{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-chroma","slug":"chroma","name":"Chroma","type":"mcp","url":"https://github.com/chroma-core/chroma-mcp","page_url":"https://unfragile.ai/chroma","categories":["rag-knowledge","documentation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-chroma__cap_0","uri":"capability://memory.knowledge.vector.based.semantic.search.with.embedding.generation","name":"vector-based semantic search with embedding generation","description":"Accepts documents or queries, automatically generates embeddings using configurable embedding models (default: all-MiniLM-L6-v2), stores vectors in an in-memory or persistent index, and retrieves semantically similar results ranked by cosine distance. Uses approximate nearest neighbor search (via hnswlib by default) to scale beyond brute-force matching, enabling sub-millisecond retrieval on million-scale collections.","intents":["Find documents semantically similar to a user query without exact keyword matching","Build RAG systems that retrieve relevant context before LLM generation","Implement semantic deduplication across large document collections","Enable similarity-based recommendations without manual feature engineering"],"best_for":["LLM application builders implementing retrieval-augmented generation (RAG)","Teams building semantic search into existing applications","Developers prototyping multi-modal search systems"],"limitations":["Embedding quality depends on model choice; domain-specific embeddings may require fine-tuning","In-memory mode limited by available RAM; persistent mode requires external storage backend","No built-in query expansion or relevance feedback — requires external reranking for production quality","Approximate search trades recall for speed; exact nearest neighbor search available but slower"],"requires":["Python 3.8+ or Node.js 14+","Embedding model (local or API-based; defaults to sentence-transformers)","Storage backend: SQLite (default), PostgreSQL, or cloud provider (Pinecone, Weaviate)"],"input_types":["text documents","document metadata (JSON)","embedding vectors (pre-computed, optional)"],"output_types":["ranked document results with similarity scores","embedding vectors","metadata associated with retrieved documents"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_1","uri":"capability://search.retrieval.full.text.search.with.bm25.ranking","name":"full-text search with bm25 ranking","description":"Indexes document text using BM25 (Okapi algorithm) for keyword-based retrieval, enabling fast full-text search without semantic embeddings. Supports boolean operators, phrase queries, and field-specific filtering. Complements vector search by providing exact-match and keyword-proximity capabilities, often combined with semantic search for hybrid retrieval pipelines.","intents":["Search for documents containing specific keywords or phrases","Implement hybrid search combining keyword and semantic relevance","Filter documents by metadata before semantic ranking","Support users who prefer explicit keyword queries over semantic matching"],"best_for":["Applications requiring both keyword and semantic search","Teams building search UIs with explicit query syntax","Developers implementing hybrid retrieval for improved recall"],"limitations":["BM25 ranking does not capture semantic relationships; 'car' and 'automobile' treated as distinct","No built-in stemming or lemmatization; requires preprocessing for morphological variants","Performance degrades on very large collections without proper indexing strategy","Limited support for complex boolean queries compared to Elasticsearch"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection with documents indexed"],"input_types":["text queries with optional boolean operators","document text","metadata filters (JSON)"],"output_types":["ranked document results with BM25 scores","matched document IDs and metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_10","uri":"capability://automation.workflow.collection.statistics.and.monitoring","name":"collection statistics and monitoring","description":"Provides collection-level statistics including document count, embedding count, metadata field cardinality, and index size. Statistics are computed on-demand and can be used for monitoring, capacity planning, and debugging. Supports per-collection metrics without requiring external monitoring infrastructure.","intents":["Monitor collection growth and index size for capacity planning","Debug missing or incomplete embeddings in collections","Verify data integrity (e.g., all documents have embeddings)","Track collection usage for billing or analytics"],"best_for":["Operations teams managing production Chroma deployments","Developers debugging data quality issues","Teams implementing usage-based billing or analytics"],"limitations":["Statistics computed on-demand; no time-series history or trend analysis","No built-in alerting on threshold violations (e.g., collection size exceeds limit)","Metadata cardinality statistics limited to top-k values; no full distribution","No per-query performance metrics; requires external profiling"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection initialized"],"input_types":["collection name"],"output_types":["document count","embedding count","metadata field statistics","index size (bytes)","collection creation