{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-run-llama--llama_index","slug":"run-llama--llama_index","name":"llama_index","type":"mcp","url":"https://developers.llamaindex.ai","page_url":"https://unfragile.ai/run-llama--llama_index","categories":["mcp-servers","documentation"],"tags":["agents","application","data","fine-tuning","framework","llamaindex","llm","multi-agents","rag","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-run-llama--llama_index__cap_0","uri":"capability://data.processing.analysis.multi.source.document.ingestion.with.adaptive.node.parsing","name":"multi-source document ingestion with adaptive node parsing","description":"LlamaIndex ingests documents from 50+ sources (files, web, cloud APIs, databases) through a pluggable NodeParser system that intelligently chunks content based on document type and semantic boundaries. The framework uses a unified Document/Node abstraction that preserves metadata and relationships, enabling downstream RAG systems to maintain context fidelity. Parsers support hierarchical chunking, sliding windows, and semantic-aware splitting via language-specific tokenizers.","intents":["I need to load PDFs, web pages, and database records into a unified format for RAG","I want to chunk documents intelligently without losing semantic structure","I need to preserve document metadata and relationships through the ingestion pipeline"],"best_for":["Teams building RAG systems with heterogeneous data sources","Developers needing production-grade document parsing without building custom loaders","Organizations migrating from ad-hoc ETL to a standardized ingestion framework"],"limitations":["Node parsing adds 50-200ms per document depending on size and chunking strategy","Complex nested structures (deeply hierarchical PDFs, multi-table documents) may require custom parser implementation","No built-in deduplication across sources — requires post-ingestion dedup logic"],"requires":["Python 3.9+","llama-index-core>=0.14.19","For web readers: requests library","For cloud connectors: provider-specific SDKs (boto3 for AWS, google-cloud-storage for GCS)"],"input_types":["PDF files","Markdown/plain text","HTML/web pages","JSON/CSV","Database records (via connectors)","Cloud storage objects (S3, GCS, Azure Blob)"],"output_types":["Document objects with metadata","Node objects (chunked text with embeddings-ready format)","Structured metadata dictionaries"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_1","uri":"capability://memory.knowledge.vector.agnostic.semantic.indexing.with.pluggable.vector.stores","name":"vector-agnostic semantic indexing with pluggable vector stores","description":"LlamaIndex abstracts vector store operations through a standardized VectorStore interface, supporting 15+ backends (Milvus, Qdrant, PostgreSQL pgvector, Azure AI Search, Pinecone, Weaviate) without changing application code. The framework handles embedding generation, vector insertion, and similarity search through a unified QueryEngine that routes queries to the appropriate index type. Index creation is lazy — vectors are generated on-demand during ingestion using configurable embedding models.","intents":["I want to switch vector stores without rewriting my RAG application","I need to use multiple vector stores simultaneously for different data domains","I want to manage embeddings lifecycle (generation, updates, deletions) consistently"],"best_for":["Teams evaluating multiple vector databases before committing to one","Enterprises with multi-cloud or hybrid deployments requiring vector store flexibility","Developers building vendor-agnostic RAG platforms"],"limitations":["Vector store abstraction adds 10-30ms per query due to interface indirection","Advanced vector store features (hybrid search, metadata filtering) require custom QueryEngine implementation","Embedding model switching requires re-indexing all documents — no in-place embedding migration"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Vector store client library (e.g., qdrant-client, pymilvus, psycopg2 for PostgreSQL)","Embedding model API key (OpenAI, Hugging Face, or local model)"],"input_types":["Node objects with text content","Embedding vectors (pre-computed or generated on-demand)","Query strings or embedding vectors"],"output_types":["Ranked list of similar nodes with similarity scores","Metadata-filtered node subsets","Vector store statistics (index size, node count)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_10","uri":"capability://automation.workflow.llamapacks.and.pre.built.application.templates","name":"llamapacks and pre-built application templates","description":"LlamaIndex provides LlamaPacks — pre-built, production-ready application templates for common use cases (document Q&A, multi-document analysis, research agents, code analysis). Each pack includes optimized configurations, prompt templates, and best practices. Packs are composable — developers can combine multiple packs or customize individual components. The framework provides a registry of community-contributed packs with versioning and dependency management.","intents":["I want to quickly build a document Q&A system without designing the architecture from scratch","I need production-ready configurations and prompts for common RAG patterns","I want to learn best practices by examining pre-built application templates"],"best_for":["Teams rapidly prototyping LLM applications with limited time","Developers learning LlamaIndex