{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-llama-flow-llamaindex","slug":"llama-flow-llamaindex","name":"@llama-flow/llamaindex","type":"framework","url":"https://github.com/run-llama/llama-flow#readme","page_url":"https://unfragile.ai/llama-flow-llamaindex","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-llama-flow-llamaindex__cap_0","uri":"capability://memory.knowledge.llamaindex.document.indexing.integration.via.llama.flow","name":"llamaindex document indexing integration via llama-flow","description":"Integrates LlamaIndex's document indexing and retrieval capabilities into the llama-flow workflow orchestration framework, enabling declarative composition of RAG pipelines. Uses llama-flow's node-based execution model to connect document loaders, index builders, and query engines as composable workflow steps with automatic data flow between stages.","intents":["Build RAG pipelines without manually wiring LlamaIndex components together","Compose multi-stage document indexing workflows with declarative syntax","Reuse indexed documents across multiple query workflows in a single application","Chain document loading, embedding, and retrieval steps with automatic context passing"],"best_for":["Teams building production RAG systems who want workflow orchestration on top of LlamaIndex","Developers migrating from imperative LlamaIndex code to declarative pipeline definitions","LLM application builders needing to compose complex multi-step retrieval workflows"],"limitations":["Requires understanding of both llama-flow execution model AND LlamaIndex API surface — steeper learning curve than using either independently","Limited to LlamaIndex's supported document types and index backends — no custom index implementations without extending the binding","No built-in persistence layer for indexed documents — requires external vector store configuration within LlamaIndex","Workflow composition is synchronous by default — async document indexing requires explicit async node definitions"],"requires":["Node.js 16+ or compatible JavaScript runtime","@llama-flow/core package for workflow orchestration","llama-index package (Python or TypeScript variant depending on runtime)","API keys for embedding models (OpenAI, Cohere, etc.) if using cloud embeddings"],"input_types":["document paths (file system or cloud storage URLs)","raw text content","structured metadata for documents","query strings for retrieval"],"output_types":["indexed document nodes with embeddings","retrieved context chunks ranked by relevance","structured retrieval results with metadata","workflow execution logs and performance metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_1","uri":"capability://automation.workflow.declarative.workflow.node.composition.for.llamaindex.operations","name":"declarative workflow node composition for llamaindex operations","description":"Exposes LlamaIndex document indexing and retrieval operations as first-class llama-flow workflow nodes with typed inputs/outputs and automatic error handling. Each node wraps a specific LlamaIndex operation (load documents, build index, query index) and integrates with llama-flow's execution engine to handle node scheduling, data passing, and failure recovery.","intents":["Define document indexing pipelines as visual or code-based DAGs without imperative boilerplate","Compose reusable workflow templates for common RAG patterns (load → embed → index → query)","Add conditional logic and branching to document retrieval workflows (e.g., different indices based on query type)","Monitor and debug multi-stage indexing workflows with execution tracing"],"best_for":["Non-Python developers who want to use LlamaIndex without writing Python code","Teams building workflow-based LLM applications where DAG composition is preferred over imperative code","Developers needing to version-control and test RAG pipeline definitions as code"],"limitations":["Node composition overhead adds latency compared to direct LlamaIndex API calls — typically 50-200ms per node execution","Limited to operations exposed as workflow nodes — advanced LlamaIndex customization requires extending the binding","No built-in support for streaming document ingestion — batch processing only","Workflow state is ephemeral by default — requires explicit persistence configuration for cross-session index reuse"],"requires":["llama-flow runtime (Node.js 16+)","@llama-flow/llamaindex package","LlamaIndex installed and configured with embedding model credentials","Basic understanding of DAG/workflow concepts"],"input_types":["workflow node definitions (JSON or TypeScript)","document source specifications","index configuration objects","query parameters"],"output_types":["workflow execution results with node outputs","indexed document collections","query results with provenance metadata","execution traces for debugging"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_10","uri":"capability://automation.workflow.error.handling.and.retry.strategies.for.indexing.retrieval.workflows","name":"error handling and retry strategies for indexing/retrieval