{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-itzcrazykns--vane","slug":"itzcrazykns--vane","name":"Vane","type":"agent","url":"https://github.com/ItzCrazyKns/Vane","page_url":"https://unfragile.ai/itzcrazykns--vane","categories":["chatbots-assistants"],"tags":["ai-agents","ai-search-engine","answering-engine","artificial-intelligence","llm","machine-learning","open-source-ai-search-engine","perplexica","rag","search-engine","searxng","searxng-copilot","self-hosted-ai","vane"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-itzcrazykns--vane__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.provider.agnostic.inference","name":"multi-provider llm abstraction with provider-agnostic inference","description":"Vane implements a unified provider abstraction layer (src/lib/models/providers) that normalizes API calls across 8+ LLM providers including OpenAI, Anthropic, Google Gemini, Groq, Ollama, LMStudio, and Lemonade. The system uses a provider factory pattern to instantiate the correct client based on configuration, handling provider-specific request/response formatting, streaming protocols, and error handling transparently. This allows swapping providers via environment variables without code changes, enabling cost optimization and fallback strategies.","intents":["Switch between local and cloud LLMs without rewriting agent code","Balance privacy (local Ollama) vs performance (cloud providers) dynamically","Implement provider failover or cost-based routing across multiple LLM backends","Support both streaming and non-streaming inference patterns uniformly"],"best_for":["Teams building privacy-sensitive search applications","Developers optimizing for cost by mixing local and cloud models","Organizations requiring vendor lock-in avoidance"],"limitations":["Provider-specific features (vision, function calling) require adapter code per provider","Streaming response handling adds ~50-100ms latency due to normalization layer","No built-in token counting or cost estimation across providers"],"requires":["Node.js 18+","API keys for selected providers (OpenAI, Anthropic, Google, Groq) OR local Ollama/LMStudio instance","Environment variables for provider selection and credentials"],"input_types":["text prompts","system messages","conversation history (multi-turn)"],"output_types":["text completions","streaming text chunks","structured JSON (when provider supports)"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_1","uri":"capability://search.retrieval.privacy.preserving.web.search.via.searxng.meta.search.integration","name":"privacy-preserving web search via searxng meta-search integration","description":"Vane integrates SearXNG (src/lib/searxng.ts), a privacy-respecting meta-search engine, to perform web queries without sending user data to Google, Bing, or other commercial search engines. The integration abstracts SearXNG's HTTP API, handling query formatting, result parsing, and deduplication of results across multiple search backends that SearXNG aggregates. Results are streamed back to the agent with source attribution, enabling the LLM to synthesize answers from multiple sources without exposing user queries to surveillance-based search providers.","intents":["Perform web searches without leaking queries to commercial search engines","Aggregate results from multiple search backends (Google, Bing, DuckDuckGo, etc.) via SearXNG","Retrieve search results with source URLs for citation and fact-checking","Support domain-specific search filtering (restrict to academic papers, news, discussions)"],"best_for":["Privacy-conscious organizations and individuals","Deployments in regulated environments (GDPR, HIPAA)","Self-hosted search applications requiring no external data leakage"],"limitations":["SearXNG instance must be self-hosted or accessed via trusted third-party; no built-in SearXNG hosting","Search result quality depends on underlying SearXNG configuration and backend availability","No real-time indexing; results lag behind commercial search engines by hours to days","Rate limiting on SearXNG instance can throttle concurrent searches"],"requires":["Running SearXNG instance (Docker or source) accessible via HTTP","SearXNG URL configured in environment variables","Network connectivity from Vane backend to SearXNG instance"],"input_types":["text search queries","domain filters (optional)","search mode parameters (speed, balanced, quality)"],"output_types":["structured search results (title, URL, snippet, source)","ranked result lists","deduplicated result sets"],"categories":["search-retrieval","privacy-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_10","uri":"capability://automation.workflow.real.time.streaming.responses.via.server.sent.events","name":"real-time streaming responses via server-sent events","description":"Vane streams research results and answer synthesis in real-time to the client using Server-Sent Events (SSE) rather than waiting for complete answer generation. The backend emits events for each research step (search initiated, results retrieved, synthesis started, answer chunk generated), allowing the client to display progress and partial results immediately. The useChat hook (src/app/c/[chatId]/hooks/useChat.ts) handles SSE event parsing and state updates, enabling smooth real-time UI updates without polling or WebSocket complexity.","intents":["Display research progress to users in real-time","Show partial answers as they are being synthesized","Provide feedback during long-running research queries","Enable cancellation of in-progress research"],"best_for":["Web applications requiring real-time