{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-13w-local-rag","slug":"13w-local-rag","name":"@13w/local-rag","type":"mcp","url":"https://github.com/13W/local-rag#readme","page_url":"https://unfragile.ai/13w-local-rag","categories":["rag-knowledge"],"tags":["mcp","claude","claude-code","rag","memory","vector-search","qdrant","ollama","code-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-13w-local-rag__cap_0","uri":"capability://memory.knowledge.distributed.semantic.memory.with.vector.persistence","name":"distributed semantic memory with vector persistence","description":"Implements a distributed semantic memory layer using Qdrant vector database as the backend storage, enabling Claude Code agents to persist and retrieve embeddings across sessions. The system stores embeddings generated from code snippets, documentation, and conversation context in a vector index, allowing agents to maintain long-term semantic understanding without re-embedding identical content. Uses MCP protocol to expose memory operations as standardized tools that Claude can invoke during code generation and reasoning tasks.","intents":["I want Claude to remember code patterns and architectural decisions across multiple coding sessions","I need to build an agent that learns from previous code reviews and applies those lessons to new code","I want to maintain a searchable knowledge base of my codebase that Claude can query during development"],"best_for":["teams building long-running Claude Code agents that need persistent context","developers working on large codebases where code patterns should be remembered across sessions","organizations implementing AI-assisted code generation with institutional memory requirements"],"limitations":["Requires external Qdrant instance — no built-in local vector storage fallback","Vector embedding quality depends on upstream embedding model (Ollama or external API)","No automatic cleanup or TTL policies for stale embeddings — requires manual maintenance","Distributed setup adds network latency for each memory operation (typically 50-200ms per query)"],"requires":["Qdrant vector database instance (local or remote)","Claude API key for MCP integration","Node.js 16+ for MCP server runtime","Network connectivity between MCP server and Qdrant instance"],"input_types":["code snippets (JavaScript, Python, TypeScript, etc.)","documentation text","conversation context","structured metadata (file paths, function signatures)"],"output_types":["vector embeddings (float arrays)","similarity scores","ranked retrieval results with metadata","memory operation confirmations"],"categories":["memory-knowledge","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_1","uri":"capability://search.retrieval.code.aware.semantic.search.with.ast.informed.embeddings","name":"code-aware semantic search with ast-informed embeddings","description":"Provides semantic search over codebases by generating embeddings that incorporate code structure awareness, not just raw text similarity. The system can index code files, extract meaningful code units (functions, classes, modules), and generate embeddings that capture both semantic meaning and syntactic context. Search queries return ranked code snippets with relevance scores, enabling Claude agents to find relevant code patterns and implementations without keyword matching.","intents":["I want Claude to find similar code patterns in my codebase when implementing new features","I need semantic search that understands code intent, not just keyword matches","I want to retrieve relevant code examples to use as context for code generation tasks"],"best_for":["developers working with large, multi-language codebases (10k+ lines)","teams implementing code generation agents that need contextual code examples","organizations building internal code search tools powered by semantic understanding"],"limitations":["Embedding quality varies by language — best support for JavaScript/TypeScript, degraded for esoteric languages","Requires pre-indexing of codebase — no real-time indexing of uncommitted changes","Search latency scales with vector database size (typically 100-500ms for large codebases)","No built-in handling of code comments or documentation — requires separate indexing pipeline"],"requires":["Qdrant instance with sufficient storage for code embeddings","Ollama instance or external embedding API (OpenAI, Hugging Face)","Codebase files accessible to MCP server (local filesystem or mounted volume)","Node.js 16+ with file system access permissions"],"input_types":["source code files (JavaScript, TypeScript, Python, Go, Rust, etc.)","natural language search queries","code snippets for similarity matching","file paths and directory structures"],"output_types":["ranked code snippets with line numbers","similarity scores (0-1 range)","file paths and function signatures","contextual code blocks with surrounding context"],"categories":["search-retrieval","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_2","uri":"capability://tool.use.integration.mcp.native.tool.exposure.for.claude.code.agents","name":"mcp-native tool exposure for claude code agents","description":"Wraps all RAG and memory operations as MCP (Model Context Protocol) tools that Claude Code agents can invoke directly, using MCP's standardized tool schema and request/response format. The system registers tools for memory operations (store, retrieve, search, delete) and exposes them through the MCP server interface, allowing Claude to autonomously decide when to access memory