","url":"https://presenton.ai","page_url":"https://unfragile.ai/presenton--presenton","categories":["app-builders"],"tags":["ai-agent","ai-presentation","api","gamma","powerpoint-automation","powerpoint-free","powerpoint-generation","presentation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-presenton--presenton__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.unified.interface","name":"multi-provider llm orchestration with unified interface","description":"Abstracts OpenAI, Gemini, Anthropic, Ollama, and custom endpoints behind a single LLMClient class in FastAPI, enabling runtime provider switching without code changes. Implements provider-agnostic prompt formatting and response parsing, with fallback error handling for provider-specific API variations. Configuration is externalized via environment variables, allowing deployment-time provider selection without recompilation.","intents":["I want to switch between OpenAI and local Ollama models without rewriting generation logic","I need to support multiple LLM providers for cost optimization and redundancy","I want to add a new LLM provider (e.g., custom fine-tuned endpoint) without modifying core generation code"],"best_for":["teams building self-hosted presentation systems with provider flexibility","enterprises requiring on-premises LLM deployment via Ollama","developers prototyping with multiple LLM providers before committing to one"],"limitations":["Provider-specific features (e.g., vision capabilities, function calling schemas) require adapter code per provider","No built-in rate limiting or quota management across providers","Latency variance between providers (Ollama local ~500ms vs OpenAI ~1-2s) not abstracted"],"requires":["API keys for chosen providers (OpenAI, Anthropic, Gemini) OR Ollama instance running locally","FastAPI backend running (Python 3.9+)","Environment variables configured for provider selection"],"input_types":["text prompts","structured JSON schemas for function calling"],"output_types":["text completions","structured JSON responses","streaming token streams"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_1","uri":"capability://data.processing.analysis.document.to.presentation.pipeline.with.multi.format.ingestion","name":"document-to-presentation pipeline with multi-format ingestion","description":"Accepts PDF, DOCX, and PPTX files via docling library for document parsing, extracts structured content (text, tables, images), and feeds parsed content into a two-stage generation pipeline: outline generation (LLM creates hierarchical slide structure) followed by per-slide content generation (LLM writes speaker notes, bullet points, titles). Asynchronous processing with real-time streaming updates to frontend via WebSocket.","intents":["I have a research paper PDF and want to auto-generate a presentation outline from it","I want to convert an existing DOCX report into slides without manual rewriting","I need to regenerate slides from a PPTX with updated content while preserving layout"],"best_for":["knowledge workers converting documents into presentations","teams automating presentation generation from source materials","researchers and analysts creating slide decks from reports"],"limitations":["docling parsing quality depends on document structure; poorly formatted PDFs may lose semantic meaning","Image extraction from documents is supported but image understanding requires separate vision model (not built-in)","PPTX parsing preserves structure but may lose custom formatting/animations","No support for embedded media (videos, audio) in source documents"],"requires":["FastAPI backend with docling library installed","PDF, DOCX, or PPTX file (max size TBD, likely 50MB+ based on typical constraints)","LLM provider configured for outline and content generation"],"input_types":["PDF files","DOCX files","PPTX files"],"output_types":["structured presentation outline (JSON)","per-slide content (titles, bullet points, speaker notes)","presentation object ready for rendering"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_10","uri":"capability://automation.workflow.configuration.management.system.with.environment.based.provider.selection","name":"configuration management system with environment-based provider selection","description":"Centralized configuration system that externalizes LLM provider selection, image provider settings, database credentials, and API keys via environment variables and configuration files. Configuration is loaded at startup and applied across all services (FastAPI, Next.js). Enables deployment-time customization without code changes: switch LLM providers, enable/disable image generation, configure database, set API keys. Configuration validation ensures required settings are present before services start.","intents":["I want to switch LLM providers (OpenAI → Anthropic) by changing environment variables","I need to configure different settings for dev, staging, and production deployments","I want to disable certain features (e.g., image generation) without code changes"],"best_for":["DevOps teams managing multi-environment deployments","enterprises requiring different configurations per deployment","developers switching between local Ollama and cloud APIs during development"],"limitations":["Configuration validation is basic; invalid settings may not be caught until runtime","No hot-reload; configuration changes require service restart","Secrets management relies on environment variables; no built-in encryption or vault integration","Configuration documentation may be incomplete; users must read code to understand all