{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"obsidian-copilot","slug":"obsidian-copilot","name":"Obsidian Copilot","type":"agent","url":"https://github.com/logancyang/obsidian-copilot","page_url":"https://unfragile.ai/obsidian-copilot","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"obsidian-copilot__cap_0","uri":"capability://search.retrieval.vault.wide.semantic.search.with.hybrid.bm25.and.vector.retrieval","name":"vault-wide semantic search with hybrid bm25+ and vector retrieval","description":"Combines lexical BM25+ search with optional embedding-backed vector search (Orama/Miyo) to retrieve semantically similar notes from the entire vault. The system maintains dual indices—one for keyword matching and one for semantic embeddings—allowing users to find notes by meaning rather than exact text matches. Queries are processed through both indices and results are ranked by relevance, enabling natural language question answering over the knowledge base.","intents":["Find all notes related to a concept without remembering exact keywords","Answer questions by searching the vault for relevant context automatically","Discover connections between notes based on semantic similarity rather than explicit links"],"best_for":["knowledge workers with large vaults (100+ notes) who need semantic discovery","researchers building on existing notes without remembering exact terminology","teams using Obsidian as a knowledge base that need AI-powered QA"],"limitations":["Embedding-backed search requires external API keys (OpenAI, Anthropic, or self-hosted Miyo)","BM25+ lexical search alone may miss semantic variations and synonyms","Vector search adds ~500ms-2s latency per query depending on vault size and embedding provider","No built-in re-ranking by note recency or importance—relies on embedding model's relevance scoring"],"requires":["Obsidian 0.15.0+","At least one LLM provider API key (OpenAI, Anthropic, Groq, etc.)","Optional: embedding provider API key (OpenAI, Anthropic, or self-hosted Miyo for vector search)"],"input_types":["natural language query (text)","selected notes/folders/tags as context"],"output_types":["ranked list of relevant notes with excerpts","structured context envelope for LLM consumption"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.streaming.response.handling","name":"multi-provider llm abstraction with streaming response handling","description":"Abstracts 15+ LLM providers (OpenAI, Anthropic, Groq, DeepSeek, Ollama, Azure OpenAI, etc.) behind a unified ChatModelProvider enum and chain execution system. Implements provider-agnostic streaming via the Chain Execution System (DeepWiki), allowing responses to stream token-by-token to the UI while maintaining consistent behavior across different model APIs. Each provider's authentication, rate limits, and response formats are normalized through a model management layer.","intents":["Switch between different LLM providers without changing prompts or logic","Use local models (Ollama, LM Studio) for privacy-critical use cases","Leverage provider-specific advantages (e.g., Groq for speed, Anthropic for long context)"],"best_for":["teams evaluating multiple LLM providers without rewriting integrations","privacy-conscious users who want to run local models (Ollama, LM Studio)","developers building multi-tenant Obsidian setups with per-user provider selection"],"limitations":["Streaming implementation adds ~50-100ms latency per token due to UI update overhead","No automatic fallback if primary provider fails—requires manual provider switching","Provider-specific features (e.g., vision, function calling) must be explicitly enabled per provider","Token counting and cost estimation not built-in—relies on provider's own tracking"],"requires":["Obsidian 0.15.0+","API key for at least one supported provider (or local Ollama/LM Studio instance)","Network connectivity for cloud providers (local providers work offline)"],"input_types":["chat message (text)","system prompt (text)","context from vault (structured)"],"output_types":["streamed text response","structured message object with metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_10","uri":"capability://search.retrieval.relevant.notes.sidebar.with.link.graph.and.semantic.suggestions","name":"relevant notes sidebar with link-graph and semantic suggestions","description":"The Relevant Notes sidebar panel (DeepWiki: User Interface) displays notes related to the current conversation using two mechanisms: link-graph analysis (showing notes linked from the current context) and semantic similarity (showing notes with similar embeddings). This provides users with contextual navigation and discovery without requiring explicit search. The panel updates dynamically as the conversation progresses.","intents":["Discover related notes without leaving the chat interface","Navigate the vault's knowledge graph based on conversation context","Find semantically similar notes to expand context for the current conversation"],"best_for":["users exploring their vault's knowledge graph while chatting","researchers discovering connections between notes during analysis","teams using Obsidian as a collaborative knowledge base"],"limitations":["Link-graph suggestions only work if notes are explicitly linked—unlinked notes are not discovered","Semantic suggestions require embedding provider API key—not available without external service","Sidebar updates add ~500ms latency per message due to embedding computation","No ranking by note importance or recency—suggestions are purely based on similarity","Sidebar can become cluttered with too many suggestions (no built-in filtering or pagination)"],"requires":["Obsidian 0.15.0+","At least one LLM