without requiring custom prompt engineering or wrapper code.","intents":["I want Claude to automatically use memory and search tools without explicit prompting","I need Claude Code agents to treat RAG operations as native capabilities, not external APIs","I want to build agents that can reason about when to access memory vs. generate from scratch"],"best_for":["developers building Claude Code agents with persistent context requirements","teams implementing multi-turn coding sessions where agents need to reference previous work","organizations standardizing on MCP for AI agent tooling"],"limitations":["Requires Claude 3.5+ with MCP support — not compatible with older Claude versions","MCP server must be running and accessible to Claude — adds deployment complexity","Tool invocation adds latency (typically 100-300ms per tool call including network round-trip)","No built-in rate limiting or quota management for tool usage"],"requires":["Claude API with MCP support enabled","MCP server runtime (Node.js 16+)","Network connectivity between Claude API and MCP server","Proper MCP server configuration and tool schema definition"],"input_types":["MCP tool invocation requests (JSON-RPC format)","tool parameters matching defined schemas","Claude-generated tool calls with arguments"],"output_types":["MCP tool responses (JSON-RPC format)","structured results matching tool schemas","error responses with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_3","uri":"capability://data.processing.analysis.ollama.integrated.local.embedding.generation","name":"ollama-integrated local embedding generation","description":"Integrates with Ollama to generate embeddings locally without external API calls, using open-source embedding models (e.g., nomic-embed-text, all-minilm). The system can invoke Ollama's embedding endpoint to convert code snippets and search queries into vector representations, enabling fully local RAG pipelines without dependency on commercial embedding APIs. Supports fallback to external embedding APIs if Ollama is unavailable.","intents":["I want to run RAG entirely locally without sending code to external APIs","I need to generate embeddings for sensitive code without cloud dependencies","I want to use open-source embedding models in my RAG pipeline"],"best_for":["organizations with data privacy requirements or air-gapped environments","developers building local-first AI tools without cloud dependencies","teams wanting to avoid embedding API costs at scale"],"limitations":["Ollama embedding quality is lower than commercial models (OpenAI, Cohere) — typically 5-15% lower retrieval accuracy","Embedding generation is slower on CPU-only systems (typically 500ms-2s per embedding vs 50-100ms for API)","Requires Ollama instance running locally or on accessible network — adds deployment complexity","Limited model selection compared to commercial APIs — fewer specialized embedding models available"],"requires":["Ollama instance running locally or on accessible network","Ollama embedding model installed (e.g., nomic-embed-text, all-minilm)","Network connectivity to Ollama endpoint (default localhost:11434)","Sufficient disk space for embedding model (typically 100MB-1GB)"],"input_types":["code snippets (any language)","natural language text","documentation","search queries"],"output_types":["vector embeddings (float arrays, typically 384-1024 dimensions)","embedding metadata (model name, dimensions, generation timestamp)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_4","uri":"capability://search.retrieval.multi.language.codebase.indexing.and.retrieval","name":"multi-language codebase indexing and retrieval","description":"Supports indexing and semantic search across multiple programming languages (JavaScript, TypeScript, Python, Go, Rust, etc.) by using language-agnostic embedding generation and optional language-specific parsing for code structure awareness. The system can index mixed-language codebases, maintain separate vector indices per language if needed, and retrieve relevant code regardless of language boundaries. Enables cross-language code pattern discovery and reuse.","intents":["I want to search for similar patterns across JavaScript and Python code in my monorepo","I need Claude to find relevant implementations in any language when generating new code","I want to build a unified code search that works across my polyglot codebase"],"best_for":["organizations with polyglot codebases (microservices, monorepos with multiple languages)","teams building code generation agents that need to work across language boundaries","developers implementing internal code search tools for mixed-language projects"],"limitations":["Embedding quality varies by language — some languages have better semantic representations than others","Language-specific parsing requires separate grammar definitions — not all languages equally well-supported","Cross-language search may return false positives due to similar patterns in different languages","Indexing speed depends on language complexity — some languages require more sophisticated parsing"],"requires":["Qdrant instance with sufficient storage for multi-language embeddings","Ollama or external embedding API supporting multi-language models","Source code files in supported languages accessible to MCP server","Optional: language-specific parsers (tree-sitter, etc.) for structure-aware indexing"],"input_types":["source code files in multiple languages","natural language search queries","language-specific code snippets","cross-language