options"],"requires":["Environment variables set before service startup","Configuration files (if using file-based config) in correct format"],"input_types":["environment variables","configuration files (YAML/JSON)"],"output_types":["loaded configuration object","validation errors (if config invalid)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_11","uri":"capability://safety.moderation.error.handling.and.fallback.logic.with.provider.redundancy","name":"error handling and fallback logic with provider redundancy","description":"Implements multi-layer error handling: provider-level fallbacks (if OpenAI fails, try Anthropic), graceful degradation (if image generation fails, skip images), and user-facing error messages. LLM provider errors are caught and logged; if primary provider fails, system attempts secondary provider. Image generation failures don't block slide generation; slides are created without images. API errors are wrapped with context (provider name, request details) for debugging. Error handling is consistent across all providers and services.","intents":["I want presentation generation to succeed even if one LLM provider is down","I need clear error messages when something fails, not cryptic API errors","I want image generation failures to not block the entire presentation generation"],"best_for":["production deployments requiring high availability","teams using multiple LLM providers for redundancy","users expecting graceful degradation when services fail"],"limitations":["Fallback logic adds latency; if primary provider fails, system waits for timeout before trying secondary","Fallback providers may produce different quality results; no guarantee of consistency","Error messages may not be user-friendly; technical details may confuse non-technical users","No automatic retry with exponential backoff; retries are manual or require external orchestration"],"requires":["Multiple LLM providers configured for fallback","Error logging infrastructure (stdout, file, external service)"],"input_types":["generation request","provider configuration"],"output_types":["successful result from primary or fallback provider","error message with context"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_12","uri":"capability://text.generation.language.slide.content.generation.with.llm.powered.text.synthesis","name":"slide content generation with llm-powered text synthesis","description":"Per-slide content generation stage where LLM writes slide titles, bullet points, speaker notes, and captions based on outline metadata and slide context. LLM receives structured prompt including slide topic, section context, slide type (title, bullet, image+text), and layout hints. Output is parsed into structured slide content (title, bullets, notes). Generation is parallelizable; multiple slides can be generated concurrently if LLM provider supports concurrent requests. Content is validated for length (titles <100 chars, bullets <200 chars) and reformatted if needed.","intents":["I want the LLM to write slide titles and bullet points based on the outline","I need speaker notes generated for each slide","I want slide content to be concise and formatted appropriately for the slide type"],"best_for":["users generating presentations with LLM-written content","teams automating content creation for large presentations","educators creating course materials with AI-generated speaker notes"],"limitations":["Content quality depends on LLM model and prompt engineering; poor prompts produce low-quality content","Concurrent generation may hit LLM rate limits; requires rate limiting logic","Content validation is basic (length checks); no semantic validation (e.g., factual accuracy)","Generated content may be generic or repetitive; no deduplication or uniqueness checks"],"requires":["LLM provider configured","Outline with slide metadata","FastAPI backend for content generation"],"input_types":["slide metadata (topic, section, type, layout hints)","outline context"],"output_types":["slide content (title, bullets, speaker notes)","structured slide object"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_2","uri":"capability://automation.workflow.template.based.slide.layout.system.with.custom.template.support","name":"template-based slide layout system with custom template support","description":"Implements a layout system where each slide conforms to a predefined template (title slide, bullet list, two-column, image + text, etc.). Templates are compiled from configuration files into rendering instructions. Custom templates can be created by users via template creation UI, compiled into the system, and previewed before use. Layout system maps generated content (titles, bullets, images) to template slots during slide rendering.","intents":["I want to ensure all generated slides follow my brand's layout guidelines","I need to create a custom template for a specific slide type (e.g., timeline, comparison matrix)","I want to preview how my content will look in a template before generating the full presentation"],"best_for":["enterprises with strict brand guidelines requiring consistent layouts","teams building presentation templates for specific use cases (sales decks, technical talks)","designers customizing presentation aesthetics without touching code"],"limitations":["Template creation requires understanding the template DSL/configuration format","No drag-and-drop template builder; templates are code/config-based","Template compilation happens at runtime; complex templates may add 100-200ms per slide render","Limited to predefined template slots; arbitrary custom layouts require template