provider API key for chat","Optional: embedding provider API key for semantic suggestions"],"input_types":["current conversation context (text)","vault structure (links, tags)"],"output_types":["list of related notes (file references)","relevance scores (numeric)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_11","uri":"capability://data.processing.analysis.document.parsing.with.pdf.epub.docx.support.via.hosted.conversion","name":"document parsing with pdf/epub/docx support via hosted conversion","description":"The PDF/EPUB/DOCX Parsing feature (DeepWiki: Core Features) allows users to upload documents in multiple formats, which are converted to Markdown via Brevilabs-hosted infrastructure. The converted content is then indexed and searchable within the vault. This enables users to incorporate external documents into their knowledge base without manual transcription. Parsing is handled server-side to avoid bloating the Obsidian plugin.","intents":["Import external documents (PDFs, EPUBs, Word docs) into the vault without manual conversion","Make document content searchable and referenceable in chat","Build knowledge bases that span both native notes and external documents"],"best_for":["researchers importing academic papers and books into their vault","teams consolidating documentation from multiple sources","users building comprehensive knowledge bases from mixed content types"],"limitations":["Requires Copilot Plus subscription—not available in free tier","Document conversion is handled by Brevilabs servers—documents are sent to external service (privacy concern)","Conversion quality depends on document structure—poorly formatted PDFs may not convert cleanly","No OCR for scanned documents—image-based PDFs cannot be converted","Converted content is stored as Markdown in the vault—large documents can significantly increase vault size"],"requires":["Obsidian 0.15.0+","Copilot Plus subscription","Network connectivity to Brevilabs servers","Document in supported format (PDF, EPUB, DOCX)"],"input_types":["document file (PDF, EPUB, DOCX)"],"output_types":["Markdown note (converted document content)","searchable vault content"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_12","uri":"capability://tool.use.integration.self.hosted.backend.replacement.with.miyo.firecrawl.and.perplexity.integration","name":"self-hosted backend replacement with miyo, firecrawl, and perplexity integration","description":"The Self-Host Mode (DeepWiki: Core Features) allows users with Copilot Plus (Believer tier) to replace Brevilabs' hosted backend with self-hosted services: Miyo for embeddings, Firecrawl for web scraping, and Perplexity for web search. This enables privacy-conscious users to run the entire Copilot Plus stack without sending data to Brevilabs. Configuration is handled through settings, allowing users to point to their own infrastructure.","intents":["Run Copilot Plus features (agents, document parsing, web search) without sending data to Brevilabs","Deploy embeddings and search infrastructure on-premises for compliance or privacy","Maintain full control over data while using advanced Copilot Plus features"],"best_for":["enterprises with strict data privacy requirements","teams operating in regulated industries (healthcare, finance) that cannot use cloud services","developers building self-hosted knowledge management systems"],"limitations":["Requires Copilot Plus (Believer tier) subscription—significant cost for self-hosting","Self-hosting requires operational expertise—users must deploy and maintain Miyo, Firecrawl, Perplexity instances","No managed updates or support for self-hosted services—users are responsible for patching and upgrades","Performance depends on self-hosted infrastructure—may be slower than Brevilabs' optimized services","Configuration is error-prone—incorrect settings can silently fall back to cloud services or fail silently"],"requires":["Obsidian 0.15.0+","Copilot Plus (Believer tier) subscription","Self-hosted Miyo instance (for embeddings)","Self-hosted Firecrawl instance (for document parsing)","Self-hosted or API access to Perplexity (for web search)","Network connectivity between Obsidian and self-hosted services"],"input_types":["self-hosted service URLs (configuration)","API keys for self-hosted services"],"output_types":["embeddings (from Miyo)","parsed documents (from Firecrawl)","web search results (from Perplexity)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_13","uri":"capability://tool.use.integration.settings.interface.with.provider.configuration.and.model.selection","name":"settings interface with provider configuration and model selection","description":"The Settings Interface (DeepWiki: Settings Interface) provides a comprehensive UI for configuring Obsidian Copilot, including provider selection, API key management, model selection, and feature toggles. The Settings and Configuration System (DeepWiki) manages the CopilotSettings interface and DEFAULT_SETTINGS baseline. Users can configure multiple providers, select default models, and enable/disable features without editing configuration files.","intents":["Configure API keys and select LLM providers without editing code","Switch between different models for chat and embeddings","Enable/disable features (semantic search, agents, document parsing) based on subscription tier"],"best_for":["non-technical users setting up Obsidian Copilot for the first time","teams managing Obsidian Copilot across multiple users with different provider preferences","developers testing different LLM providers and models"],"limitations":["Settings UI does not validate API keys before saving—invalid keys are only discovered when used","No built-in cost estimation—users