{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-yichuan-w--leann","slug":"yichuan-w--leann","name":"LEANN","type":"model","url":"https://arxiv.org/abs/2506.08276","page_url":"https://unfragile.ai/yichuan-w--leann","categories":["chatbot","search"],"tags":["ai","faiss","gpt-oss","langchain","llama-index","llm","localstorage","offline-first","ollama","privacy","python","rag","retrieval-augmented-generation","vector-database","vector-search","vectors"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-yichuan-w--leann__cap_0","uri":"capability://memory.knowledge.graph.based.selective.recomputation.for.97.storage.reduction","name":"graph-based selective recomputation for 97% storage reduction","description":"LEANN achieves extreme storage efficiency by building a pruned graph during index construction where only high-degree hub nodes retain full embeddings, while low-degree nodes have embeddings discarded. During search, pruned embeddings are recomputed on-demand during graph traversal using the embedding model, trading compute for storage. This approach uses high-degree preserving pruning to maintain search accuracy while eliminating the need to store millions of embedding vectors in full precision.","intents":["I need to index millions of documents on consumer hardware without massive storage overhead","I want semantic search capability without paying for cloud vector database storage costs","I need to run RAG applications on a laptop with limited disk space while maintaining search quality"],"best_for":["solo developers building privacy-first RAG applications on personal devices","teams deploying semantic search to resource-constrained environments","organizations with strict data residency requirements avoiding cloud storage"],"limitations":["Search latency increases due to on-demand embedding recomputation — typical 50-200ms overhead per search depending on pruning ratio and hardware","Accuracy depends on pruning strategy; aggressive pruning (>97% reduction) may reduce recall by 1-3% on some datasets","Recomputation requires embedding model to be loaded in memory during search, increasing RAM footprint","Not suitable for real-time search applications requiring sub-10ms latency"],"requires":["Python 3.9+","Embedding model (local Ollama instance or API key for OpenAI/Anthropic)","Minimum 4GB RAM for typical workloads, 8GB+ for million-scale document indexing","CPU with AVX2 support for optimal performance (HNSW backend)"],"input_types":["document collections (PDF, TXT, Markdown, code files)","structured metadata for filtering","query vectors or text queries"],"output_types":["ranked list of semantically similar documents with relevance scores","pruned graph index stored on disk","metadata-filtered search results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_1","uri":"capability://data.processing.analysis.pluggable.vector.search.backend.abstraction.with.hnsw.diskann.and.ivf.implementations","name":"pluggable vector search backend abstraction with hnsw, diskann, and ivf implementations","description":"LEANN provides a backend plugin system that abstracts vector search algorithms, allowing users to swap between HNSW (hierarchical navigable small world graphs for in-memory search), DiskANN (disk-optimized approximate nearest neighbor for large-scale indexing), and IVF (inverted file index for clustering-based search). Each backend implements a common interface for index building, searching, and metadata filtering, enabling performance tuning without changing application code.","intents":["I need to choose between different vector search algorithms based on my hardware and latency requirements","I want to benchmark HNSW vs DiskANN vs IVF to find the optimal backend for my dataset size","I need to extend LEANN with a custom vector search algorithm without modifying core code"],"best_for":["developers optimizing for specific hardware constraints (CPU-only, GPU-accelerated, disk-bound)","researchers experimenting with novel approximate nearest neighbor algorithms","teams migrating between vector database backends"],"limitations":["HNSW backend requires all vectors in memory — not suitable for >10M vectors on typical consumer hardware","DiskANN backend has slower index construction time (2-5x slower than HNSW) but better memory efficiency","IVF backend requires careful tuning of cluster count and probe parameters; suboptimal settings can degrade recall by 5-15%","Backend switching requires full index rebuild; no online migration between backends"],"requires":["Python 3.9+","HNSW: hnswlib library","DiskANN: diskann Python bindings","IVF: faiss library","Custom backends: implementation of Backend abstract interface"],"input_types":["embedding vectors (float32 arrays)","backend configuration parameters (M, ef, num_clusters, probe)","metadata for filtering"],"output_types":["backend-specific index files on disk","search results with neighbor IDs and distances","performance metrics (latency, memory usage, recall)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_10","uri":"capability://tool.use.integration.python.api.and.cli.for.index.management.and.querying","name":"python