patterns through working examples","Organizations building similar applications (document Q&A, research agents) repeatedly"],"limitations":["Packs are templates — customization requires understanding the underlying architecture","Pack updates may break customizations — no automatic migration path","Limited pack variety — only covers common use cases, not specialized domains","Packs may not reflect latest framework features — maintenance lag possible"],"requires":["Python 3.9+","llama-index>=0.14.19","LLM API credentials (OpenAI, Anthropic, etc.)","Optional: vector store and other integrations depending on pack"],"input_types":["Pack selection and configuration","Custom parameters (model, vector store, etc.)","Application-specific data (documents, queries)"],"output_types":["Instantiated application with configured components","Query results or agent responses","Application state and configuration"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_11","uri":"capability://search.retrieval.hybrid.retrieval.with.bm25.keyword.search.and.semantic.reranking","name":"hybrid retrieval with bm25 keyword search and semantic reranking","description":"LlamaIndex supports hybrid retrieval combining vector similarity search with BM25 keyword matching, optionally followed by semantic reranking using cross-encoder models or LLM-based ranking. The framework provides configurable fusion algorithms (reciprocal rank fusion, weighted combination) to merge results from multiple retrieval strategies. Reranking can use built-in models (Cohere, BGE) or custom LLM-based rankers that consider query-document relevance and other criteria.","intents":["I need to improve retrieval quality by combining semantic and keyword search","I want to rerank retrieval results using semantic similarity or LLM judgment","I need to handle both semantic queries and exact phrase matching"],"best_for":["Teams building search systems requiring high precision and recall","Developers optimizing retrieval quality for specialized vocabularies","Organizations with queries mixing semantic intent and exact phrase matching"],"limitations":["Hybrid retrieval adds 50-200ms per query due to multiple retrieval passes","Reranking adds 100-500ms depending on reranker model and result count","BM25 requires inverted index maintenance — adds ingestion overhead","Fusion algorithm tuning is manual — no automatic weight optimization"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Vector store for semantic search","BM25 index (built-in or external like Elasticsearch)","Optional: reranker model (Cohere API, local BGE model, or LLM)"],"input_types":["Query strings","Retrieval configuration (weights, fusion algorithm)","Reranker model selection"],"output_types":["Ranked list of nodes from hybrid retrieval","Reranking scores and explanations","Retrieval component contributions (vector score, BM25 score)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_12","uri":"capability://search.retrieval.document.level.metadata.filtering.and.structured.querying","name":"document-level metadata filtering and structured querying","description":"LlamaIndex supports metadata filtering at the document and node level, enabling structured queries that combine semantic search with metadata constraints (date ranges, document type, author, custom tags). The framework provides a query language for expressing complex filters and integrates filtering with all retrieval strategies (vector, keyword, graph). Metadata is preserved through the ingestion pipeline and can be used for post-retrieval filtering or pre-filtering to reduce search scope.","intents":["I need to filter search results by document metadata (date, source, category)","I want to combine semantic search with structured constraints","I need to restrict searches to specific document subsets based on metadata"],"best_for":["Teams building search systems with multi-tenant or multi-source data","Developers implementing document management systems with rich metadata","Organizations needing to enforce access control through metadata filtering"],"limitations":["Metadata filtering adds 10-50ms per query depending on filter complexity","Complex metadata queries may require custom filter implementations","Metadata schema must be predefined — no dynamic metadata addition","Vector stores vary in metadata filtering support — some require post-retrieval filtering"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Vector store with metadata filtering support (Qdrant, Milvus, Pinecone, etc.)","Metadata defined during document ingestion"],"input_types":["Query strings with optional metadata filters","Filter expressions (date ranges, categories, tags)","Metadata schema definitions"],"output_types":["Filtered node results matching semantic and metadata criteria","Filter application logs","Metadata statistics (filtered result count, metadata distribution)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_13","uri":"capability://text.generation.language.streaming.responses.with.token.level.control","name":"streaming responses with token-level control","description":"LlamaIndex supports streaming LLM responses at the token level, enabling real-time response display and early termination based on token content or count. The framework provides streaming abstractions for both LLM calls and query engines, with configurable buffering and batching. Streaming works across all LLM providers and integrates with observability for tracking streamed token