timestamp"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_2","uri":"capability://memory.knowledge.multi.modal.document.storage.with.metadata.indexing","name":"multi-modal document storage with metadata indexing","description":"Stores documents as collections with associated metadata (JSON objects), enabling filtering and retrieval based on custom fields. Supports document IDs, text content, embeddings, and arbitrary metadata in a single record. Metadata is indexed and queryable, allowing WHERE-clause filtering before semantic or full-text search, reducing result sets before ranking.","intents":["Store documents with rich metadata (author, date, source, category) for filtered retrieval","Implement multi-tenant search by filtering on tenant_id metadata","Build document management systems with custom indexing on domain-specific fields","Combine metadata filtering with semantic search for precision retrieval"],"best_for":["Applications with structured document metadata","Multi-tenant systems requiring isolation via metadata filters","Teams building domain-specific search with custom categorization"],"limitations":["Metadata filtering is exact-match or range-based; no full-text search on metadata fields","No support for nested metadata objects; flat JSON structure only","Metadata indexing adds storage overhead; not optimized for high-cardinality fields","No automatic schema validation; incorrect metadata types can cause silent failures"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection initialized","Metadata as flat JSON objects"],"input_types":["document text","document IDs (string)","metadata (JSON object)","embeddings (optional, pre-computed)"],"output_types":["stored documents with metadata","filtered document subsets","metadata-based aggregations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_3","uri":"capability://memory.knowledge.persistent.and.ephemeral.collection.modes","name":"persistent and ephemeral collection modes","description":"Supports both in-memory (ephemeral) collections for development and testing, and persistent collections backed by SQLite, PostgreSQL, or cloud storage for production use. Collections can be created, queried, and updated with automatic persistence without explicit save operations. Switching between modes requires only configuration changes, not code refactoring.","intents":["Prototype RAG applications quickly with in-memory collections","Deploy production systems with durable persistence and recovery","Test embedding and search logic without database setup","Migrate from development to production without rewriting collection code"],"best_for":["Developers iterating rapidly on RAG prototypes","Teams deploying to serverless or containerized environments","Applications requiring both development simplicity and production durability"],"limitations":["In-memory mode loses all data on process restart; unsuitable for production","SQLite persistence limited to single-machine deployments; no distributed replication","PostgreSQL backend requires external database setup and management","No built-in backup or disaster recovery; relies on underlying storage backend"],"requires":["Python 3.8+ or Node.js 14+","SQLite (included with Python) or PostgreSQL 12+ for persistence","Disk space proportional to document count and embedding dimensions"],"input_types":["collection configuration (name, metadata schema)","documents and embeddings"],"output_types":["persisted collection state","collection metadata and statistics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_4","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.llm.agents","name":"mcp (model context protocol) integration for llm agents","description":"Exposes Chroma collections as MCP tools, allowing LLM agents and Claude to invoke vector search, full-text search, and document retrieval directly within agentic workflows. Implements MCP resource and tool schemas for semantic search, metadata filtering, and document management, enabling agents to autonomously retrieve context without human intervention or external API calls.","intents":["Enable Claude or other LLM agents to retrieve documents autonomously during reasoning","Build agentic RAG systems where agents decide when and what to search","Integrate Chroma search into multi-step agent workflows","Allow agents to manage document collections (add, update, delete) via MCP tools"],"best_for":["Teams building Claude-powered agents with document retrieval","Developers implementing agentic RAG systems","Applications requiring autonomous context retrieval without human prompting"],"limitations":["MCP integration limited to Claude and compatible LLM platforms; not all LLMs support MCP","Agent decision-making on when to search depends on LLM reasoning quality; no guardrails on search frequency","Tool calling overhead adds latency per search invocation; not suitable for real-time applications","No built-in caching of search results; repeated queries trigger new searches"],"requires":["Claude API access or compatible MCP-supporting LLM","Chroma collection initialized and accessible","MCP server running (provided by chroma-mcp package)"],"input_types":["natural language queries from LLM agents","search parameters (query, filters, top_k)","document management commands"],"output_types":["search results formatted for LLM consumption","document metadata and content","operation status (success/failure)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_5","uri":"capability://memory.knowledge.pluggable.embedding.model.providers","name":"pluggable