api and cli for index management and querying","description":"LEANN exposes both a Python API (for programmatic use in applications) and a command-line interface (for index building, searching, and management tasks). The API provides high-level abstractions for index creation, document addition, search, and RAG operations, while the CLI enables batch operations and scripting without writing Python code.","intents":["I want to build a Python application that uses LEANN for semantic search","I need to build an index from the command line without writing code","I want to script index updates, backups, and maintenance tasks"],"best_for":["Python developers building RAG applications","DevOps engineers automating index management","data engineers building document processing pipelines"],"limitations":["Python API requires Python 3.9+ — no support for other languages without language bindings","CLI is less flexible than API — complex workflows may require Python scripting","API documentation is sparse — requires reading source code for advanced features","No built-in API versioning — breaking changes may occur between releases"],"requires":["Python 3.9+","LEANN package installed via pip"],"input_types":["Python code using LEANN API","CLI commands with arguments and flags","configuration files (YAML/JSON)"],"output_types":["index objects and search results (Python API)","CLI output (JSON, text, CSV)","index files on disk"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_11","uri":"capability://memory.knowledge.personal.data.rag.with.privacy.preserving.local.processing","name":"personal data rag with privacy-preserving local processing","description":"LEANN enables building RAG applications over personal data (emails, notes, files, browsing history) with all processing happening locally on the user's device. No data is sent to cloud services unless explicitly configured, and the system provides privacy guarantees through local embedding computation and storage, making it suitable for sensitive personal information.","intents":["I want to search my personal notes and documents without uploading them to cloud services","I need a private knowledge base for sensitive information (health records, financial data, personal notes)","I want to build a personal AI assistant that understands my context without sharing data with third parties"],"best_for":["individuals building personal knowledge management systems","privacy-conscious users avoiding cloud storage of personal data","organizations with strict data residency requirements"],"limitations":["Local processing is slower than cloud APIs — 10-50x slower for embedding computation","Requires managing local model storage and updates — manual model management overhead","No built-in backup or sync across devices — data loss risk if local storage fails","Limited to single-device usage — no multi-device synchronization"],"requires":["Python 3.9+","Local embedding model (Ollama or similar)","Sufficient local storage for documents and indices (varies by collection size)","Sufficient RAM for embedding model (4-8GB typical)"],"input_types":["personal documents (emails, notes, files)","browsing history or other personal data sources","user queries"],"output_types":["search results from personal data","RAG responses based on personal context","index statistics and storage usage"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_12","uri":"capability://tool.use.integration.live.data.integration.via.mcp.for.real.time.context","name":"live data integration via mcp for real-time context","description":"LEANN can integrate with live data sources (APIs, databases, web services) through MCP tools, allowing RAG queries to incorporate real-time information alongside indexed documents. This enables hybrid RAG that combines static indexed knowledge with dynamic live data, useful for applications requiring current information.","intents":["I want to search my indexed documents but also retrieve real-time data from APIs","I need RAG that combines historical documents with current information (stock prices, weather, news)","I want to build an agent that can fetch live data when indexed documents are insufficient"],"best_for":["developers building hybrid RAG systems combining static and dynamic data","teams needing real-time context in RAG applications","organizations integrating LEANN with external data sources"],"limitations":["Live data integration adds latency — API calls may take 100ms-5s depending on source","No caching of live data — each query makes fresh API calls, increasing cost and latency","Requires MCP tool definitions for each data source — manual integration work","Live data may conflict with indexed data — no built-in conflict resolution"],"requires":["Python 3.9+","MCP server running with live data tools configured","API credentials for live data sources","Network connectivity to live data sources"],"input_types":["user queries","MCP tool definitions for live data sources","API credentials and endpoints"],"output_types":["combined