{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"khoj","slug":"khoj","name":"Khoj","type":"agent","url":"https://khoj.dev","page_url":"https://unfragile.ai/khoj","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"khoj__cap_0","uri":"capability://memory.knowledge.multi.source.document.and.note.indexing.with.semantic.search","name":"multi-source document and note indexing with semantic search","description":"Khoj indexes local documents, notes, and files into a searchable knowledge base using semantic embeddings, enabling retrieval of contextually relevant information across heterogeneous sources (markdown, PDFs, text files, etc.). The system maintains a local or cloud-hosted vector index that maps document chunks to embeddings, allowing natural language queries to surface relevant context without keyword matching. This indexed knowledge is then injected into the agent's context window for grounded responses.","intents":["I want my AI assistant to answer questions based on my personal notes and documents, not just general knowledge","I need to search across hundreds of documents using natural language without manually organizing them","I want to build an agent that stays grounded in my organization's internal knowledge base"],"best_for":["knowledge workers managing large document collections","teams building internal AI assistants with proprietary knowledge","developers creating RAG-based agents with self-hosted control"],"limitations":["Indexing latency scales with document corpus size; no incremental indexing details provided","Semantic search quality depends on embedding model choice; no comparison of embedding models offered","No documented support for real-time document updates or change detection","Vector index storage requirements not specified; unclear scaling characteristics"],"requires":["Document files in supported formats (markdown, PDF, text)","Embedding model API access or local embedding service","Storage for vector index (local disk or cloud backend)","Python 3.8+ for self-hosted deployment"],"input_types":["markdown files","PDF documents","plain text files","natural language queries"],"output_types":["ranked document chunks with relevance scores","context-injected LLM responses","structured metadata about source documents"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_1","uri":"capability://search.retrieval.web.search.and.online.content.retrieval.with.agent.integration","name":"web search and online content retrieval with agent integration","description":"Khoj enables the agent to search the web in real-time and retrieve current information from online sources, augmenting local knowledge with live data. The agent can invoke web search as a tool during reasoning, fetching and parsing search results to answer questions about current events, recent publications, or information not present in local documents. Search results are ranked and summarized before injection into the LLM context.","intents":["I want my AI assistant to answer questions about current events and recent news","I need the agent to research topics online and synthesize information from multiple web sources","I want to combine my personal knowledge base with real-time web data in a single query"],"best_for":["researchers and analysts needing current information synthesis","customer support agents requiring up-to-date product/service information","content creators researching trending topics"],"limitations":["Web search quality depends on underlying search provider (Google, Bing, etc.); no comparison provided","No documented filtering for misinformation or source credibility assessment","Search result parsing may fail on dynamically-rendered or JavaScript-heavy websites","Rate limiting and quota constraints from search providers not documented"],"requires":["Internet connectivity","Web search API key (Google Custom Search, Bing Search, or similar)","Agent framework supporting tool invocation"],"input_types":["natural language queries","search filters or constraints"],"output_types":["ranked web search results with snippets","parsed and summarized web content","citations with source URLs"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_10","uri":"capability://data.processing.analysis.structured.data.extraction.from.documents.and.web.content","name":"structured data extraction from documents and web content","description":"Khoj can extract structured information (entities, relationships, tables, metadata) from documents and web content using LLM-based extraction with optional schema guidance. Extracted data can be formatted as JSON, CSV, or other structured formats, enabling integration with downstream systems. The extraction process can be applied to individual documents or batched across large collections.","intents":["I want to extract contact information, dates, and entities from a collection of documents","I need to convert unstructured text into structured data for database import","I want to extract tables or data from PDFs and convert them to CSV"],"best_for":["data teams processing unstructured documents for data warehousing","researchers extracting metadata from academic papers or reports","business analysts converting documents into structured formats for analysis"],"limitations":["Extraction accuracy depends on LLM capability and document clarity; no accuracy metrics or benchmarks provided","Schema definition and validation not documented; unclear how to specify extraction requirements","Handling of ambiguous or conflicting information not specified","No documented support for complex nested structures or relationships","Batch processing performance and cost implications not documented"],"requires":["Configured LLM provider","Document collection in supported formats","Optional: schema definitions for structured extraction"],"input_types":["unstructured documents (text, PDF, web content)","extraction schemas or instructions","batch specifications"],"output_types":["JSON or CSV structured data","extracted entities and relationships","metadata and annotations"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_11","uri":"capability://tool.use.integration.model.configuration.and.parameter.tuning","name":"model configuration and parameter tuning","description":"Allows users to configure LLM parameters (temperature, top-p, max tokens, etc.) and embedding model selection to tune assistant