code changes"],"requires":["Next.js frontend for template creation UI","FastAPI backend for template compilation and preview","Understanding of template configuration format (likely YAML or JSON schema)"],"input_types":["template configuration (YAML/JSON)","slide content (title, bullets, images, metadata)"],"output_types":["compiled template object","rendered slide HTML/canvas","template preview image"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_3","uri":"capability://text.generation.language.ai.assisted.presentation.editing.with.undo.redo.state.management","name":"ai-assisted presentation editing with undo/redo state management","description":"Provides interactive editor UI (Next.js React components) for post-generation slide editing: text editing, image/icon replacement, and AI-assisted content refinement. State management tracks all edits via an undo/redo system (likely using Redux or similar state machine), enabling users to revert changes. AI-assisted editing allows users to request LLM-powered rewrites of slide text, bullet points, or speaker notes without regenerating the entire presentation.","intents":["I want to edit slide text and images after generation without losing the presentation structure","I need to undo a series of edits and revert to a previous version","I want to ask the AI to rewrite a bullet point or speaker note without regenerating the whole slide"],"best_for":["users refining AI-generated presentations before delivery","teams collaborating on slide edits with version history","presenters customizing generated content for specific audiences"],"limitations":["Undo/redo state is in-memory; closing the editor without saving loses edit history","AI-assisted editing requires LLM provider configured; latency adds 1-3s per edit request","No collaborative real-time editing; single-user editor only","Image editing limited to replacement; no in-editor image manipulation (crop, resize, filter)"],"requires":["Next.js frontend running","FastAPI backend for AI-assisted edit requests","LLM provider configured for content refinement"],"input_types":["text (slide titles, bullets, speaker notes)","images (for replacement)","edit requests (text rewrites, content changes)"],"output_types":["updated slide content","edited presentation state","undo/redo history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_4","uri":"capability://automation.workflow.multi.format.export.with.pptx.and.pdf.generation","name":"multi-format export with pptx and pdf generation","description":"Exports presentations to PPTX (PowerPoint) and PDF formats via dedicated export pipeline. PPTX export uses python-pptx library to construct PowerPoint objects from presentation data model, embedding fonts, images, and formatting. PDF export converts PPTX to PDF or renders slides to PDF directly. Export architecture abstracts format-specific logic, allowing new export formats to be added. Handles image embedding, text formatting (fonts, sizes, colors), and layout preservation during export.","intents":["I want to download my presentation as a .pptx file to edit in PowerPoint","I need to export slides as PDF for sharing or printing","I want to ensure exported presentations preserve all formatting and images"],"best_for":["users integrating Presenton presentations into PowerPoint workflows","teams distributing presentations as PDFs to stakeholders","enterprises requiring standard office format compatibility"],"limitations":["PPTX export may lose advanced animations or custom transitions not supported by python-pptx","PDF export quality depends on underlying rendering engine; complex layouts may render differently","Large presentations (100+ slides) with embedded images may produce large file sizes (10-50MB+)","Font embedding in PPTX requires fonts to be available on export system; missing fonts fall back to defaults"],"requires":["FastAPI backend with python-pptx library","Presentation data model in memory","Image files accessible for embedding"],"input_types":["presentation object (JSON/structured data)","slide content (text, images, metadata)","formatting metadata (fonts, colors, sizes)"],"output_types":["PPTX file (binary)","PDF file (binary)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_5","uri":"capability://image.visual.image.generation.and.stock.image.integration.with.provider.abstraction","name":"image generation and stock image integration with provider abstraction","description":"Abstracts image sourcing behind a provider architecture supporting AI image generators (DALL-E, Stable Diffusion via ComfyUI) and stock image APIs (Unsplash, Pexels, Pixabay). During slide generation, the system identifies image needs (e.g., 'business meeting photo'), queries configured image providers, and embeds selected images into slides. ComfyUI integration enables local Stable Diffusion inference for privacy-preserving image generation. Provider selection is configurable; fallback logic tries multiple providers if one fails.","intents":["I want AI-generated images on my slides without manually searching for stock photos","I need to use local Stable Diffusion for image generation to keep data private","I want to mix AI-generated and stock images on the same presentation"],"best_for":["users generating presentations with visual content without manual image sourcing","enterprises requiring on-premises image generation via ComfyUI","teams balancing cost (stock images) with customization (AI generation)"],"limitations":["AI image generation quality varies by model; Stable Diffusion may produce lower quality than DALL-E","ComfyUI integration requires local GPU and model downloads (10-50GB); setup complexity high","Stock image APIs have rate limits; high-volume generation may hit quotas","Image selection is automatic; no user control over which provider or image is chosen per slide","Generated images may not match slide content perfectly; LLM prompt engineering affects quality"],"requires":["FastAPI backend with image provider integrations","API keys for chosen providers (DALL-E, Unsplash, etc.) OR ComfyUI instance running locally","Image processing libraries (Pillow, etc.) for embedding"],"input_types":["image description (text prompt from LLM)","slide context (topic, theme)"],"output_types":["image URL or binary image data","embedded image in slide"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_6","uri":"capability://automation.workflow.real.time.streaming.presentation.generation.with.asynchronous.processing","name":"real-time streaming presentation generation with asynchronous processing","description":"Implements asynchronous generation pipeline where outline and slide content are generated sequentially, with results streamed to frontend via WebSocket in real-time. Each generation step (outline → slide 1 → slide 2 → ...) emits updates as they complete, allowing users to see progress and partial results before full presentation is ready. Backend uses async/await (FastAPI) and frontend listens to WebSocket events, updating UI incrementally. Prevents blocking UI during long-running generation (typically 30-120s for full presentation).","intents":["I want to see my presentation being generated in real-time instead of waiting for a spinner","I need to cancel generation mid-way if I see the outline is wrong","I want to start editing slides as soon as they're generated, not wait for the full presentation"],"best_for":["users generating presentations interactively and expecting responsive UI","teams building presentation tools with real-time feedback","applications where generation latency is high (100+ slides) and progressive updates improve UX"],"limitations":["WebSocket connection must remain open; network interruptions lose streaming updates","Partial results (incomplete outline, partial slides) may be inconsistent if generation is cancelled mid-way","Streaming adds complexity to state management; out-of-order updates possible if backend processes slides in parallel","Frontend must handle incremental updates; not all UI frameworks handle streaming state well"],"requires":["FastAPI backend with async/await support","WebSocket server (built into FastAPI)","Next.js frontend with WebSocket client library","Network connectivity between frontend and backend"],"input_types":["generation request (prompt, document, template)","user cancellation signal"],"output_types":["streamed JSON events (outline, slide updates)","final presentation object"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_7","uri":"capability://planning.reasoning.outline.and.structure.generation.with.hierarchical.slide.planning","name":"outline and structure generation with hierarchical slide planning","description":"LLM-powered stage that takes a prompt or document and generates a hierarchical presentation outline (title, sections, subsections, slide count, key points per slide). Outline serves as a blueprint for subsequent slide content generation, ensuring logical flow and structure. Outline generation is separate from content generation, allowing users to review/edit the outline before committing to full slide generation. Outline includes metadata (slide type, layout hints, image suggestions) that guides per-slide content generation.","intents":["I want to see the presentation structure before generating all slides","I need to edit the outline (add/remove sections, reorder slides) before full generation","I want the LLM to plan the presentation logically before writing slide content"],"best_for":["users who want to review presentation structure before committing to generation","teams building presentations with specific narrative arcs or section requirements","educators and trainers designing course materials with logical progression"],"limitations":["Outline generation quality depends on LLM prompt engineering; poor prompts produce incoherent outlines","No validation that outline is logically sound; LLM may generate unbalanced structures (e.g., 50 slides in one section)","Outline editing UI may be limited; users may not be able to easily reorder or restructure","Outline-to-content mapping may not preserve user edits if outline is regenerated"],"requires":["LLM provider configured","Prompt or document input","FastAPI backend for outline generation"],"input_types":["text prompt","document content (from document parsing)"],"output_types":["hierarchical outline (JSON)","slide metadata (titles, section names, layout hints)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_8","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.agent.orchestration","name":"mcp server integration for ai agent orchestration","description":"Implements a Model Context Protocol (MCP) server (running on port 8001) that exposes Presenton capabilities as MCP tools, enabling external AI agents to orchestrate presentation generation programmatically. MCP tools include presentation creation, outline generation, slide editing, and export. Agents can call these tools via standard MCP protocol, enabling workflows like 'generate presentation from web search results' or 'create slides from meeting notes'. MCP server is separate from main application, allowing integration with external agent frameworks (e.g., Claude, LangChain agents).","intents":["I want to build an AI agent that