results from indexed documents and live data","metadata indicating source (indexed vs. live)","timestamps for live data freshness"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_13","uri":"capability://automation.workflow.index.configuration.and.tuning.for.performance.optimization","name":"index configuration and tuning for performance optimization","description":"LEANN provides configuration options for tuning index performance across multiple dimensions: backend selection (HNSW, DiskANN, IVF), pruning ratio (controlling storage vs. accuracy tradeoff), distance metrics, and search parameters (ef, num_probes). Users can benchmark different configurations and select optimal settings for their hardware and latency requirements.","intents":["I want to tune my index for faster search latency on my specific hardware","I need to find the optimal balance between storage savings and search accuracy","I want to benchmark different backend configurations to choose the best one"],"best_for":["developers optimizing LEANN for production deployments","teams with specific latency or storage constraints","researchers benchmarking vector search algorithms"],"limitations":["Configuration tuning is empirical — no automated optimization; requires manual experimentation","Different configurations require separate indices — no online configuration changes","Benchmarking is time-consuming — full index rebuild needed for each configuration","Optimal settings are hardware and dataset-specific — no universal recommendations"],"requires":["Python 3.9+","LEANN configuration files (YAML/JSON)","Time for benchmarking (hours to days for large indices)"],"input_types":["configuration parameters (backend, pruning ratio, distance metric, search params)","benchmark datasets and queries","hardware specifications"],"output_types":["performance metrics (latency, throughput, recall, storage usage)","benchmark reports comparing configurations","recommended settings for specific constraints"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_2","uri":"capability://data.processing.analysis.local.first.embedding.computation.with.optional.cloud.provider.fallback","name":"local-first embedding computation with optional cloud provider fallback","description":"LEANN computes embeddings locally using Ollama (for open-source models like Nomic Embed, Llama 2) or via local embedding servers, with optional fallback to OpenAI/Anthropic APIs. The embedding computation layer abstracts provider selection, batching, and caching, allowing users to keep all data on-device while optionally using cloud APIs for specific models. Embeddings are cached after computation to avoid redundant recomputation.","intents":["I want to embed documents locally without sending data to OpenAI or other cloud providers","I need to switch between local Ollama embeddings and cloud APIs based on model availability","I want to cache computed embeddings to avoid recomputing them when updating indices"],"best_for":["organizations with strict data privacy requirements (healthcare, finance, legal)","developers building offline-first applications","teams avoiding cloud API costs for embedding computation"],"limitations":["Local embedding models (Nomic Embed, Llama 2) are typically 50-70% slower than cloud APIs like OpenAI text-embedding-3","Ollama requires 4-8GB RAM and 5-15GB disk space for model storage depending on model size","Embedding quality varies significantly between models — open-source models may have 5-15% lower recall than commercial models","Batch embedding computation is limited by available GPU/CPU memory; typical batch size 32-256 on consumer hardware"],"requires":["Python 3.9+","Ollama 0.1+ (for local embeddings) OR OpenAI/Anthropic API key","4GB+ RAM for local embedding models","GPU optional but recommended for 10x+ speedup (CUDA 11.8+ or Metal for Apple Silicon)"],"input_types":["text documents or chunks","embedding model name (e.g., 'nomic-embed-text', 'text-embedding-3-small')","provider configuration (Ollama endpoint, API keys)"],"output_types":["embedding vectors (float32, typically 384-1536 dimensions)","embedding metadata (model used, timestamp, provider)","cached embeddings stored in index"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_3","uri":"capability://code.generation.editing.ast.aware.code.chunking.for.semantic.code.indexing","name":"ast-aware code chunking for semantic code indexing","description":"LEANN includes specialized document chunking that parses code using Abstract Syntax Trees (AST) to preserve semantic boundaries (functions, classes, methods) rather than naive line-based or token-based splitting. This enables more accurate semantic search over codebases by ensuring chunks correspond to logical code units, improving retrieval quality for code-specific RAG applications.","intents":["I want to index a codebase and retrieve functions/classes by semantic meaning, not just keyword matching","I need to split code files intelligently to preserve function and class boundaries","I want to search across a large codebase and get relevant