feedback","Mobile applications with unreliable connections (SSE is more resilient than WebSocket)","Applications with long-running research queries (5+ seconds)"],"limitations":["SSE is unidirectional (server to client); client messages require separate HTTP requests","Browser SSE support is universal but some proxies/firewalls block streaming responses","Event parsing adds ~10-20ms latency per event","No built-in event ordering guarantees; out-of-order events can cause UI glitches"],"requires":["Next.js route handler supporting streaming responses","Client-side SSE event listener implementation","Browser with SSE support (all modern browsers)"],"input_types":["research query","search mode"],"output_types":["SSE events (JSON-formatted)","event types: search_started, results_retrieved, synthesis_started, answer_chunk, citations, complete"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_11","uri":"capability://memory.knowledge.multi.turn.conversation.context.with.follow.up.question.support","name":"multi-turn conversation context with follow-up question support","description":"Vane maintains multi-turn conversation context by storing previous messages and citations in SQLite, passing conversation history to the LLM for each new query. The research agent uses conversation context to understand follow-up questions (e.g., 'Tell me more about X' refers to previous answer), refine searches based on prior results, and avoid redundant research. The system tracks which sources were already cited to avoid repetition and enables the LLM to make context-aware decisions about which new sources to research.","intents":["Ask follow-up questions that reference previous answers","Refine research based on prior results without re-searching","Maintain conversation coherence across multiple turns","Avoid redundant research on already-explored topics"],"best_for":["Conversational search applications with iterative refinement","Research applications requiring deep exploration of topics","Applications serving expert users conducting extended research sessions"],"limitations":["Conversation history is stored in SQLite; no distributed conversation state across multiple backend instances","LLM context window limits conversation length; very long conversations require summarization","No automatic conversation summarization; users must manually manage context","Context window usage grows linearly with conversation length, increasing per-query latency"],"requires":["SQLite database with conversation history schema","LLM provider with sufficient context window (8K+ tokens)","Session management to track conversation ID"],"input_types":["new user query","conversation history (previous messages and citations)"],"output_types":["context-aware answer","citations (new and referenced)","conversation history update"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_12","uri":"capability://tool.use.integration.configurable.model.provider.selection.with.environment.based.switching","name":"configurable model provider selection with environment-based switching","description":"Vane allows switching between LLM providers via environment variables (e.g., PROVIDER=openai, PROVIDER=ollama) without code changes. The configuration system (src/lib/models/providers) reads provider settings from environment variables, instantiates the appropriate provider client, and passes it to the research agent. This enables different deployment configurations: development with local Ollama, staging with Anthropic, production with OpenAI, all from the same codebase. Provider-specific settings (API keys, model names, temperature) are also environment-configurable.","intents":["Switch between local and cloud LLMs for development vs production","Test answer quality across different providers without code changes","Deploy different provider configurations to different environments","Implement provider failover by restarting with different environment variables"],"best_for":["Teams with multi-environment deployments (dev/staging/prod)","Organizations evaluating different LLM providers","Cost-sensitive deployments wanting to optimize provider selection per environment"],"limitations":["Provider switching requires application restart; no hot-swapping","Environment variable configuration is error-prone; no validation at startup","No built-in provider health checks or automatic failover","Provider-specific features (vision, function calling) require adapter code"],"requires":["Environment variables for provider selection and credentials","Restart capability for provider switching"],"input_types":["environment variables (PROVIDER, API_KEY, MODEL_NAME, etc.)"],"output_types":["instantiated provider client","provider-specific configuration"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_2","uri":"capability://planning.reasoning.research.agent.with.multi.source.document.synthesis","name":"research agent with multi-source document synthesis","description":"Vane implements a research agent (src/lib/agents/search/researcher) that decomposes user queries into sub-research tasks, executes parallel searches across multiple source types (web, academic papers, discussions, domain-specific databases), and synthesizes results into a coherent answer with citations. The agent uses chain-of-thought reasoning to determine which sources are relevant, iteratively refines searches based on intermediate results, and tracks source provenance throughout the synthesis process. Results are streamed via Server-Sent Events, allowing real-time progress updates to the client.","intents":["Answer complex questions requiring synthesis from