usage.","intents":["I need to display LLM responses in real-time as tokens are generated","I want to implement early stopping based on response content or token count","I need to track token usage for streamed responses"],"best_for":["Teams building interactive chat interfaces requiring real-time responses","Developers implementing cost-aware applications with token budgets","Organizations needing to display long responses progressively"],"limitations":["Streaming adds 5-10ms per token due to buffering and transmission","Early termination may interrupt coherent responses — requires careful threshold tuning","Not all LLM providers support streaming equally — some have higher latency","Streaming responses are harder to debug — token-level errors may be missed"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM provider with streaming support (OpenAI, Anthropic, Ollama, etc.)","Async runtime for efficient streaming"],"input_types":["Query or prompt for streaming","Streaming configuration (buffer size, token limit)","Early termination criteria"],"output_types":["Token stream (generator or async iterator)","Streamed response text","Token usage statistics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_14","uri":"capability://automation.workflow.batch.processing.and.async.execution.for.scalable.ingestion","name":"batch processing and async execution for scalable ingestion","description":"LlamaIndex supports batch processing of documents and async execution for scalable ingestion and querying. The framework provides batch APIs for ingesting multiple documents in parallel, with configurable concurrency limits and error handling. Async execution is available throughout the stack (LLM calls, retrievals, agent steps), enabling efficient resource utilization. Batch operations support progress tracking and resumable processing for long-running jobs.","intents":["I need to ingest thousands of documents efficiently without blocking","I want to parallelize LLM calls and retrieval operations","I need to track progress and resume interrupted batch jobs"],"best_for":["Teams processing large document collections (1000+ documents)","Developers building scalable RAG systems with high throughput requirements","Organizations needing to optimize API costs through batching"],"limitations":["Batch processing requires careful concurrency tuning — too high causes rate limiting, too low wastes resources","Async execution adds complexity — requires understanding of async/await patterns","Error handling in batches is complex — partial failures require retry logic","Progress tracking requires external storage — no built-in progress persistence"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Async runtime (asyncio)","LLM provider with rate limit awareness (OpenAI, Anthropic)","Optional: external job queue (Celery, RQ) for distributed processing"],"input_types":["Document collections (list of Nodes or raw text)","Batch configuration (concurrency, chunk size)","Processing function (ingestion, query, etc.)"],"output_types":["Processed results (indexed documents, query responses)","Progress tracking (completed count, errors)","Performance metrics (throughput, latency)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_2","uri":"capability://search.retrieval.multi.index.query.orchestration.with.hybrid.retrieval.strategies","name":"multi-index query orchestration with hybrid retrieval strategies","description":"LlamaIndex's QueryEngine system orchestrates queries across multiple index types (vector, keyword, graph, structured) using a composable strategy pattern. The framework supports hybrid retrieval (combining vector similarity with BM25 keyword search, graph traversal, or SQL queries) through a unified query interface. Query routing is configurable — developers can implement custom routers that select the optimal index based on query semantics, or use built-in routers that combine results from multiple indices.","intents":["I need to search across vector, keyword, and graph indices simultaneously","I want to route queries intelligently to the best index based on query type","I need to combine results from multiple retrieval strategies with custom ranking"],"best_for":["Teams building enterprise search systems requiring multi-modal retrieval","Developers implementing domain-specific query routing logic","Organizations with complex knowledge graphs needing hybrid search"],"limitations":["Orchestrating multiple indices adds 100-500ms latency depending on index count and result merging complexity","Custom query routers require manual implementation — no automatic router optimization","Result ranking across heterogeneous indices (vector scores vs. BM25 vs. graph centrality) requires manual normalization"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Multiple index backends configured (vector store, keyword index, optional graph store)","LLM for query understanding (if using semantic routing)"],"input_types":["Query strings","Structured query objects with filters","Query embeddings (pre-computed)"],"output_types":["Ranked list of nodes from multiple indices","Merged/deduplicated result sets","Query routing decisions (which indices were queried)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_3","uri":"capability://automation.workflow.event.driven.workflow.orchestration.with.state.management","name":"event-driven workflow orchestration with state management","description":"LlamaIndex's Workflow system provides an