workflows","description":"Implements configurable error handling and retry strategies as workflow nodes that can recover from transient failures (API timeouts, rate limits) and handle permanent failures gracefully. Supports exponential backoff, circuit breakers, and fallback operations to ensure workflow resilience.","intents":["Automatically retry failed indexing operations due to transient API errors","Implement circuit breakers to prevent cascading failures when embedding APIs are down","Fall back to alternative embedding providers if primary provider fails","Log and alert on indexing failures for manual investigation"],"best_for":["Production RAG systems needing resilience against API failures","Applications with strict uptime requirements","Systems using external embedding APIs with variable reliability"],"limitations":["Retry logic adds latency — exponential backoff can delay indexing by minutes","Circuit breakers require state management — need persistent configuration","Fallback strategies require multiple provider credentials — increases operational complexity","No automatic failure detection — requires explicit error handling in workflow nodes"],"requires":["Retry policy configuration (max retries, backoff strategy)","Circuit breaker configuration (failure threshold, reset timeout)","Fallback provider credentials if using multiple embedding providers","Error logging and alerting infrastructure"],"input_types":["workflow node definitions with error handling","retry policy specifications","circuit breaker configurations","fallback operation definitions"],"output_types":["successful operation results after retries","circuit breaker state (open/closed)","error logs with retry history","fallback operation results","failure alerts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_2","uri":"capability://automation.workflow.multi.index.query.routing.and.fallback.within.workflows","name":"multi-index query routing and fallback within workflows","description":"Enables workflow nodes to route queries to different LlamaIndex indices based on runtime conditions (query metadata, document type, index performance) and automatically fall back to alternative indices if primary retrieval fails. Implemented as conditional workflow nodes that evaluate routing logic and select the appropriate index before executing the query operation.","intents":["Route queries to specialized indices based on content domain or document type","Implement fallback retrieval strategies when primary index returns low-confidence results","A/B test different indexing strategies by routing subsets of queries to different indices","Handle index failures gracefully by automatically switching to backup indices"],"best_for":["Applications with heterogeneous document collections requiring domain-specific retrieval","Production RAG systems needing resilience against index failures","Teams experimenting with multiple indexing strategies and wanting to compare results"],"limitations":["Routing logic must be defined explicitly in workflow — no automatic routing based on query semantics","Multiple index queries increase latency — parallel routing requires explicit workflow branching","Fallback chains are sequential by default — no built-in timeout or circuit-breaker patterns","No cost optimization — all routed queries execute regardless of confidence scores"],"requires":["Multiple LlamaIndex indices pre-built and available","Routing logic defined as workflow conditional nodes","Query metadata or context to inform routing decisions","llama-flow conditional node support"],"input_types":["query strings with optional metadata","routing rules (conditions and index mappings)","index references or identifiers","fallback strategy definitions"],"output_types":["selected index identifier","retrieval results from chosen index","routing decision logs","fallback attempt history"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_3","uri":"capability://automation.workflow.streaming.document.ingestion.and.incremental.indexing.workflows","name":"streaming document ingestion and incremental indexing workflows","description":"Supports incremental document indexing within llama-flow workflows where new documents can be added to existing indices without full re-indexing. Implements document batching, embedding caching, and index update operations as workflow nodes that process incoming documents in stages and maintain index consistency across workflow executions.","intents":["Add new documents to an indexed corpus without rebuilding the entire index","Process document streams (e.g., from APIs or databases) and incrementally update indices","Implement scheduled re-indexing workflows that only process changed documents","Cache embeddings across workflow runs to reduce redundant embedding API calls"],"best_for":["Applications with continuously growing document collections","Systems processing document updates from external sources (databases, APIs, file systems)","Cost-sensitive applications where embedding API calls are a significant expense"],"limitations":["Incremental indexing requires index backends that support document