behavior and performance. Provides configuration interfaces for adjusting generation quality, response length, and semantic search sensitivity without code changes.","intents":["I want to adjust how creative or deterministic the assistant's responses are","I need to control response length and token usage","I want to fine-tune search sensitivity for my knowledge base"],"best_for":["advanced users optimizing assistant behavior","developers tuning model performance","organizations managing inference costs"],"limitations":["Parameter tuning guidance not provided — users must understand LLM parameters","No automated parameter optimization or recommendation system","Impact of parameter changes on quality/cost not quantified","Embedding model tuning options likely limited"],"requires":["Understanding of LLM parameters (temperature, top-p, etc.)","Configuration interface or file access","Knowledge of model-specific parameter ranges"],"input_types":["parameter configuration","model selection","tuning values"],"output_types":["adjusted model behavior","generation quality changes","token usage metrics"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_2","uri":"capability://tool.use.integration.multi.model.llm.abstraction.with.provider.agnostic.agent.configuration","name":"multi-model llm abstraction with provider-agnostic agent configuration","description":"Khoj abstracts away LLM provider differences through a unified interface, allowing users to configure any supported model (OpenAI, Anthropic, Ollama, local models, etc.) as the agent backbone. The system handles prompt formatting, token counting, and API calls transparently, enabling users to swap models without changing agent logic or tool definitions. This abstraction supports both cloud-hosted and self-hosted model deployment.","intents":["I want to use my preferred LLM provider without rewriting my agent code","I need to run a private LLM locally without sending data to cloud APIs","I want to experiment with different models (GPT-4, Claude, Llama) for the same agent tasks"],"best_for":["developers building model-agnostic AI applications","organizations with data privacy requirements necessitating local model deployment","teams evaluating multiple LLM providers for cost and performance"],"limitations":["Model-specific capabilities (vision, function calling) may not be uniformly supported across all providers","Prompt formatting differences between models can affect output quality; no automatic prompt optimization provided","Token counting accuracy varies by model; no built-in token budget enforcement documented","Latency and cost characteristics differ significantly between providers; no cost estimation or benchmarking tools provided"],"requires":["API key for at least one supported LLM provider (OpenAI, Anthropic, etc.) OR local model server (Ollama, vLLM)","Configuration file specifying model selection and parameters","Network access to model provider or local model endpoint"],"input_types":["model configuration (provider, model name, API key)","system prompts and user queries"],"output_types":["LLM completions","structured outputs (JSON, function calls)","token usage metrics"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_3","uri":"capability://memory.knowledge.conversational.context.management.with.multi.turn.memory","name":"conversational context management with multi-turn memory","description":"Khoj maintains conversation history across multiple turns, managing context windows and token budgets to keep relevant prior exchanges accessible to the agent while respecting model token limits. The system implements context compression or summarization strategies to preserve conversation coherence without exceeding token budgets. Memory can be persisted across sessions for long-term conversation continuity.","intents":["I want my AI assistant to remember previous questions and answers in the same conversation","I need the agent to maintain context across multiple interactions without losing important details","I want conversation history to persist so I can resume discussions later"],"best_for":["interactive chat applications requiring conversation continuity","personal assistants that learn from user interaction patterns","customer support systems maintaining ticket-level context"],"limitations":["Context window size limits how much history can be retained; no automatic summarization strategy documented","Long conversations may require expensive context compression or summarization, increasing latency and cost","No documented mechanism for selective context pruning or importance-based retention","Persistence layer not specified; unclear if conversation history is encrypted or how long it's retained"],"requires":["Storage backend for conversation history (local database, cloud storage, or in-memory)","Token counting mechanism for the selected LLM","Session management system to track conversation threads"],"input_types":["user messages","agent responses","metadata (timestamps, user IDs)"],"output_types":["conversation history with turn-by-turn exchanges","context summaries for token budget management","session identifiers for persistence"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_4","uri":"capability://text.generation.language.content.generation.and.writing.assistance.with.template.support","name":"content generation and writing assistance with template support","description":"Khoj can generate written content (emails, blog posts, summaries, etc.) using the configured LLM, optionally grounded in indexed documents or web search results. The system supports templates and structured prompts to guide content generation toward specific formats or styles. Generated content can be edited, refined, and exported in multiple formats.","intents":["I want to generate blog posts or articles based on my research notes","I need to draft emails or documents quickly using my personal knowledge as context","I want to summarize long documents or research into concise formats"],"best_for":["content creators and writers seeking AI-assisted drafting","knowledge workers automating routine writing tasks","teams generating documentation from internal knowledge bases"],"limitations":["Generated content quality depends on LLM capability and prompt engineering; no quality metrics or evaluation framework provided","No built-in fact-checking or hallucination