event-driven architecture for building multi-step LLM applications using a declarative step-based model. Workflows are defined as a graph of Steps that emit and consume Events, with built-in state management for maintaining context across steps. The framework handles event routing, step scheduling, and error recovery automatically. Workflows support both synchronous and asynchronous execution, with optional persistence for long-running operations.","intents":["I need to build multi-step LLM agents with clear state transitions","I want to define complex workflows declaratively without managing event queues manually","I need to persist workflow state for resumable long-running operations"],"best_for":["Teams building autonomous agents with multi-step reasoning","Developers implementing complex document processing pipelines","Organizations needing resumable workflows with fault tolerance"],"limitations":["Event-driven architecture adds 20-50ms per step due to event routing overhead","Workflow state persistence requires external storage (database, file system) — no built-in in-memory state","Debugging multi-step workflows requires event tracing — standard Python debuggers are insufficient","No built-in workflow visualization — requires custom tooling to inspect workflow DAG"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Async runtime (asyncio) for async workflows","Optional: external state store (PostgreSQL, Redis) for persistence"],"input_types":["Step definitions (Python classes inheriting from Step)","Event objects with typed payloads","Workflow configuration (YAML or Python)"],"output_types":["Workflow execution results","Event logs with timestamps","Persisted workflow state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_4","uri":"capability://planning.reasoning.multi.agent.orchestration.with.memory.and.tool.coordination","name":"multi-agent orchestration with memory and tool coordination","description":"LlamaIndex's Agent system supports both single-agent and multi-agent architectures with configurable memory backends and tool calling patterns. Agents can be composed hierarchically (sub-agents delegating to other agents) or coordinated through a central orchestrator. The framework provides memory abstractions (chat history, summary memory, hybrid memory) that persist across agent interactions. Tool calling is standardized through a schema-based registry supporting OpenAI, Anthropic, and Ollama function-calling APIs.","intents":["I need to build multi-agent systems where agents collaborate on complex tasks","I want agents to maintain conversation history and learn from past interactions","I need to coordinate tool usage across multiple agents without conflicts"],"best_for":["Teams building autonomous agent systems for document analysis and research","Developers implementing hierarchical agent architectures (manager agents coordinating workers)","Organizations needing persistent agent memory across sessions"],"limitations":["Multi-agent coordination adds 200-500ms per agent interaction due to inter-agent communication","Memory persistence requires external storage — no built-in in-process memory beyond chat history","Agent tool conflicts (multiple agents calling incompatible tools) require custom coordination logic","No built-in agent performance monitoring — requires custom instrumentation"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM with function-calling support (OpenAI, Anthropic, Ollama, or compatible)","Tool definitions with JSON schemas","Optional: external memory store (database) for persistent agent state"],"input_types":["User queries/instructions","Tool definitions with schemas","Agent configuration (model, memory type, tools)","Chat history (for memory-enabled agents)"],"output_types":["Agent responses with reasoning traces","Tool call logs with arguments and results","Updated agent memory state"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_5","uri":"capability://memory.knowledge.knowledge.graph.construction.and.property.graph.indexing","name":"knowledge graph construction and property graph indexing","description":"LlamaIndex's Knowledge Graph system automatically extracts entities and relationships from documents using LLM-based extraction, building a Property Graph Index that supports both semantic and structural queries. The framework provides graph store abstractions (Neo4j, Kuzu, Nebula) and enables hybrid retrieval combining graph traversal with vector search. Graph construction is configurable — developers can customize entity/relationship extraction prompts, define custom schemas, or use pre-built extractors.","intents":["I need to extract structured knowledge (entities, relationships) from unstructured documents","I want to query documents using both semantic similarity and graph relationships","I need to build knowledge graphs that evolve as new documents are ingested"],"best_for":["Teams building knowledge management systems for research or enterprise data","Developers implementing semantic search with relationship-aware ranking","Organizations needing to surface implicit connections in document collections"],"limitations":["LLM-based entity extraction adds 500ms-2s per document depending on document size and LLM latency","Extraction quality depends on LLM capability — hallucinations can introduce spurious relationships","Graph schema must be predefined or inferred — no automatic schema evolution","Graph store integration requires external database (Neo4j, Kuzu) — no embedded graph option"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM for entity/relationship extraction (OpenAI, Anthropic, or local model)","Graph store client (neo4j, kuzu, nebula client libraries)","Graph database instance (Neo4j, Kuzu, Nebula Graph)"],"input_types":["Document text or Node objects","Entity/relationship extraction prompts","Graph schema definitions"],"output_types":["Property Graph Index with nodes and edges","Entity and relationship lists with confidence scores","Graph traversal results (paths, connected components)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_6","uri":"capability://tool.use.integration.llm.provider.abstraction.with.unified.tool.calling.interface","name":"llm provider abstraction with unified tool-calling interface","description":"LlamaIndex abstracts LLM interactions through a unified LLM interface supporting 20+ providers (OpenAI, Anthropic, AWS Bedrock, Google GenAI, Ollama, Azure OpenAI, Hugging Face, etc.) without changing application code. The framework standardizes tool calling across providers with different native formats (OpenAI functions, Anthropic tools, Ollama function calling) through a schema-based registry. LLM selection is configurable at runtime — applications can switch models or providers without code changes.","intents":["I want to build LLM applications that work with multiple providers interchangeably","I need to use different models for different tasks (fast model for routing, powerful model for reasoning)","I want to standardize tool calling across providers with different APIs"],"best_for":["Teams building multi-provider LLM applications for cost optimization or redundancy","Developers implementing model-agnostic RAG systems","Organizations evaluating multiple LLM providers before committing"],"limitations":["LLM abstraction adds 5-10ms per call due to interface indirection","Provider-specific features (vision, function calling variants) require custom implementation","Tool calling schema normalization may lose provider-specific optimizations","No built-in fallback mechanism — requires custom retry logic for provider failures"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Provider-specific SDK (openai, anthropic, boto3, google-generativeai, ollama, etc.)","API keys or credentials for chosen LLM provider(s)"],"input_types":["Chat messages with role and content","Tool schemas (JSON format)","System prompts and user queries"],"output_types":["LLM responses with content and tool calls","Structured tool call arguments","Token usage statistics"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.based.querying","name":"structured data extraction with schema-based querying","description":"LlamaIndex supports structured data extraction from documents using LLM-based extraction with optional schema validation. The framework can extract data into Pydantic models, JSON, or SQL tables, with configurable extraction prompts and validation rules. Structured indices enable SQL-like querying over extracted data, combining semantic search with structured filters. The system supports both single-document extraction and batch extraction across document collections.","intents":["I need to extract structured data (tables, entities, key-value pairs) from unstructured documents","I want to query extracted data using SQL-like filters combined with semantic search","I need to validate extracted data against schemas before storing"],"best_for":["Teams building document understanding systems for forms, contracts, or reports","Developers implementing data extraction pipelines with quality validation","Organizations needing to convert unstructured documents to structured databases"],"limitations":["LLM-based extraction adds 500ms-3s per document depending on schema complexity","Extraction accuracy depends on schema clarity and LLM capability — ambiguous schemas produce inconsistent results","Schema changes require re-extraction of all documents — no schema migration","Batch extraction requires sequential LLM calls — no built-in parallelization"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM for extraction (OpenAI, Anthropic, or local model)","Pydantic models or JSON schema definitions","Optional: SQL database for storing extracted structured data"],"input_types":["Document text or Node objects","Pydantic model or JSON schema definitions","Extraction prompts (customizable)"],"output_types":["Extracted data in Pydantic model format","JSON or CSV representations","Validation results with error messages"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_8","uri":"capability://data.processing.analysis.fine.tuning.pipeline.with.dataset.generation.and.evaluation","name":"fine-tuning pipeline with dataset generation and evaluation","description":"LlamaIndex provides end-to-end fine-tuning support including automatic training data generation from documents, fine-tuning orchestration across providers (OpenAI, Hugging Face), and evaluation metrics for retrieval and generation quality. The framework generates synthetic question-answer pairs from documents, supports custom evaluation metrics, and tracks fine-tuning experiments. Fine-tuning can target embedding models, LLMs, or ranking models depending on application needs.","intents":["I need to generate training data for fine-tuning from my document collection","I want to fine-tune embedding models or LLMs to improve domain-specific performance","I need to evaluate fine-tuned models against