cannot see estimated costs for different model selections","Settings are stored in Obsidian's local storage—no cloud sync across devices","No settings versioning—configuration changes are not tracked or auditable","Complex settings (like self-hosted backend URLs) are error-prone and lack validation"],"requires":["Obsidian 0.15.0+","API keys for selected providers (or local Ollama/LM Studio instance)"],"input_types":["provider selection (enum)","API keys (text)","model selection (enum)","feature toggles (boolean)"],"output_types":["CopilotSettings object (configuration)","validated settings (with error messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_2","uri":"capability://memory.knowledge.context.aware.chat.with.selective.note.folder.tag.inclusion","name":"context-aware chat with selective note/folder/tag inclusion","description":"Enables users to explicitly select which notes, folders, or tags should be included as context for each chat message. The Chat Input and Context Control system (DeepWiki) allows users to toggle context sources on/off before sending a message, building a context envelope that's passed to the LLM. This prevents token waste on irrelevant notes while maintaining fine-grained control over what the AI can see.","intents":["Ask questions about specific notes or folders without cluttering the prompt with the entire vault","Isolate conversations to a project or topic by limiting context to relevant tags","Prevent the AI from accessing sensitive notes by excluding them from context"],"best_for":["users with large vaults who need to scope conversations to specific projects","teams with mixed public/private notes who want granular access control","developers building context-aware agents that need to respect note boundaries"],"limitations":["Context selection is per-message, not per-conversation—must re-select context for each message","No automatic context suggestion based on query relevance—users must manually select","Context envelope size is not displayed before sending, risking token limit overruns","No conflict detection if selected notes have contradictory information"],"requires":["Obsidian 0.15.0+","At least one LLM provider API key","Notes organized with folders or tags for effective scoping"],"input_types":["chat message (text)","selected notes (file references)","selected folders (directory references)","selected tags (tag references)"],"output_types":["context envelope (structured object with selected sources)","LLM response with context applied"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_3","uri":"capability://planning.reasoning.react.style.autonomous.agent.with.tool.calling.loop","name":"react-style autonomous agent with tool-calling loop","description":"Implements a ReAct (Reasoning + Acting) agent loop that iteratively calls tools (vault search, web search, composer edits) based on LLM reasoning. The Tool System and Autonomous Agents subsystem (DeepWiki) manages tool registration, execution, and result feedback. The agent reasons about which tool to use, executes it, observes the result, and decides whether to continue or return a final answer. This enables multi-step problem solving without user intervention.","intents":["Answer complex questions that require searching the vault, web, and then synthesizing results","Automatically find and apply relevant information from multiple sources","Execute multi-step writing tasks (research, draft, edit) without manual context switching"],"best_for":["users with Copilot Plus tier who need autonomous research and writing","teams building knowledge synthesis workflows (research → draft → edit)","developers implementing agentic RAG systems within Obsidian"],"limitations":["Requires Copilot Plus subscription—not available in free tier","Agent loops can exceed token limits on large vaults or complex queries (no built-in loop termination)","Tool availability depends on configuration (web search requires Perplexity API, composer requires write permissions)","No explicit cost tracking—agent loops can consume significant API credits without user awareness","Agent reasoning is opaque to users—no visibility into tool selection logic or intermediate steps"],"requires":["Obsidian 0.15.0+","Copilot Plus subscription","LLM provider API key (OpenAI, Anthropic recommended for reasoning capability)","Optional: Perplexity API key for web search tool","Optional: write permissions on vault for composer tool"],"input_types":["user query (text)","tool definitions (schema)","vault context (notes, folders)"],"output_types":["final synthesized answer (text)","tool execution trace (optional, for debugging)","edited notes (if composer tool is used)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_4","uri":"capability://text.generation.language.ai.assisted.note.editing.with.diff.preview.and.one.click.application","name":"ai-assisted note editing with diff preview and one-click application","description":"The Composer system (DeepWiki: Tool System and Autonomous Agents) allows users to request AI edits to notes, which are previewed as diffs before application. Users can accept, reject, or modify suggested edits. The system integrates with the note editor, applying changes atomically and maintaining undo history. This enables AI-assisted writing (summarization, expansion, tone adjustment) without overwriting original content until explicitly approved.","intents":["Get AI suggestions for improving note content (grammar, clarity, tone) before committing changes","Automatically expand bullet points into full paragraphs or vice versa","Apply batch edits to multiple notes with preview and approval workflow"],"best_for":["writers and researchers who want AI feedback without losing