pattern descriptions"],"output_types":["ranked code snippets from any language","language-tagged results with file paths","similarity scores with language information","cross-language pattern matches"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_5","uri":"capability://memory.knowledge.context.aware.memory.management.with.metadata.filtering","name":"context-aware memory management with metadata filtering","description":"Stores embeddings with rich metadata (file paths, function signatures, timestamps, code language, author, etc.) and enables filtering/retrieval based on metadata predicates, not just semantic similarity. The system can retrieve embeddings matching specific criteria (e.g., 'all Python functions modified in last week', 'all code in src/utils directory') and combine metadata filtering with semantic search for precise context retrieval. Metadata is stored alongside vectors in Qdrant using payload filtering.","intents":["I want to retrieve code from specific directories or files when searching","I need to find recent code changes relevant to my current task","I want to filter search results by language, author, or other metadata"],"best_for":["large teams where context filtering by ownership or location is important","organizations tracking code provenance and modification history","developers building agents that need to respect code organization and boundaries"],"limitations":["Metadata filtering adds query complexity — requires careful schema design to avoid performance degradation","Metadata must be maintained and kept in sync with actual codebase — requires indexing pipeline discipline","Qdrant payload filtering has performance limits at scale (large number of metadata fields or high cardinality)","No built-in metadata extraction — requires custom logic to populate metadata during indexing"],"requires":["Qdrant instance with payload filtering support","Metadata extraction logic (custom code to populate file paths, timestamps, etc.)","Consistent metadata schema across all indexed embeddings","Indexing pipeline that captures and stores metadata alongside embeddings"],"input_types":["code snippets with associated metadata","metadata filter predicates (e.g., file path patterns, language, timestamp ranges)","hybrid queries combining semantic similarity and metadata filters"],"output_types":["filtered embeddings matching metadata criteria","ranked results combining semantic and metadata relevance","metadata-tagged code snippets with context"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_6","uri":"capability://memory.knowledge.session.scoped.memory.isolation.for.multi.agent.scenarios","name":"session-scoped memory isolation for multi-agent scenarios","description":"Provides memory isolation mechanisms that allow different Claude Code agents or sessions to maintain separate memory spaces, preventing cross-contamination of context. The system can scope memory operations to specific sessions, users, or projects using namespace/partition strategies in Qdrant, enabling multiple agents to operate independently while sharing the same vector database infrastructure. Supports both isolated and shared memory modes depending on use case.","intents":["I want multiple Claude agents working on different projects to have isolated memory","I need to prevent one agent's context from affecting another agent's decisions","I want to run parallel coding sessions with independent memory spaces"],"best_for":["organizations running multiple Claude Code agents simultaneously","teams implementing multi-tenant AI systems where memory isolation is required","developers building agent orchestration systems with independent agent contexts"],"limitations":["Memory isolation adds query complexity — requires namespace/partition logic in every operation","No built-in cross-session memory sharing — requires explicit APIs if agents need to share context","Isolation enforcement depends on correct namespace usage — no automatic enforcement at database level","Scaling to many sessions may require Qdrant partitioning strategy — adds operational complexity"],"requires":["Qdrant instance with collection partitioning or namespace support","Session/agent identifier management in MCP server","Consistent namespace usage across all memory operations","Session lifecycle management (creation, cleanup, expiration)"],"input_types":["session identifiers or agent IDs","memory operations scoped to specific sessions","namespace/partition specifications"],"output_types":["session-scoped memory results","isolation confirmation","cross-session boundary enforcement"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_7","uri":"capability://data.processing.analysis.incremental.codebase.indexing.with.change.detection","name":"incremental codebase indexing with change detection","description":"Supports incremental indexing of codebase changes rather than full re-indexing, using file modification timestamps or git diff to detect changed files and update only affected embeddings. The system can track which files have been indexed, detect changes since last indexing, and update only the changed code units in the vector database. Enables efficient maintenance of large codebase indices without full re-embedding on every update.","intents":["I want to keep my code index up-to-date without re-indexing the entire codebase","I need to index only files that have changed since the last indexing run","I want to maintain a live code search index that reflects recent changes"],"best_for":["teams with large