code units, not arbitrary line ranges"],"best_for":["developers building code search and code generation tools","teams indexing large codebases for AI-assisted development","organizations using LEANN for code-to-code RAG (e.g., finding similar implementations)"],"limitations":["AST parsing is language-specific; LEANN supports Python, JavaScript/TypeScript, Java, C++ — other languages fall back to token-based chunking","AST parsing adds 10-30% overhead to document loading time compared to naive chunking","Very large functions (>1000 lines) may still exceed token limits and require secondary splitting","Nested code structures (deeply nested classes/functions) may create very small chunks, reducing context"],"requires":["Python 3.9+","tree-sitter library for AST parsing","Language-specific tree-sitter grammars (auto-downloaded on first use)"],"input_types":["source code files (.py, .js, .ts, .java, .cpp, etc.)","code strings with language specification"],"output_types":["semantically-bounded code chunks with metadata (function name, class name, line range)","chunk embeddings indexed for semantic search"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_4","uri":"capability://automation.workflow.file.synchronization.and.change.detection.for.incremental.index.updates","name":"file synchronization and change detection for incremental index updates","description":"LEANN monitors the file system for changes (new files, modified files, deletions) and incrementally updates indices without full rebuilds. The system uses file modification timestamps and content hashing to detect changes, then recomputes embeddings only for modified chunks, reducing index update time from hours to minutes for large document collections.","intents":["I want to keep my document index in sync with a folder that changes frequently","I need to update my RAG index when new documents are added without rebuilding from scratch","I want to run LEANN in daemon mode to continuously monitor and update indices"],"best_for":["developers building document management systems with live RAG","teams maintaining searchable archives of frequently-updated documents","organizations using LEANN for personal knowledge management with continuous updates"],"limitations":["File synchronization relies on modification timestamps — clock skew or network file systems may cause missed updates","Content hashing adds 5-10% overhead to change detection on large file collections","Deletions require explicit index cleanup; orphaned embeddings may persist until full rebuild","Daemon mode requires persistent process — no built-in process management or systemd integration"],"requires":["Python 3.9+","File system with reliable modification timestamps (NTFS, ext4, APFS)","Sufficient disk space for temporary index files during updates"],"input_types":["file system paths to monitor","file type filters (.pdf, .txt, .md, etc.)","change detection configuration (polling interval, hash algorithm)"],"output_types":["updated index with new/modified documents","change log with timestamps and affected documents","sync status and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_5","uri":"capability://search.retrieval.metadata.filtering.and.structured.search.with.distance.metrics","name":"metadata filtering and structured search with distance metrics","description":"LEANN supports filtering search results by metadata (document type, date range, tags, custom fields) before or after vector search, and provides configurable distance metrics (cosine similarity, L2 distance, inner product) with optional vector normalization. Metadata filters are applied efficiently during graph traversal to reduce search space, and distance metrics can be swapped per-query without index rebuilds.","intents":["I want to search documents by semantic similarity but only within a specific date range or category","I need to use different distance metrics for different query types (cosine for text, L2 for images)","I want to combine vector search with structured filters (e.g., 'find similar documents from 2024 only')"],"best_for":["developers building filtered search experiences (e.g., search within a document type)","teams using LEANN for multi-tenant applications with per-user data filtering","organizations needing flexible distance metrics for different data modalities"],"limitations":["Metadata filtering reduces search efficiency — filtering after search is slower than filtering during traversal for selective filters","Complex boolean filters (AND/OR/NOT combinations) may require multiple index scans","Distance metric changes require recomputing similarities for all candidates — no caching across metric changes","Metadata storage adds 5-20% overhead depending on field count and cardinality"],"requires":["Python 3.9+","Metadata schema definition (field names, types, indexing strategy)","Support for standard distance metrics (cosine, L2, inner product) or custom implementations"],"input_types":["query vector or text","metadata filter expressions (e.g., {'date': {'$gte': '2024-01-01'}, 'category': 'research'})","distance