detection for generated content","Template system not documented; unclear what customization options exist","No version control or collaborative editing features mentioned"],"requires":["Configured LLM provider","Optional: indexed documents or web search for grounding","Template definitions (if using structured generation)"],"input_types":["writing prompts or instructions","template specifications","source documents for grounding"],"output_types":["generated text in various formats (markdown, HTML, plain text)","structured outputs (JSON, YAML)","citations and source references"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_5","uri":"capability://automation.workflow.task.automation.and.scheduling.with.local.execution","name":"task automation and scheduling with local execution","description":"Khoj (via the Pipali product) can schedule and execute automated tasks on a local machine, such as periodic research, document processing, or data collection. Tasks run 'safely on your computer' with defined execution schedules and can integrate with local tools and scripts. The system manages task state, logging, and error handling for autonomous execution.","intents":["I want to schedule my AI assistant to run research tasks daily and summarize findings","I need to automate document processing or data collection on a regular schedule","I want the agent to execute tasks locally without cloud dependencies"],"best_for":["power users automating personal research and information gathering","teams running autonomous data collection or processing pipelines","organizations requiring local-only task execution for compliance"],"limitations":["Task execution environment and sandboxing details not documented; unclear what system access tasks have","No documented error recovery or retry mechanisms for failed tasks","Scheduling granularity and maximum task complexity not specified","No monitoring or alerting system mentioned for task failures","Local execution requires the machine to be running; no cloud fallback documented"],"requires":["Khoj/Pipali installed locally","Machine running continuously or scheduled wake-up capability","Task definitions in supported format (not specified)","Optional: local tools or scripts to integrate with tasks"],"input_types":["task definitions (schedule, actions, parameters)","integration specifications for local tools"],"output_types":["task execution logs","generated reports or data","status notifications"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_6","uri":"capability://text.generation.language.natural.language.query.interface.with.context.aware.responses","name":"natural language query interface with context-aware responses","description":"Khoj provides a conversational chat interface where users ask questions in natural language and receive contextually grounded answers. The agent processes queries by combining indexed document search, optional web search, and LLM reasoning to synthesize responses. Responses include citations to source documents or web results, enabling users to verify information and explore sources.","intents":["I want to ask my AI assistant questions and get answers grounded in my personal knowledge","I need to understand where information comes from; I want citations for every answer","I want a conversational interface that feels like talking to a knowledgeable colleague"],"best_for":["individual knowledge workers seeking a personal AI assistant","teams building internal Q&A systems over proprietary knowledge","researchers needing grounded information synthesis"],"limitations":["Answer quality depends on indexed document coverage; questions about missing topics will lack grounding","Citation accuracy not guaranteed; LLM may hallucinate sources or misattribute information","No explicit confidence scoring or uncertainty quantification in responses","Latency varies based on document corpus size and search complexity; no SLA or performance guarantees"],"requires":["Indexed documents or knowledge base","Configured LLM provider","Optional: web search API for real-time information"],"input_types":["natural language questions","follow-up queries"],"output_types":["natural language responses","source citations with document references","confidence or relevance scores (if supported)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_7","uri":"capability://automation.workflow.multi.platform.deployment.with.self.hosted.and.cloud.options","name":"multi-platform deployment with self-hosted and cloud options","description":"Khoj can be deployed as a self-hosted application (on personal machines, servers, or containers) or accessed as a cloud service, giving users flexibility in infrastructure choice. Self-hosted deployment provides full data control and privacy, while cloud deployment offers convenience and reduced operational overhead. The same agent logic works across both deployment modes.","intents":["I want to run Khoj on my own infrastructure without sending data to external servers","I need a managed cloud service for Khoj without worrying about infrastructure","I want to migrate between self-hosted and cloud deployment without changing my agent configuration"],"best_for":["organizations with strict data privacy or compliance requirements","individual users wanting full control over their AI assistant","teams evaluating deployment options before committing to infrastructure"],"limitations":["Self-hosted deployment requires operational expertise; no managed service support documented","Resource requirements for self-hosted deployment not specified (CPU, RAM, disk, bandwidth)","Cloud deployment pricing and SLA not documented","Data synchronization between self-hosted and cloud instances not mentioned","No documented migration path or tooling for moving between deployment modes"],"requires":["For self-hosted: Python 3.8+, Docker (optional), or native installation","For cloud: account creation and authentication","Sufficient compute resources for self-hosted (specs not provided)"],"input_types":["deployment configuration (self-hosted vs. cloud)","infrastructure specifications (for self-hosted)"],"output_types":["deployed Khoj instance","API endpoints or web interface","deployment logs and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_8","uri":"capability://memory.knowledge.integration.with.note.taking.and.productivity.tools","name":"integration