updates — not all LlamaIndex index types support this","Embedding cache invalidation is manual — no automatic detection of changed documents","Workflow state must persist across executions — requires external state store configuration","No built-in deduplication — duplicate documents must be filtered before indexing"],"requires":["LlamaIndex index backend with update support (e.g., vector stores with document ID tracking)","Persistent storage for workflow state and embedding cache","Document change detection logic (timestamps, hashes, or external change feeds)","Embedding model API credentials if using cloud embeddings"],"input_types":["new document batches","document metadata (IDs, timestamps, change indicators)","index references","embedding cache state"],"output_types":["updated index with new documents","embedding cache updates","indexing operation metrics (documents added, embeddings cached)","index consistency validation results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_4","uri":"capability://data.processing.analysis.metadata.aware.document.filtering.and.preprocessing.in.workflows","name":"metadata-aware document filtering and preprocessing in workflows","description":"Integrates document filtering and preprocessing as workflow nodes that operate on document metadata (type, source, date, custom fields) before indexing. Filters can be chained together to implement complex document selection logic, and preprocessing nodes can normalize content, extract metadata, or split documents based on workflow-defined rules.","intents":["Filter documents by type, source, or date before indexing to reduce index size","Extract and normalize metadata from documents to improve retrieval relevance","Split large documents into chunks with custom logic (by section, by token count, by semantic boundaries)","Implement document quality checks and validation before indexing"],"best_for":["Applications with heterogeneous document sources requiring selective indexing","Systems needing custom document preprocessing beyond LlamaIndex's built-in loaders","Teams implementing document quality gates before indexing"],"limitations":["Preprocessing logic must be defined as workflow nodes — no inline transformation support","Complex metadata extraction requires custom node implementations","Filtering is applied before indexing — no post-index filtering based on query context","Performance degrades with large document batches — no built-in parallelization"],"requires":["Document sources with accessible metadata","Filtering and preprocessing logic defined as workflow nodes","Custom node implementations for domain-specific preprocessing","llama-flow node composition support"],"input_types":["raw documents with metadata","filter criteria (predicates on metadata fields)","preprocessing rules (transformation functions)","document batch specifications"],"output_types":["filtered document collections","preprocessed documents with normalized metadata","filtering/preprocessing operation logs","documents rejected by filters with rejection reasons"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_5","uri":"capability://automation.workflow.embedding.model.abstraction.and.provider.switching.in.workflows","name":"embedding model abstraction and provider switching in workflows","description":"Abstracts embedding model selection as a workflow configuration, allowing different embedding providers (OpenAI, Cohere, local models) to be swapped without changing indexing or query logic. Implemented as a configurable workflow parameter that gets passed to embedding nodes, enabling A/B testing of embedding models and cost optimization.","intents":["Switch embedding providers without rewriting indexing code","A/B test different embedding models to compare retrieval quality","Reduce embedding costs by using cheaper models for non-critical indices","Use local embedding models for privacy-sensitive documents"],"best_for":["Teams evaluating different embedding models for quality/cost tradeoffs","Applications with privacy requirements needing local embedding options","Cost-conscious projects wanting to optimize embedding API spend"],"limitations":["Embedding model switching requires re-indexing — existing indices cannot be queried with different embeddings","Different embedding models produce incompatible vector spaces — cannot mix models in same index","No automatic embedding quality evaluation — model selection is manual","Local embedding models require additional infrastructure and may have lower quality than cloud models"],"requires":["Embedding model credentials for selected providers","Workflow configuration support for embedding model selection","LlamaIndex embedding model integrations for chosen providers","Sufficient compute for local models if using on-device embeddings"],"input_types":["embedding model identifier/configuration","documents to embed","embedding model parameters (dimension, pooling strategy)"],"output_types":["vector embeddings in selected model's space","embedding operation metrics (latency, cost)","model compatibility