embedding model providers","description":"Supports multiple embedding model sources: local sentence-transformers models, OpenAI embeddings API, Hugging Face Inference API, and custom embedding functions. Embedding generation is abstracted behind a provider interface, allowing users to swap models without changing collection code. Embeddings can be pre-computed externally and loaded directly, or generated on-demand during document insertion.","intents":["Use domain-specific embedding models for improved semantic search quality","Switch between local and API-based embeddings based on cost/latency tradeoffs","Implement custom embedding logic (e.g., multi-modal embeddings combining text and metadata)","Avoid vendor lock-in by easily switching embedding providers"],"best_for":["Teams with domain-specific embedding requirements","Applications optimizing for cost (local models) or quality (API-based models)","Developers building multi-modal or specialized search systems"],"limitations":["Embedding model quality varies significantly; no automatic model selection or recommendation","Local models require GPU for reasonable performance; CPU inference adds 100-500ms per document","API-based embeddings introduce external dependencies and rate limits","No built-in embedding caching; repeated embeddings of identical text trigger new generations"],"requires":["Python 3.8+ or Node.js 14+","Embedding model (local or API key for external provider)","GPU recommended for local models; CPU acceptable for small collections"],"input_types":["document text","pre-computed embedding vectors","embedding model configuration"],"output_types":["embedding vectors (768-1536 dimensions typical)","embedding metadata (model name, timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_6","uri":"capability://data.processing.analysis.batch.document.operations.with.upsert.semantics","name":"batch document operations with upsert semantics","description":"Supports bulk insertion, updating, and deletion of documents in a single operation using upsert semantics (insert if new, update if exists based on document ID). Batch operations are optimized for throughput, reducing per-document overhead compared to individual inserts. Embeddings are generated or updated in batches, leveraging vectorization for faster processing.","intents":["Ingest large document collections (thousands to millions) efficiently","Update existing documents without manual delete-then-insert logic","Rebuild collections with new embeddings or metadata without downtime","Implement incremental indexing of new documents alongside existing collections"],"best_for":["Data pipeline teams ingesting large document corpora","Applications with frequent document updates or refreshes","Teams building search indices from external data sources"],"limitations":["Batch size limited by available memory; very large batches (>100k documents) may require chunking","No transactional guarantees; partial batch failures may leave collection in inconsistent state","Embedding generation for large batches can be slow without GPU acceleration","No built-in progress tracking or resumable uploads for failed batches"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection initialized","Documents as list of dicts with id, text, metadata fields"],"input_types":["list of documents (id, text, metadata, embeddings)","batch size configuration"],"output_types":["operation status (success/failure per document)","collection statistics (document count, embedding count)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_7","uri":"capability://memory.knowledge.collection.level.access.control.and.isolation","name":"collection-level access control and isolation","description":"Organizes documents into named collections with independent indices, metadata schemas, and embedding configurations. Collections are isolated at the API level, allowing multi-tenant applications to maintain separate document spaces without cross-contamination. Each collection maintains its own vector index, full-text index, and metadata store, enabling per-collection configuration of embedding models and search parameters.","intents":["Build multi-tenant SaaS applications with per-customer document isolation","Organize documents by domain or use case without mixing indices","Implement role-based access control at the collection level","Scale to multiple collections without performance degradation"],"best_for":["SaaS platforms serving multiple customers or organizations","Applications with distinct document domains requiring separate search indices","Teams implementing fine-grained access control"],"limitations":["Collection isolation is logical, not cryptographic; no encryption between collections","No built-in role-based access control (RBAC); access control must be implemented at application layer","Cross-collection search not supported; queries limited to single collection","Collection metadata (name, embedding model) visible to all users; no per-collection secrets"],"requires":["Python 3.8+ or Node.js 14+","Application-layer access control logic","Chroma instance with persistent storage for multi-tenant deployments"],"input_types":["collection name and configuration","documents and metadata","access control rules (application-defined)"],"output_types":["collection metadata","isolated search results per collection","collection