metric name (cosine, l2, inner_product)"],"output_types":["filtered search results with relevance scores","metadata for each result","distance values using specified metric"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_6","uri":"capability://planning.reasoning.react.agent.framework.for.multi.step.reasoning.with.tool.use","name":"react agent framework for multi-step reasoning with tool use","description":"LEANN includes a ReAct (Reasoning + Acting) agent implementation that decomposes complex queries into multi-step reasoning chains, using vector search as a tool alongside other capabilities. The agent maintains conversation context, plans actions (search, summarize, retrieve), executes them, and iterates based on results, enabling complex information retrieval tasks beyond simple semantic search.","intents":["I want to answer complex questions that require searching multiple documents and synthesizing information","I need an agent that can break down a query into sub-queries and retrieve relevant context for each","I want to build a conversational RAG system that maintains context across multiple turns"],"best_for":["developers building conversational AI systems with complex reasoning","teams creating question-answering systems over large document collections","organizations needing multi-step information retrieval with reasoning"],"limitations":["ReAct agents require multiple LLM calls per query — 3-5x higher latency and cost compared to single-step retrieval","Agent reasoning quality depends heavily on LLM capability; weaker models may produce suboptimal action sequences","No built-in memory persistence — conversation history must be managed externally","Token limits on LLM context may constrain reasoning depth for very long conversations"],"requires":["Python 3.9+","LLM provider (OpenAI, Anthropic, Ollama) with function calling support","LEANN vector search backend configured and indexed"],"input_types":["user query (natural language)","conversation history (optional)","tool definitions (search, summarize, retrieve)"],"output_types":["final answer with reasoning chain","intermediate search results and reasoning steps","tool usage trace for debugging"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_7","uri":"capability://tool.use.integration.mcp.server.integration.for.claude.code.and.ide.based.rag","name":"mcp server integration for claude code and ide-based rag","description":"LEANN exposes a Model Context Protocol (MCP) server that allows Claude and other MCP-compatible clients to query LEANN indices directly from IDEs or Claude Code. This enables developers to use LEANN as a knowledge source within their development environment, retrieving relevant code, documentation, or context without leaving their editor.","intents":["I want Claude to search my codebase and documentation while I'm coding in VS Code","I need to provide Claude with context from my personal knowledge base during code generation","I want to use LEANN indices as a tool within Claude Code for context-aware assistance"],"best_for":["developers using Claude Code or Claude in VS Code","teams building IDE-integrated RAG experiences","organizations using Claude for code generation with custom knowledge bases"],"limitations":["MCP server requires running LEANN daemon process — adds system resource overhead","Claude's context window limits how much search results can be included per request","MCP protocol adds network latency compared to direct library calls","Requires Claude 3.5+ or compatible MCP client; older Claude versions not supported"],"requires":["Python 3.9+","LEANN daemon running with MCP server enabled","Claude 3.5+ or MCP-compatible client","VS Code 1.80+ with Claude extension (for IDE integration)"],"input_types":["natural language queries from Claude","MCP tool call requests with search parameters"],"output_types":["search results formatted for Claude context","document excerpts with metadata","structured results for tool use"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_8","uri":"capability://data.processing.analysis.task.specific.embedding.models.with.prompt.templates","name":"task-specific embedding models with prompt templates","description":"LEANN supports task-specific embedding models (e.g., models fine-tuned for code, legal documents, scientific papers) and allows users to define custom prompt templates that modify how text is embedded. This enables optimizing embedding quality for specific domains by using domain-adapted models or prepending task-specific instructions to documents before embedding.","intents":["I want to use a code-specific embedding model for better code search accuracy","I need to embed documents with domain-specific context (e.g., 'This is a legal contract') to improve retrieval","I want to experiment with different embedding models and prompts without reindexing"],"best_for":["developers optimizing RAG for specific domains (code, legal, medical, scientific)","researchers experimenting with embedding model selection","teams fine-tuning embedding models for proprietary