information"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_6","uri":"capability://automation.workflow.query.result.ranking.and.relevance.scoring.in.workflows","name":"query result ranking and relevance scoring in workflows","description":"Implements post-retrieval ranking and relevance scoring as workflow nodes that re-rank LlamaIndex query results based on custom scoring functions or metadata. Supports multi-stage ranking (initial retrieval → filtering → re-ranking) and can combine multiple scoring signals (semantic similarity, metadata match, recency, custom domain scores).","intents":["Re-rank retrieval results based on domain-specific relevance signals beyond semantic similarity","Filter low-confidence results before returning to LLM","Combine multiple ranking signals (semantic + metadata + recency) for better relevance","Implement diversity-aware ranking to avoid redundant results"],"best_for":["Applications with domain-specific relevance requirements beyond semantic similarity","Systems needing to boost recent or high-quality documents in results","Teams implementing multi-signal ranking strategies"],"limitations":["Ranking logic must be implemented as custom workflow nodes — no built-in scoring functions","Re-ranking adds latency to query execution — typically 50-500ms depending on result set size","Scoring functions require domain knowledge to implement effectively","No automatic tuning of ranking weights — requires manual experimentation"],"requires":["Custom ranking/scoring logic implemented as workflow nodes","Query results from LlamaIndex retrieval","Metadata available on documents for scoring","Domain knowledge to define effective scoring functions"],"input_types":["query results from LlamaIndex","scoring function definitions","ranking parameters (weights, thresholds)","document metadata for scoring"],"output_types":["re-ranked result list","relevance scores per result","ranking operation logs","filtered results (below confidence threshold)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_7","uri":"capability://automation.workflow.workflow.based.index.lifecycle.management.and.versioning","name":"workflow-based index lifecycle management and versioning","description":"Manages index creation, updates, and versioning as workflow operations with explicit version tracking and rollback support. Indices are created as workflow artifacts with metadata (creation date, document count, embedding model) and can be versioned to enable A/B testing of different indexing strategies or rolling back to previous versions.","intents":["Version indices to track changes and enable rollback if indexing quality degrades","A/B test different indexing strategies by maintaining multiple index versions","Track index metadata (document count, embedding model, creation date) for debugging","Implement index lifecycle policies (retention, cleanup, archival)"],"best_for":["Production RAG systems needing index versioning and rollback capabilities","Teams experimenting with different indexing strategies and wanting to compare results","Applications with compliance requirements for audit trails of index changes"],"limitations":["Index versioning requires persistent storage — no built-in versioning without external state store","Multiple index versions consume storage — no automatic cleanup or compression","Rollback requires re-deploying workflows — not instantaneous","Version metadata is manual — no automatic tracking of indexing parameters"],"requires":["Persistent storage for index versions (vector store with versioning support or external storage)","Workflow state management for tracking index metadata","Index naming/tagging conventions to distinguish versions","Deployment infrastructure to switch between index versions"],"input_types":["index creation specifications","version metadata (tags, descriptions)","rollback target version identifiers","lifecycle policies"],"output_types":["versioned index artifacts with metadata","version history logs","rollback operation results","index lifecycle status"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_8","uri":"capability://automation.workflow.cross.workflow.index.sharing.and.reuse","name":"cross-workflow index sharing and reuse","description":"Enables indices created in one workflow to be referenced and reused in other workflows without re-indexing. Implemented through a shared index registry that tracks available indices and their metadata, allowing workflows to discover and load pre-built indices by name or query criteria.","intents":["Build once, query many times — create an index in one workflow and reuse in multiple query workflows","Share indices across teams or applications without duplicating indexing work","Implement index discovery and dynamic loading based on query metadata","Reduce indexing costs by reusing indices across multiple use cases"],"best_for":["Organizations with multiple applications sharing document collections","Teams wanting to centralize index management and avoid duplication","Cost-sensitive projects where indexing is expensive and should be amortized"],"limitations":["Index registry requires centralized state management — adds operational complexity","Index discovery is manual or requires explicit metadata — no automatic matching","Cross-workflow index sharing requires careful coordination — risk of stale indices","No built-in access control — requires external authorization layer for multi-tenant scenarios"],"requires":["Centralized index registry (database, key-value store, or custom service)","Index metadata standards for discovery","Workflow coordination mechanisms to ensure index freshness","Access control if sharing across teams/applications"],"input_types":["index identifiers or query criteria","index metadata for discovery","index reference specifications"],"output_types":["loaded index references","index metadata and statistics","index availability status","discovery results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-llama-flow-llamaindex__cap_9","uri":"capability://automation.workflow.workflow.execution.monitoring.and.performance.metrics.for.indexing.retrieval","name":"workflow execution monitoring and performance metrics for indexing/retrieval","description":"Captures detailed performance metrics for each workflow node (indexing latency, embedding API costs, query latency, result quality) and exposes them through a metrics interface. Metrics are collected automatically during workflow execution and can be aggregated, filtered, and exported for monitoring and optimization.","intents":["Monitor indexing performance and identify bottlenecks (slow embedding API, large document batches)","Track query latency and identify slow retrieval operations","Calculate embedding API costs per workflow execution","Identify opportunities for optimization (caching, batching, model switching)"],"best_for":["Production RAG systems needing performance visibility","Cost-conscious applications wanting to track and optimize embedding API spend","Teams debugging slow indexing or retrieval workflows"],"limitations":["Metrics collection adds overhead — typically 5-10% latency increase","Metrics storage requires external system — no built-in persistence","Metrics are node-level — no automatic correlation with query quality","Cost tracking requires API pricing configuration — not automatic"],"requires":["Metrics collection infrastructure (e.g., Prometheus, CloudWatch, custom logging)","Workflow execution context with timing information","API pricing data for cost calculation","Monitoring dashboard or analysis tools"],"input_types":["workflow execution events","node execution timing data","API call logs with costs","query results with quality metrics"],"output_types":["performance metrics per node","aggregated workflow metrics","cost breakdowns by operation","performance trend data","optimization recommendations"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or compatible JavaScript runtime","@llama-flow/core package for workflow orchestration","llama-index package (Python or TypeScript variant depending on runtime)","API keys for embedding models (OpenAI, Cohere, etc.) if using cloud embeddings","llama-flow runtime (Node.js 16+)","@llama-flow/llamaindex package","LlamaIndex installed and configured with embedding model credentials","Basic understanding of DAG/workflow concepts","Retry policy configuration (max retries, backoff strategy)","Circuit breaker configuration (failure threshold, reset timeout)"],"failure_modes":["Requires understanding of both llama-flow execution model AND LlamaIndex API surface — steeper learning curve than using either independently","Limited to LlamaIndex's supported document types and index backends — no custom index implementations without extending the binding","No built-in persistence layer for indexed documents — requires external vector store configuration within LlamaIndex","Workflow composition is synchronous by default — async document indexing requires explicit async node definitions","Node composition overhead adds latency compared to direct LlamaIndex API calls — typically 50-200ms per node execution","Limited to operations exposed as workflow nodes — advanced LlamaIndex customization requires extending the binding","No built-in support for streaming document ingestion — batch processing only","Workflow state is ephemeral by default — requires explicit persistence configuration for cross-session index reuse","Retry logic adds latency — exponential backoff can delay indexing by minutes","Circuit breakers require state management — need persistent configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.07075701760979364,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":255,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llama-flow-llamaindex","compare_url":"https://unfragile.ai/compare?artifact=llama-flow-llamaindex"}},"signature":"dnzvXXHpbEO4TjUIqA2d7nyNxdvPYqwjRhmv78fFPuCDOCZzRik/XiAd/gZO1Dq1LhWSEUxFpvf5DNveDeWyBw==","signedAt":"2026-06-15T12:39:15.505Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-flow-llamaindex","artifact":"https://unfragile.ai/llama-flow-llamaindex","verify":"https://unfragile.ai/api/v1/verify?slug=llama-flow-llamaindex","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"}}