multiple source types","Generate cited answers with explicit source URLs and attribution","Perform iterative research (refine searches based on initial results)","Support different search modes (speed vs quality) with corresponding source selection strategies"],"best_for":["Building research-focused AI applications (academic, investigative, competitive intelligence)","Teams needing transparent, auditable answer generation with source tracking","Applications requiring multi-source synthesis without hallucination"],"limitations":["Research quality depends on SearXNG backend availability and configuration","No built-in fact-checking or source credibility scoring; relies on LLM judgment","Parallel searches can exhaust SearXNG rate limits on high-concurrency deployments","Source synthesis adds 2-5 seconds latency per query due to iterative refinement"],"requires":["SearXNG instance configured and accessible","LLM provider with sufficient context window (8K+ tokens recommended)","SQLite database for conversation history","Server-Sent Events support in client"],"input_types":["natural language queries","search mode selection (speed/balanced/quality)","optional domain filters"],"output_types":["synthesized text answer","structured citations (URL, title, snippet)","streaming progress events","conversation history"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_3","uri":"capability://planning.reasoning.search.mode.optimization.with.configurable.depth.vs.speed.tradeoffs","name":"search mode optimization with configurable depth-vs-speed tradeoffs","description":"Vane provides three search modes (Speed, Balanced, Quality) implemented in src/lib/agents/search/index.ts that adjust the research agent's behavior: Speed mode performs single-pass searches with minimal source diversity, Balanced mode uses 2-3 parallel searches across different source types, and Quality mode executes iterative refinement with 5+ searches and cross-source validation. Each mode configures the number of parallel searches, result filtering thresholds, and LLM reasoning depth, allowing users to trade latency for answer comprehensiveness without code changes.","intents":["Get quick answers for simple factual queries (weather, stock prices, definitions)","Balance latency and quality for typical research questions","Perform exhaustive research for complex or ambiguous queries requiring high confidence"],"best_for":["Applications serving diverse user needs (quick facts vs deep research)","Cost-sensitive deployments wanting to minimize LLM token usage","Real-time applications requiring configurable latency budgets"],"limitations":["Quality mode can exceed 10 seconds latency on slow SearXNG instances","Mode selection is per-query; no automatic mode selection based on query complexity","Speed mode may miss relevant sources for nuanced queries","No cost estimation per mode; token usage varies unpredictably"],"requires":["SearXNG instance with sufficient concurrency limits","LLM provider with reasonable rate limits","Client support for streaming responses (Server-Sent Events)"],"input_types":["search mode enum (speed/balanced/quality)","user query","optional domain filters"],"output_types":["synthesized answer","citations","streaming progress events","execution time metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_4","uri":"capability://text.generation.language.contextual.widget.generation.for.structured.data.queries","name":"contextual widget generation for structured data queries","description":"Vane includes a widget system (src/lib/agents/search/widgets) that detects query intent and generates contextual UI cards for structured data types: weather widgets display current conditions and forecasts, stock widgets show price and trend data, calculator widgets handle mathematical expressions, and domain-specific widgets (sports scores, flight info) render relevant data. The system uses LLM-based intent detection to determine widget type, queries specialized APIs or SearXNG for data, and returns structured JSON that the frontend renders as rich UI components rather than plain text.","intents":["Display weather forecasts, stock prices, and other structured data in rich UI format","Provide quick answers to factual queries without full research synthesis","Enhance answer readability with domain-specific visualizations"],"best_for":["General-purpose search applications requiring rich answer formatting","Mobile-first applications where structured data improves usability","Applications serving non-technical users expecting Google-like answer cards"],"limitations":["Widget coverage is limited to pre-implemented types (weather, stocks, calculator, etc.)","Adding new widget types requires backend code changes and frontend component development","Widget data freshness depends on underlying API latency (weather APIs, stock feeds)","Intent detection via LLM adds 200-500ms latency per query"],"requires":["LLM provider for intent detection","API keys for specialized data sources (weather, stocks) if not using SearXNG fallback","Frontend React components for widget rendering"],"input_types":["natural language query","query context (conversation history)"],"output_types":["structured widget JSON","fallback text answer","widget metadata (type, data fields)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_5","uri":"capability://memory.knowledge.conversation.history.persistence.with.sqlite.and.session.management","name":"conversation history persistence with sqlite and session management","description":"Vane maintains persistent conversation history using SQLite (src/lib/db) with a schema supporting