data"],"limitations":["Task-specific models may not be available for all domains — requires custom fine-tuning or external model sources","Prompt templates add complexity to index management — different templates require separate indices","Changing embedding models requires full index rebuild; no online model migration","Prompt engineering is empirical — no guarantee that custom prompts improve retrieval quality"],"requires":["Python 3.9+","Task-specific embedding model (local or via API)","Prompt template definition (string with placeholders)"],"input_types":["embedding model name or path","prompt template string (e.g., 'Code snippet: {text}')","documents to embed"],"output_types":["embeddings computed with task-specific model and prompt","index metadata tracking model and prompt used","comparison metrics for different models/prompts"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yichuan-w--leann__cap_9","uri":"capability://data.processing.analysis.document.loading.and.chunking.pipeline.with.format.support","name":"document loading and chunking pipeline with format support","description":"LEANN includes a document loading pipeline that supports multiple formats (PDF, TXT, Markdown, JSON, code files) with format-specific parsing and chunking strategies. The pipeline handles document extraction, text cleaning, and semantic chunking (respecting paragraph/section boundaries), producing chunks optimized for embedding and retrieval.","intents":["I want to index a mix of PDFs, Markdown docs, and code files without writing custom loaders","I need to extract text from PDFs while preserving structure (sections, tables, code blocks)","I want to chunk documents intelligently based on semantic boundaries, not just token count"],"best_for":["developers building document indexing pipelines","teams managing heterogeneous document collections","organizations migrating from other RAG systems"],"limitations":["PDF extraction quality varies by PDF type (scanned vs. digital) — scanned PDFs require OCR (not built-in)","Complex document structures (tables, multi-column layouts) may not parse correctly","Chunking strategies are heuristic-based — optimal chunk size varies by domain and model","Large files (>100MB) may cause memory issues during loading; requires streaming or batch processing"],"requires":["Python 3.9+","Format-specific libraries: pypdf (PDF), python-docx (DOCX), markdown (Markdown), etc.","Sufficient memory for document loading (varies by file size)"],"input_types":["file paths or file objects","supported formats: .pdf, .txt, .md, .json, .py, .js, .ts, .java, .cpp, etc.","chunking configuration (chunk size, overlap, strategy)"],"output_types":["document chunks with metadata (source, page number, section, line range)","cleaned text ready for embedding","chunk statistics (count, average size, coverage)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Embedding model (local Ollama instance or API key for OpenAI/Anthropic)","Minimum 4GB RAM for typical workloads, 8GB+ for million-scale document indexing","CPU with AVX2 support for optimal performance (HNSW backend)","HNSW: hnswlib library","DiskANN: diskann Python bindings","IVF: faiss library","Custom backends: implementation of Backend abstract interface","LEANN package installed via pip","Local embedding model (Ollama or similar)"],"failure_modes":["Search latency increases due to on-demand embedding recomputation — typical 50-200ms overhead per search depending on pruning ratio and hardware","Accuracy depends on pruning strategy; aggressive pruning (>97% reduction) may reduce recall by 1-3% on some datasets","Recomputation requires embedding model to be loaded in memory during search, increasing RAM footprint","Not suitable for real-time search applications requiring sub-10ms latency","HNSW backend requires all vectors in memory — not suitable for >10M vectors on typical consumer hardware","DiskANN backend has slower index construction time (2-5x slower than HNSW) but better memory efficiency","IVF backend requires careful tuning of cluster count and probe parameters; suboptimal settings can degrade recall by 5-15%","Backend switching requires full index rebuild; no online migration between backends","Python API requires Python 3.9+ — no support for other languages without language bindings","CLI is less flexible than API — complex workflows may require Python scripting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3533402210663395,"quality":0.4,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-02T01:05:33Z"},"community":{"stars":10951,"forks":961,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yichuan-w--leann","compare_url":"https://unfragile.ai/compare?artifact=yichuan-w--leann"}},"signature":"oKO52dAX94cpx3jkYSAK/6GSutVhPQlix0ywUPl1fv30VNre+EX9RTAXas5rYwK8EwrJ6Vm+Gxg4oF4zkE9rCA==","signedAt":"2026-06-22T14:01:52.733Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yichuan-w--leann","artifact":"https://unfragile.ai/yichuan-w--leann","verify":"https://unfragile.ai/api/v1/verify?slug=yichuan-w--leann","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"}}