generates presentations as part of a larger workflow","I need to integrate Presenton into my existing MCP-based agent system","I want to automate presentation generation from external data sources via agent orchestration"],"best_for":["AI agent developers building multi-step workflows involving presentation generation","teams integrating Presenton into larger AI automation systems","enterprises building custom agents that need presentation capabilities"],"limitations":["MCP server adds operational complexity; requires separate port and process management","MCP tool definitions must be kept in sync with backend API changes","No built-in authentication for MCP server; requires external auth layer if exposed over network","Agent orchestration latency depends on agent framework and network; adds 100-500ms per tool call"],"requires":["MCP server running (port 8001)","MCP client (external agent framework)","FastAPI backend for tool implementation"],"input_types":["MCP tool calls (JSON-RPC)","tool parameters (prompt, document, template)"],"output_types":["MCP tool results (presentation object, export URL)","streaming results for long-running operations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-presenton--presenton__cap_9","uri":"capability://automation.workflow.docker.and.electron.desktop.deployment.with.unified.architecture","name":"docker and electron desktop deployment with unified architecture","description":"Supports three deployment modes (Web via Docker, Desktop via Electron, Cloud) with unified backend architecture. Docker deployment packages FastAPI backend, Next.js frontend, and Nginx reverse proxy in containers, managed by docker-compose. Electron desktop app embeds FastAPI and Next.js servers, enabling offline-first operation without external services. Both deployments share identical backend code, ensuring feature parity. Electron IPC handlers enable native OS integration (file dialogs, system notifications). Node.js orchestrator (start.js) manages service startup and lifecycle across all deployment modes.","intents":["I want to run Presenton locally in Docker without cloud dependencies","I need a desktop app that works offline without internet connectivity","I want to deploy Presenton on-premises with full control over data and infrastructure"],"best_for":["enterprises requiring on-premises deployment for data privacy","users wanting offline-first desktop application","teams deploying Presenton in air-gapped or restricted networks"],"limitations":["Docker deployment requires Docker and docker-compose; adds operational overhead","Electron app bundles all services; larger download size (100-500MB+) compared to web-only","Desktop app updates require full app reinstall; no automatic updates built-in","Electron IPC adds complexity; native integrations may lag behind web version","Resource consumption higher for desktop (embedded servers); not suitable for low-end machines"],"requires":["Docker and docker-compose for Docker deployment","Node.js 18+ for Electron app","Python 3.9+ for FastAPI backend","Sufficient disk space for Docker images or Electron app bundle"],"input_types":["docker-compose.yml configuration","environment variables for service configuration"],"output_types":["running services (FastAPI, Next.js, Nginx)","desktop application executable"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["API keys for chosen providers (OpenAI, Anthropic, Gemini) OR Ollama instance running locally","FastAPI backend running (Python 3.9+)","Environment variables configured for provider selection","FastAPI backend with docling library installed","PDF, DOCX, or PPTX file (max size TBD, likely 50MB+ based on typical constraints)","LLM provider configured for outline and content generation","Environment variables set before service startup","Configuration files (if using file-based config) in correct format","Multiple LLM providers configured for fallback","Error logging infrastructure (stdout, file, external service)"],"failure_modes":["Provider-specific features (e.g., vision capabilities, function calling schemas) require adapter code per provider","No built-in rate limiting or quota management across providers","Latency variance between providers (Ollama local ~500ms vs OpenAI ~1-2s) not abstracted","docling parsing quality depends on document structure; poorly formatted PDFs may lose semantic meaning","Image extraction from documents is supported but image understanding requires separate vision model (not built-in)","PPTX parsing preserves structure but may lose custom formatting/animations","No support for embedded media (videos, audio) in source documents","Configuration validation is basic; invalid settings may not be caught until runtime","No hot-reload; configuration changes require service restart","Secrets management relies on environment variables; no built-in encryption or vault integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31238704534772277,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:04.027Z","last_commit":"2026-05-03T13:09:02Z"},"community":{"stars":4866,"forks":953,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=presenton--presenton","compare_url":"https://unfragile.ai/compare?artifact=presenton--presenton"}},"signature":"llp69dpTuc/Ljqoow7AasY9vh7Vukm30eqTB2ZwjD+lbXrZM9e2Mb7sJC1LY02UD1Sc3ROIYMjF7Ded61yUCAg==","signedAt":"2026-06-20T07:45:22.569Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/presenton--presenton","artifact":"https://unfragile.ai/presenton--presenton","verify":"https://unfragile.ai/api/v1/verify?slug=presenton--presenton","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"}}