multi-turn chat sessions, message storage with metadata (timestamps, sources, citations), and user preferences. The session management system (src/app/c/[chatId]) generates unique chat IDs, stores conversation state server-side, and enables resuming conversations across browser sessions. The useChat hook (src/app/c/[chatId]/hooks/useChat.ts) manages client-side state synchronization with the backend, handling optimistic updates and conflict resolution.","intents":["Persist conversation history across browser sessions and devices","Enable conversation resumption from any chat ID","Track conversation metadata (creation time, last accessed, source citations)","Support multi-turn context for follow-up questions"],"best_for":["Self-hosted deployments requiring local data persistence","Applications serving returning users with conversation continuity","Privacy-focused applications avoiding cloud-based conversation storage"],"limitations":["SQLite is single-writer; concurrent writes from multiple backend instances require external coordination","No built-in encryption at rest; conversation data is plaintext in SQLite file","Database growth unbounded; no automatic cleanup or archival of old conversations","No cross-device sync without external replication layer"],"requires":["SQLite 3.x","Writable filesystem for database file","Database migrations applied before startup"],"input_types":["chat messages (text)","search results with citations","user preferences"],"output_types":["conversation history (JSON)","session metadata","chat list with summaries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_6","uri":"capability://memory.knowledge.semantic.search.over.uploaded.documents.with.file.indexing","name":"semantic search over uploaded documents with file indexing","description":"Vane supports uploading documents (PDFs, text files) and performing semantic search over them (src/lib/uploads). The system extracts text from uploaded files, chunks documents into semantic units, generates embeddings using the configured LLM provider, stores embeddings in a vector store, and retrieves relevant chunks via similarity search. Uploaded documents are indexed per-session, enabling users to ask questions about their own documents without exposing them to external services. The search integrates with the research agent, allowing hybrid queries that combine web search with document search.","intents":["Ask questions about uploaded documents (PDFs, research papers, contracts)","Combine web search with document search for comprehensive answers","Maintain document privacy by indexing locally without external APIs"],"best_for":["Enterprise applications requiring document analysis without data exfiltration","Research applications combining user documents with web sources","Privacy-sensitive use cases (legal, medical, financial documents)"],"limitations":["Embedding generation requires LLM provider with embedding support; not all providers supported","Vector store is in-memory or file-based; no distributed vector database integration","Document chunking is naive (fixed-size chunks); no semantic chunking","Large documents (100+ pages) can exceed context window limits","No built-in document format support beyond text and PDF; requires custom parsers for other formats"],"requires":["LLM provider with embedding support (OpenAI, Anthropic, local Ollama)","File upload endpoint and storage","Vector similarity search implementation"],"input_types":["PDF files","text files","document metadata (title, author)"],"output_types":["document embeddings","semantic search results (chunks with similarity scores)","hybrid search results (web + document)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_7","uri":"capability://search.retrieval.image.and.video.search.with.media.result.integration","name":"image and video search with media result integration","description":"Vane provides image and video search capabilities (src/app/api/images, src/app/api/videos) that query SearXNG for media results and integrate them into the research pipeline. The system returns structured media metadata (URL, title, source, thumbnail) that the frontend renders as image galleries or video embeds. Media search is available as a research action, allowing the research agent to include images and videos in answer synthesis when relevant to the query.","intents":["Search for images and videos relevant to research queries","Include media in synthesized answers with proper attribution","Display image galleries and video embeds in the chat interface"],"best_for":["General-purpose search applications requiring visual content","Applications serving visual learners or mobile users","Content creation tools needing image/video sourcing"],"limitations":["Media search quality depends on SearXNG backend configuration","No image analysis or OCR; images are returned as URLs only","No video transcription or summarization","Media result freshness lags behind real-time image search engines","No copyright or license filtering; all results treated equally"],"requires":["SearXNG instance with image and video search enabled","Frontend components for image gallery and video embed rendering"],"input_types":["text search query","media type filter (image/video)"],"output_types":["structured media results (URL, title, source, thumbnail)","image gallery JSON","video embed metadata"],"categories":["search-retrieval","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_8","uri":"capability://planning.reasoning.smart.query.suggestions.powered.by.llm.based.intent.analysis","name":"smart query suggestions powered by llm-based intent