original content","teams with editing workflows that require approval before changes are committed","users building AI-assisted writing pipelines within Obsidian"],"limitations":["Diff preview only shows changes, not full context—large edits may be hard to evaluate","No multi-note batch editing in free tier—requires Copilot Plus for autonomous agent edits","Composer tool requires write permissions on the vault—read-only vaults cannot use this feature","No version history beyond Obsidian's native undo—previous versions are not stored","Large edits (>10KB) may exceed token limits or cause UI lag during preview rendering"],"requires":["Obsidian 0.15.0+","At least one LLM provider API key","Write permissions on the vault","Optional: Copilot Plus for autonomous agent-driven edits"],"input_types":["note content (text)","edit instruction (text prompt)","selected text range (optional)"],"output_types":["diff preview (structured change representation)","edited note content (text)","change metadata (timestamp, model used)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_5","uri":"capability://text.generation.language.custom.command.system.with.markdown.based.prompt.templates.and.variable.substitution","name":"custom command system with markdown-based prompt templates and variable substitution","description":"Allows users to define custom commands as Markdown files with templated prompts that support variable substitution (e.g., {{selectedText}}, {{fileName}}, {{date}}). The Command System (DeepWiki) parses these templates at runtime, substitutes variables from the current context, and executes the resulting prompt. This enables users to create reusable AI workflows (e.g., 'summarize this note', 'generate outline') without writing code.","intents":["Create reusable AI workflows for common tasks (summarization, outline generation, tone adjustment)","Build context-aware prompts that automatically include the current note name, date, or selected text","Share prompt templates with team members as Markdown files"],"best_for":["non-technical users who want to customize AI behavior without coding","teams with standardized writing or analysis workflows","power users building personal AI assistant templates"],"limitations":["Variable substitution is limited to predefined variables (selectedText, fileName, date, etc.)—no custom logic","No conditional logic in templates—cannot branch based on note properties or content","Template syntax is Markdown-based, not a full templating language (no loops, filters, or functions)","No built-in validation of template syntax—malformed templates fail silently at runtime","Commands are stored as Markdown files in the vault, mixing configuration with content"],"requires":["Obsidian 0.15.0+","At least one LLM provider API key","Basic Markdown knowledge to create templates"],"input_types":["template definition (Markdown file)","context variables (current note, selection, date, etc.)"],"output_types":["substituted prompt (text)","LLM response (text)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_6","uri":"capability://image.visual.vision.capable.chat.with.image.attachment.and.understanding","name":"vision-capable chat with image attachment and understanding","description":"Allows users to attach images to chat messages and send them to vision-capable LLM models (GPT-4V, Claude 3, Gemini Vision, etc.). The system handles image encoding, provider-specific vision API formatting, and response streaming. Images are embedded inline in the chat history and can be referenced in follow-up messages. This enables users to ask questions about diagrams, screenshots, or visual content within their vault.","intents":["Ask questions about diagrams, screenshots, or images attached to notes","Extract text or data from images using OCR-capable vision models","Analyze visual content (charts, graphs, designs) and get AI-generated insights"],"best_for":["researchers and students analyzing visual content (diagrams, charts, screenshots)","teams using Obsidian for design documentation with visual assets","users building knowledge bases with images that need AI-powered analysis"],"limitations":["Vision capability depends on selected LLM provider—not all providers support vision (Ollama, LM Studio do not)","Image encoding adds ~100-500ms latency depending on image size and provider","No image optimization—large images (>10MB) may exceed provider limits or cause timeouts","Vision models have different image understanding capabilities—results vary significantly by provider","No built-in image storage or versioning—images are referenced from the vault but not deduplicated"],"requires":["Obsidian 0.15.0+","Vision-capable LLM provider (OpenAI GPT-4V, Anthropic Claude 3, Google Gemini, etc.)","API key for the selected vision provider"],"input_types":["image file (PNG, JPEG, WebP, GIF)","chat message (text)","image from vault or external URL"],"output_types":["text response analyzing the image","extracted text from image (OCR)","structured data (e.g., JSON) extracted from visual content"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_7","uri":"capability://memory.knowledge.persistent.chat.history.with.markdown.note.storage.and.retrieval","name":"persistent chat history with markdown note storage and retrieval","description":"Stores chat conversations as Markdown notes in the vault, enabling users to review, search, and reference past conversations. The Chat Persistence and History subsystem (DeepWiki) saves each conversation as a timestamped note with metadata (model used, context sources, etc.). Users can search chat history using Obsidian's native search and link to conversations from other notes. This creates a persistent knowledge artifact from