codebases (100k+ lines) where full re-indexing is expensive","organizations running continuous code indexing pipelines","developers building live code search tools that need to stay current"],"limitations":["Change detection requires reliable file timestamps or git integration — may fail with clock skew or git operations","Incremental indexing adds complexity to the indexing pipeline — requires careful state management","Partial updates to embeddings may miss related code changes (e.g., refactoring that affects multiple files)","No built-in handling of deleted code — requires separate cleanup logic to remove stale embeddings"],"requires":["File system access with reliable modification timestamps","Optional: git integration for change detection (git diff, git log)","State tracking mechanism to remember last indexing timestamp","Qdrant instance with update/delete capabilities"],"input_types":["file paths and modification timestamps","git diff output or change manifests","incremental indexing requests"],"output_types":["updated embeddings for changed files","indexing status and progress","change detection results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-13w-local-rag__cap_8","uri":"capability://search.retrieval.hybrid.search.combining.semantic.and.keyword.matching","name":"hybrid search combining semantic and keyword matching","description":"Supports hybrid search that combines semantic vector similarity with keyword/BM25 matching, enabling retrieval that balances semantic understanding with exact term matching. The system can execute both semantic and keyword searches in parallel, rank results using combined scores, and return results that capture both semantic relevance and keyword precision. Useful for code search where exact function names or identifiers matter alongside semantic similarity.","intents":["I want to find code that matches both semantic intent and specific keywords","I need search results that include exact matches for function names alongside semantic matches","I want to balance semantic understanding with keyword precision in code search"],"best_for":["code search scenarios where exact identifiers matter (function names, class names, etc.)","teams needing both semantic and keyword precision in retrieval","developers building search interfaces that need to handle both natural language and code-specific queries"],"limitations":["Hybrid search requires dual indexing (vector + keyword) — adds storage overhead and indexing complexity","Ranking combination requires careful tuning of semantic/keyword weights — no universal optimal weights","Keyword indexing may not work well for code without proper tokenization — requires language-specific handling","Qdrant has limited built-in keyword search — may require external BM25 index (e.g., Elasticsearch) for production use"],"requires":["Qdrant instance for semantic search","Optional: external keyword search index (Elasticsearch, Meilisearch, or Qdrant's sparse vector support)","Ranking algorithm to combine semantic and keyword scores","Dual indexing pipeline for vectors and keywords"],"input_types":["natural language queries","code-specific queries with keywords","hybrid search requests with semantic and keyword components"],"output_types":["ranked results combining semantic and keyword relevance","combined relevance scores","results tagged with match type (semantic, keyword, or both)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Qdrant vector database instance (local or remote)","Claude API key for MCP integration","Node.js 16+ for MCP server runtime","Network connectivity between MCP server and Qdrant instance","Qdrant instance with sufficient storage for code embeddings","Ollama instance or external embedding API (OpenAI, Hugging Face)","Codebase files accessible to MCP server (local filesystem or mounted volume)","Node.js 16+ with file system access permissions","Claude API with MCP support enabled","MCP server runtime (Node.js 16+)"],"failure_modes":["Requires external Qdrant instance — no built-in local vector storage fallback","Vector embedding quality depends on upstream embedding model (Ollama or external API)","No automatic cleanup or TTL policies for stale embeddings — requires manual maintenance","Distributed setup adds network latency for each memory operation (typically 50-200ms per query)","Embedding quality varies by language — best support for JavaScript/TypeScript, degraded for esoteric languages","Requires pre-indexing of codebase — no real-time indexing of uncommitted changes","Search latency scales with vector database size (typically 100-500ms for large codebases)","No built-in handling of code comments or documentation — requires separate indexing pipeline","Requires Claude 3.5+ with MCP support — not compatible with older Claude versions","MCP server must be running and accessible to Claude — adds deployment complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.060205999132796235,"quality":0.28,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":100,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=13w-local-rag","compare_url":"https://unfragile.ai/compare?artifact=13w-local-rag"}},"signature":"lTxUki7mqSEMjOQ/QNNE2lXmHjkCZsloag5oQUM9jtIFXPrncY1aAsXI9rktDJZ5GxjNwY25AzRo0CGXtb8qBw==","signedAt":"2026-06-23T04:41:25.336Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/13w-local-rag","artifact":"https://unfragile.ai/13w-local-rag","verify":"https://unfragile.ai/api/v1/verify?slug=13w-local-rag","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"}}