analysis","description":"Vane generates contextual query suggestions (src/app/api/suggestions) by analyzing the current conversation context and user query with an LLM, predicting likely follow-up questions or related searches. The system uses the conversation history to understand context, generates 3-5 suggestions that expand or refine the current research direction, and returns them as clickable suggestions in the UI. Suggestions are generated asynchronously and streamed to the client, enabling real-time suggestion updates as the user types.","intents":["Guide users toward productive follow-up questions","Suggest related research directions based on conversation context","Reduce friction for users unsure how to refine their search"],"best_for":["Conversational search applications with iterative refinement","Applications serving non-expert users unfamiliar with search strategies","Mobile applications where typing friction is high"],"limitations":["Suggestion quality depends on LLM reasoning; can be generic or off-topic","Generating suggestions adds 500ms-1s latency per keystroke","No personalization; suggestions are based on conversation context only, not user history","Suggestions can be repetitive or suggest queries already explored"],"requires":["LLM provider with fast inference (for real-time suggestions)","Conversation history context","Client support for streaming suggestions"],"input_types":["current user query (partial or complete)","conversation history"],"output_types":["list of suggested queries (3-5 items)","suggestion metadata (relevance score, category)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-itzcrazykns--vane__cap_9","uri":"capability://search.retrieval.domain.specific.search.filtering.with.website.restrictions","name":"domain-specific search filtering with website restrictions","description":"Vane allows users to restrict searches to specific domains or website lists (src/lib/agents/search/researcher) by passing domain filters to SearXNG queries. The system supports include-list filtering (search only these domains) and exclude-list filtering (search everywhere except these domains), enabling users to focus on authoritative sources (e.g., .edu for academic research, .gov for official information) or avoid low-quality sources. Domain filters are applied at the SearXNG query level, reducing irrelevant results before the LLM synthesis stage.","intents":["Search only academic sources (.edu, .org research sites)","Restrict to official government information (.gov domains)","Exclude low-quality sources (content farms, spam sites)","Focus on domain-specific expertise (e.g., StackOverflow for programming)"],"best_for":["Research applications requiring source credibility filtering","Academic and professional use cases with domain-specific authority","Applications serving expert users with specific source preferences"],"limitations":["Domain filtering is coarse-grained; no per-source credibility scoring","SearXNG must support domain filtering in its configuration","Exclude-list filtering can miss relevant sources on non-standard domains","No automatic domain categorization; users must manually specify domains"],"requires":["SearXNG instance with domain filtering support","User interface for domain filter input"],"input_types":["domain include-list (optional)","domain exclude-list (optional)","search query"],"output_types":["filtered search results","source domain metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API keys for selected providers (OpenAI, Anthropic, Google, Groq) OR local Ollama/LMStudio instance","Environment variables for provider selection and credentials","Running SearXNG instance (Docker or source) accessible via HTTP","SearXNG URL configured in environment variables","Network connectivity from Vane backend to SearXNG instance","Next.js route handler supporting streaming responses","Client-side SSE event listener implementation","Browser with SSE support (all modern browsers)","SQLite database with conversation history schema"],"failure_modes":["Provider-specific features (vision, function calling) require adapter code per provider","Streaming response handling adds ~50-100ms latency due to normalization layer","No built-in token counting or cost estimation across providers","SearXNG instance must be self-hosted or accessed via trusted third-party; no built-in SearXNG hosting","Search result quality depends on underlying SearXNG configuration and backend availability","No real-time indexing; results lag behind commercial search engines by hours to days","Rate limiting on SearXNG instance can throttle concurrent searches","SSE is unidirectional (server to client); client messages require separate HTTP requests","Browser SSE support is universal but some proxies/firewalls block streaming responses","Event parsing adds ~10-20ms latency per event","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7903495812094257,"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":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-04-11T14:33:06Z"},"community":{"stars":34118,"forks":3718,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=itzcrazykns--vane","compare_url":"https://unfragile.ai/compare?artifact=itzcrazykns--vane"}},"signature":"jlKBRNKPgfKEAmZwewI6E34Dt67AK5HAyU/fUQtdZrzkgt0tiiZkl6Cw0ALezLUin+JL3z9zbRxMtPrlHtWWCQ==","signedAt":"2026-06-20T18:26:49.404Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/itzcrazykns--vane","artifact":"https://unfragile.ai/itzcrazykns--vane","verify":"https://unfragile.ai/api/v1/verify?slug=itzcrazykns--vane","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"}}