AI interactions.","intents":["Review past AI conversations and decisions without losing context","Search chat history to find previous answers to similar questions","Link to specific conversations from notes to maintain decision rationale"],"best_for":["researchers and analysts who need to audit AI-assisted decisions","teams building institutional knowledge from AI interactions","users who want to preserve AI conversations as part of their knowledge base"],"limitations":["Chat history is stored as Markdown, not structured data—querying requires text search, not semantic search","No automatic cleanup of old conversations—vault can accumulate large numbers of chat notes","Chat notes are not deduplicated—similar conversations create duplicate content","No privacy controls on chat history—all conversations are stored in the vault with full context","Metadata (model used, tokens consumed, cost) is not automatically tracked in chat notes"],"requires":["Obsidian 0.15.0+","Write permissions on the vault to store chat notes","Sufficient disk space for chat history (each conversation can be 10-100KB depending on length)"],"input_types":["chat message (text)","context sources (notes, folders, tags)","LLM response (text)"],"output_types":["chat note (Markdown file)","chat metadata (timestamp, model, context sources)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_8","uri":"capability://memory.knowledge.project.scoped.context.with.folder.tag.url.based.boundaries","name":"project-scoped context with folder/tag/url-based boundaries","description":"The Project System (DeepWiki: Project System) allows users to define scoped contexts from folders, tags, or external URLs. Each project has its own chat history, context sources, and configuration. This enables users to isolate conversations to specific projects (e.g., 'Research Project A', 'Client B Documentation') without mixing context. Projects are persisted and can be switched between without losing state.","intents":["Isolate conversations to specific projects to prevent context pollution","Define project-specific context sources (folders, tags) that are automatically included","Switch between projects without losing chat history or configuration"],"best_for":["teams managing multiple projects in a single Obsidian vault","researchers working on multiple concurrent studies with separate knowledge bases","consultants with multiple client projects that need isolated contexts"],"limitations":["Project switching requires manual selection—no automatic project detection based on current note","Context sources (folders, tags) are static—changes to folder structure require manual project updates","No project-level access control—all projects share the same vault permissions","Project metadata is not versioned—project configuration changes are not tracked","No built-in project templates—each project must be manually configured"],"requires":["Obsidian 0.15.0+","Copilot Plus subscription (project mode is a Plus feature)","At least one LLM provider API key"],"input_types":["project definition (folders, tags, URLs)","chat message (text)"],"output_types":["project-scoped context envelope","LLM response with project context applied"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__cap_9","uri":"capability://memory.knowledge.long.term.memory.with.persistent.agent.readable.writable.memory.notes","name":"long-term memory with persistent agent-readable/writable memory notes","description":"The Long-Term Memory feature (DeepWiki: Tool System and Autonomous Agents) allows autonomous agents to read and write persistent memory notes that persist across conversations. Agents can store facts, decisions, or context in dedicated memory notes and retrieve them in future conversations. This enables agents to build on previous interactions and maintain continuity across sessions without requiring users to manually provide context.","intents":["Enable agents to remember facts and decisions from previous conversations","Build persistent context that agents can reference in future interactions","Create an audit trail of agent-generated insights and decisions"],"best_for":["users with Copilot Plus who want agents to learn from past interactions","teams building long-running research or analysis projects with agents","developers implementing stateful agentic workflows within Obsidian"],"limitations":["Requires Copilot Plus subscription—not available in free tier","Memory notes are not automatically cleaned up—can accumulate stale or contradictory information","No conflict resolution if agent writes contradictory information to memory","Memory retrieval is not semantic—agents cannot search memory by meaning, only by note title/tags","No access control on memory notes—all agents can read/write all memory notes"],"requires":["Obsidian 0.15.0+","Copilot Plus subscription","LLM provider API key (OpenAI, Anthropic recommended for reasoning)","Write permissions on the vault for agent memory writes"],"input_types":["agent reasoning (text)","memory note references (file paths)","facts to store (text)"],"output_types":["memory note (Markdown file)","agent response incorporating memory context"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"obsidian-copilot__headline","uri":"capability://tool.use.integration.ai.assisted.note.taking.plugin.for.obsidian","name":"ai-assisted note-taking plugin for obsidian","description":"Obsidian Copilot is an AI-powered plugin that enhances your Obsidian vault with conversational search, question answering, and writing assistance, making it easier to manage and utilize your notes effectively.","intents":["best AI note-taking plugin","AI assistant for 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