statistics"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_8","uri":"capability://search.retrieval.similarity.threshold.and.top.k.result.filtering","name":"similarity threshold and top-k result filtering","description":"Supports configurable result filtering based on similarity score thresholds and top-k result limits. Queries can specify minimum similarity scores (e.g., cosine distance > 0.7) to exclude low-relevance results, or retrieve only the top N most similar documents. Filtering is applied after ranking, enabling precision-recall tradeoffs without re-running searches.","intents":["Filter out low-confidence search results to improve precision","Limit result sets to top N documents for performance or UX reasons","Implement confidence-based result filtering in RAG systems","Tune search quality by adjusting similarity thresholds per use case"],"best_for":["RAG systems requiring high-precision context retrieval","Applications with strict latency requirements (limiting result sets)","Teams tuning search quality through threshold experimentation"],"limitations":["Similarity thresholds are model-dependent; no universal threshold across embedding models","Top-k filtering applied post-ranking; no early termination optimization for large collections","No adaptive thresholding based on query difficulty or result distribution","Threshold tuning requires manual experimentation; no automated threshold selection"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection with indexed documents","Similarity threshold and top-k parameters"],"input_types":["query text or embedding","similarity threshold (0-1 range)","top-k parameter (integer)"],"output_types":["filtered search results with similarity scores","result count and statistics"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chroma__cap_9","uri":"capability://search.retrieval.query.result.deduplication.and.re.ranking","name":"query result deduplication and re-ranking","description":"Supports deduplication of search results based on document ID or metadata fields, preventing duplicate documents from appearing in result sets. Optional re-ranking can be applied post-retrieval using external models or custom scoring functions, enabling multi-stage ranking pipelines (e.g., BM25 first-pass, cross-encoder re-ranking second-pass).","intents":["Remove duplicate documents from search results without manual filtering","Implement multi-stage ranking pipelines for improved relevance","Apply cross-encoder re-ranking to refine semantic search results","Deduplicate results across multiple search modalities (vector + full-text)"],"best_for":["Applications with duplicate documents in collections","Teams implementing advanced ranking pipelines","RAG systems requiring high-quality result ranking"],"limitations":["Deduplication based on exact ID match; no fuzzy deduplication for near-duplicates","Re-ranking requires external model; no built-in cross-encoder support","Re-ranking adds latency (100-500ms per query depending on model)","No automatic deduplication strategy selection; requires manual configuration"],"requires":["Python 3.8+ or Node.js 14+","Chroma collection with documents","Re-ranking model (optional; external)"],"input_types":["search results from vector or full-text search","deduplication field (document ID or metadata field)","re-ranking model configuration (optional)"],"output_types":["deduplicated search results","re-ranked results with updated scores","deduplication statistics"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 14+","Embedding model (local or API-based; defaults to sentence-transformers)","Storage backend: SQLite (default), PostgreSQL, or cloud provider (Pinecone, Weaviate)","Chroma collection with documents indexed","Chroma collection initialized","Metadata as flat JSON objects","SQLite (included with Python) or PostgreSQL 12+ for persistence","Disk space proportional to document count and embedding dimensions","Claude API access or compatible MCP-supporting LLM","Chroma collection initialized and accessible"],"failure_modes":["Embedding quality depends on model choice; domain-specific embeddings may require fine-tuning","In-memory mode limited by available RAM; persistent mode requires external storage backend","No built-in query expansion or relevance feedback — requires external reranking for production quality","Approximate search trades recall for speed; exact nearest neighbor search available but slower","BM25 ranking does not capture semantic relationships; 'car' and 'automobile' treated as distinct","No built-in stemming or lemmatization; requires preprocessing for morphological variants","Performance degrades on very large collections without proper indexing strategy","Limited support for complex boolean queries compared to Elasticsearch","Statistics computed on-demand; no time-series history or trend analysis","No built-in alerting on threshold violations (e.g., collection size exceeds limit)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=chroma","compare_url":"https://unfragile.ai/compare?artifact=chroma"}},"signature":"eEWLojXKgBjKGvNnk+2wazNfU9lx+iGrI9YlipQGfyNUY9KsztjLddpc57bEtlpLinNmB+kHtgMV+i7YpvZVBw==","signedAt":"2026-06-21T01:38:02.822Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chroma","artifact":"https://unfragile.ai/chroma","verify":"https://unfragile.ai/api/v1/verify?slug=chroma","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"}}