baseline metrics"],"best_for":["Teams building domain-specific RAG systems requiring custom embeddings or models","Developers optimizing retrieval quality for specialized vocabularies or domains","Organizations with sufficient data to justify fine-tuning investments"],"limitations":["Synthetic data generation adds 1-5s per document depending on generation strategy","Fine-tuning requires significant compute resources and API costs (OpenAI fine-tuning: $0.03-0.30 per 1K tokens)","Generated training data quality depends on base LLM — may require manual curation","No built-in A/B testing framework — requires custom evaluation harness"],"requires":["Python 3.9+","llama-index-core>=0.14.19","LLM for synthetic data generation (OpenAI, Anthropic)","Fine-tuning provider credentials (OpenAI API key for model fine-tuning)","Sufficient documents (100+) for meaningful training data generation"],"input_types":["Document collection (Nodes or raw text)","Fine-tuning configuration (model, hyperparameters)","Evaluation metrics (custom or built-in)"],"output_types":["Generated training datasets (QA pairs, triplets)","Fine-tuned model IDs or weights","Evaluation metrics and comparison reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-run-llama--llama_index__cap_9","uri":"capability://automation.workflow.observability.and.instrumentation.with.event.tracing","name":"observability and instrumentation with event tracing","description":"LlamaIndex provides comprehensive observability through an instrumentation framework that captures events across the entire application lifecycle (LLM calls, retrieval operations, agent steps, workflow transitions). The framework integrates with observability platforms (Langfuse, Arize, Datadog, New Relic) and provides structured event logging with automatic context propagation. Developers can define custom events and metrics, and the framework handles event batching and async transmission.","intents":["I need to trace LLM calls, retrieval operations, and agent decisions for debugging","I want to monitor application performance metrics (latency, token usage, cost)","I need to integrate with observability platforms for production monitoring"],"best_for":["Teams operating LLM applications in production requiring observability","Developers debugging complex multi-step workflows and agent behaviors","Organizations needing cost tracking and performance optimization"],"limitations":["Event instrumentation adds 5-20ms per operation due to event capture and transmission","Observability platform integration requires additional API keys and configuration","Event batching introduces slight latency (100-500ms) before events appear in observability platform","Custom event definitions require code changes — no runtime event schema modification"],"requires":["Python 3.9+","llama-index-core>=0.14.19","Observability platform account (Langfuse, Arize, Datadog, etc.) with API credentials","Network connectivity to observability platform"],"input_types":["Application events (LLM calls, retrievals, agent steps)","Custom metrics and metadata","Event configuration (sampling rate, batching)"],"output_types":["Structured event logs with timestamps and context","Performance metrics (latency, token usage)","Traces showing operation relationships"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","llama-index-core>=0.14.19","For web readers: requests library","For cloud connectors: provider-specific SDKs (boto3 for AWS, google-cloud-storage for GCS)","Vector store client library (e.g., qdrant-client, pymilvus, psycopg2 for PostgreSQL)","Embedding model API key (OpenAI, Hugging Face, or local model)","llama-index>=0.14.19","LLM API credentials (OpenAI, Anthropic, etc.)","Optional: vector store and other integrations depending on pack","Vector store for semantic search"],"failure_modes":["Node parsing adds 50-200ms per document depending on size and chunking strategy","Complex nested structures (deeply hierarchical PDFs, multi-table documents) may require custom parser implementation","No built-in deduplication across sources — requires post-ingestion dedup logic","Vector store abstraction adds 10-30ms per query due to interface indirection","Advanced vector store features (hybrid search, metadata filtering) require custom QueryEngine implementation","Embedding model switching requires re-indexing all documents — no in-place embedding migration","Packs are templates — customization requires understanding the underlying architecture","Pack updates may break customizations — no automatic migration path","Limited pack variety — only covers common use cases, not specialized domains","Packs may not reflect latest framework features — maintenance lag possible","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8382827266889569,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:19.180Z","last_commit":"2026-05-01T14:56:08Z"},"community":{"stars":49109,"forks":7348,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=run-llama--llama_index","compare_url":"https://unfragile.ai/compare?artifact=run-llama--llama_index"}},"signature":"jLKblrnvK901Yb6qozHmByhTxrhPq50j/Phgto6Mo2zZTLxg7sL9f1xnI5XG/qXFawlNKwJo9vBDOyw07bW3Cw==","signedAt":"2026-06-20T03:59:59.653Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/run-llama--llama_index","artifact":"https://unfragile.ai/run-llama--llama_index","verify":"https://unfragile.ai/api/v1/verify?slug=run-llama--llama_index","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"}}