with note-taking and productivity tools","description":"Khoj integrates with popular note-taking systems (Obsidian, Logseq, Roam Research, etc.) and productivity tools, automatically indexing notes and enabling the agent to access and reason over personal knowledge graphs. Integration typically works through file system access or API connections, keeping the knowledge base synchronized with the user's existing tools.","intents":["I want my AI assistant to understand and reference my Obsidian vault without manual exports","I need the agent to stay synchronized with my note-taking system as I add new notes","I want to use Khoj as a search and reasoning layer on top of my existing knowledge management system"],"best_for":["knowledge workers using note-taking systems as primary information repositories","researchers maintaining interconnected knowledge graphs","teams using shared note-taking platforms for collaborative knowledge management"],"limitations":["Integration quality depends on tool-specific APIs; some tools may have limited or undocumented integration points","Synchronization latency not specified; unclear how quickly new notes are indexed","No conflict resolution mechanism documented for notes edited in multiple places","Supported tools list not provided; unclear which note-taking systems are compatible"],"requires":["Khoj installation","Supported note-taking tool (Obsidian, Logseq, etc.)","File system access or API credentials for the note-taking tool"],"input_types":["notes in markdown or proprietary formats","knowledge graph structure (if supported)"],"output_types":["indexed notes in vector database","agent responses referencing note content","backlinks or knowledge graph visualizations (if supported)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__cap_9","uri":"capability://planning.reasoning.research.automation.and.information.synthesis","name":"research automation and information synthesis","description":"Khoj can autonomously conduct research tasks by combining web search, document retrieval, and LLM reasoning to gather and synthesize information on specified topics. The agent can be configured to research topics, compare sources, identify gaps, and produce structured research summaries. Research tasks can be scheduled to run periodically, building up research dossiers over time.","intents":["I want the agent to research a topic and provide a comprehensive summary with sources","I need to monitor a topic over time and get weekly research updates","I want to compare information across multiple sources and identify contradictions or gaps"],"best_for":["researchers and analysts conducting competitive intelligence or market research","content creators gathering research for articles or reports","teams monitoring topics or trends for strategic decision-making"],"limitations":["Research quality depends on web search coverage and source credibility; no built-in fact-checking","No documented mechanism for identifying or handling misinformation or biased sources","Research depth and comprehensiveness not guaranteed; unclear how the agent determines when research is complete","Synthesis quality varies; no evaluation framework or quality metrics provided","Cost implications of extensive web search not documented"],"requires":["Web search API access","Configured LLM provider","Optional: indexed documents for supplementary context"],"input_types":["research topics or questions","research parameters (scope, depth, time period)","scheduling specifications"],"output_types":["research summaries with structured findings","source citations and links","comparison matrices or analysis reports","research dossiers or archives"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"khoj__headline","uri":"capability://automation.workflow.ai.personal.assistant.for.research.and.content.generation","name":"ai personal assistant for research and content generation","description":"Khoj is an open-source AI personal assistant that connects to your notes and online content, providing contextual answers, generating content, and automating research tasks, making it ideal for users seeking a versatile tool for information management.","intents":["best AI personal assistant","AI assistant for research tasks","AI tool for content generation","open-source personal assistant for notes","cloud-based AI assistant for document management"],"best_for":["research automation","content generation","note organization"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Document files in supported formats (markdown, PDF, text)","Embedding model API access or local embedding service","Storage for vector index (local disk or cloud backend)","Python 3.8+ for self-hosted deployment","Internet connectivity","Web search API key (Google Custom Search, Bing Search, or similar)","Agent framework supporting tool invocation","Configured LLM provider","Document collection in supported formats","Optional: schema definitions for structured extraction"],"failure_modes":["Indexing latency scales with document corpus size; no incremental indexing details provided","Semantic search quality depends on embedding model choice; no comparison of embedding models offered","No documented support for real-time document updates or change detection","Vector index storage requirements not specified; unclear scaling characteristics","Web search quality depends on underlying search provider (Google, Bing, etc.); no comparison provided","No documented filtering for misinformation or source credibility assessment","Search result parsing may fail on dynamically-rendered or JavaScript-heavy websites","Rate limiting and quota constraints from search providers not documented","Extraction accuracy depends on LLM capability and document clarity; no accuracy metrics or benchmarks provided","Schema definition and validation not documented; unclear how to specify extraction requirements","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=khoj","compare_url":"https://unfragile.ai/compare?artifact=khoj"}},"signature":"4M2K6Bu0/Uy7nrbFrm2mIYFr4BLfUZI3IHfRJEa9XKUtki3N6fcOwWDQBnQkC6AAGyVXYp4MV3A3q/aphoqgCw==","signedAt":"2026-06-22T00:31:21.493Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/khoj","artifact":"https://unfragile.ai/khoj","verify":"https://unfragile.ai/api/v1/verify?slug=khoj","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"}}