{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openlit","slug":"openlit","name":"OpenLIT","type":"repo","url":"https://github.com/openlit/openlit","page_url":"https://unfragile.ai/openlit","categories":["observability"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openlit__cap_0","uri":"capability://automation.workflow.auto.instrumentation.of.llm.provider.calls.with.semantic.telemetry.capture","name":"auto-instrumentation of llm provider calls with semantic telemetry capture","description":"Automatically intercepts and instruments calls to 30+ LLM providers (OpenAI, Anthropic, Google, Azure, local models) using the OpenTelemetry BaseInstrumentor pattern to patch third-party libraries at runtime. Captures prompts, completions, token usage, latency, costs, and model metadata without code changes, exporting structured traces and metrics via OTLP to any OpenTelemetry-compatible backend. Uses provider-specific wrapper implementations to normalize heterogeneous APIs into OpenTelemetry semantic conventions.","intents":["Monitor LLM API costs and token usage across multiple providers in production","Capture full request/response payloads and latency metrics for LLM calls without modifying application code","Correlate LLM calls with application traces for end-to-end observability","Debug LLM provider failures and performance bottlenecks with structured telemetry"],"best_for":["AI/ML teams running multi-provider LLM applications in production","Developers building cost-aware LLM applications needing real-time usage tracking","Organizations migrating to OpenTelemetry-native observability stacks"],"limitations":["Instrumentation overhead adds ~5-15ms per LLM call due to telemetry export","Streaming responses require buffering for token counting, increasing memory usage","Vision/audio model payloads (images, audio bytes) are not captured by default to avoid storage bloat","Local model instrumentation (Ollama, vLLM) requires manual endpoint configuration"],"requires":["Python 3.8+ (SDK), Node.js 16+ (TypeScript SDK), or Go 1.18+ (Go SDK)","OpenTelemetry Collector or compatible OTLP endpoint (Grafana Cloud, New Relic, SigNoz, etc.)","LLM provider API keys (OpenAI, Anthropic, etc.) for the providers being instrumented"],"input_types":["LLM API calls (text prompts, function calls, streaming completions)","Provider credentials and configuration (API keys, endpoints, model names)"],"output_types":["OpenTelemetry traces (spans with attributes: prompt, completion, tokens, cost, latency)","OpenTelemetry metrics (counters: token usage, cost; histograms: latency, input/output tokens)"],"categories":["automation-workflow","observability","instrumentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_1","uri":"capability://automation.workflow.vector.database.instrumentation.with.embedding.and.retrieval.tracking","name":"vector database instrumentation with embedding and retrieval tracking","description":"Auto-instruments vector database clients (Qdrant, Chroma, Pinecone, Milvus, Astra, Weaviate) to capture embedding operations, retrieval queries, and vector similarity metrics. Tracks embedding model usage, vector dimensions, retrieval latency, and result cardinality as OpenTelemetry spans and metrics. Integrates with the LLM instrumentation pipeline to correlate RAG retrieval steps with downstream LLM calls for end-to-end observability.","intents":["Monitor embedding API costs and latency for RAG pipelines","Track vector database query performance and retrieval quality metrics","Correlate embedding operations with LLM calls to understand RAG pipeline bottlenecks","Debug vector similarity search failures and embedding model performance"],"best_for":["Teams building RAG (Retrieval-Augmented Generation) systems with multiple vector stores","Developers optimizing embedding model selection and vector database performance","Organizations monitoring end-to-end RAG pipeline costs and latency"],"limitations":["Vector payloads (embeddings themselves) are not captured to avoid storage overhead","Batch retrieval operations are tracked as single spans, limiting granularity for large batches","Custom vector database implementations require manual instrumentation","Embedding model costs are estimated based on token counts, not actual provider billing"],"requires":["Python 3.8+ or TypeScript SDK with vector database client library installed","Vector database instance (Qdrant, Chroma, Pinecone, etc.) with network connectivity","OpenTelemetry Collector or OTLP endpoint for telemetry export"],"input_types":["Vector database queries (text queries, embedding vectors, filter conditions)","Embedding model calls (text inputs, model names, dimensions)"],"output_types":["OpenTelemetry traces (spans for embedding operations, retrieval queries, latency)","OpenTelemetry metrics (counters: queries, embeddings; histograms: retrieval latency, result count)"],"categories":["automation-workflow","data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_10","uri":"capability://data.processing.analysis.semantic.conventions.and.standardized.telemetry.schema.for.ai.operations","name":"semantic conventions and standardized telemetry schema for ai operations","description":"Defines and implements OpenTelemetry semantic conventions for AI operations (LLM calls, embeddings, vector database queries, agent steps) that standardize attribute names, span types, and metric definitions across all SDKs and providers. Semantic conventions enable consistent telemetry collection across heterogeneous LLM providers and frameworks, allowing downstream tools to understand and correlate AI telemetry without provider-specific logic. Conventions are documented in the OpenTelemetry specification and implemented in all SDKs.","intents":["Standardize telemetry schema across multiple LLM providers and frameworks","Enable downstream tools (dashboards, alerts, analysis) to work with AI telemetry without provider-specific logic","Correlate telemetry across heterogeneous AI systems using standard attribute names","Contribute to OpenTelemetry standards for AI observability"],"best_for":["Organizations building observability tools for AI applications","Teams standardizing on OpenTelemetry for AI observability","Developers contributing to OpenTelemetry AI semantic conventions"],"limitations":["Semantic conventions are evolving; some AI operations may not have standardized conventions yet","Custom AI operations require manual mapping to semantic conventions","Downstream tools must implement support for AI semantic conventions; not all tools support them yet"],"requires":["OpenTelemetry SDK and semantic conventions library","Understanding of OpenTelemetry concepts (spans, attributes, metrics)"],"input_types":["AI operations (LLM calls, embeddings, vector database queries, agent steps)","Operation metadata (model names, provider names, token counts, latency)"],"output_types":["OpenTelemetry spans and metrics with standardized attributes","Telemetry that conforms to OpenTelemetry AI semantic conventions"],"categories":["data-processing-analysis","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_11","uri":"capability://automation.workflow.trace.context.propagation.and.distributed.tracing.across.services","name":"trace context propagation and distributed tracing across services","description":"Implements W3C Trace Context propagation to correlate traces across multiple services and languages in distributed AI applications. Automatically injects trace context (trace ID, span ID, trace flags) into outgoing requests (HTTP, gRPC) and extracts trace context from incoming requests to maintain trace continuity. Enables end-to-end tracing of requests that span multiple microservices, including LLM calls, vector database queries, and application logic.","intents":["Correlate LLM calls across multiple microservices in distributed AI applications","Trace requests end-to-end from user input through LLM inference to response","Debug latency and failures in distributed AI systems by following trace context","Understand service dependencies and call patterns in complex AI architectures"],"best_for":["Organizations with distributed AI microservices architectures","Teams debugging latency and failures across multiple services","Developers understanding service dependencies in complex AI systems"],"limitations":["Trace context propagation requires explicit header injection in HTTP/gRPC clients; not automatic for all transports","Large traces spanning many services may exceed backend storage limits; requires sampling","Trace context is lost if services don't propagate headers correctly; requires careful implementation","Cross-language trace correlation requires W3C Trace Context support in all languages"],"requires":["OpenTelemetry SDK with trace context propagation support","HTTP/gRPC clients that support header injection (most modern clients do)","Backend support for distributed tracing (most OpenTelemetry backends support this)"],"input_types":["Outgoing HTTP/gRPC requests (to be instrumented with trace context headers)","Incoming HTTP/gRPC requests (to extract trace context from)"],"output_types":["HTTP/gRPC requests with W3C Trace Context headers (traceparent, tracestate)","Distributed traces in backend with correlated spans across services"],"categories":["automation-workflow","tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_12","uri":"capability://automation.workflow.real.time.telemetry.streaming.and.live.dashboard.visualization","name":"real-time telemetry streaming and live dashboard visualization","description":"Provides a real-time dashboard that streams telemetry data (traces, metrics, logs) from the OpenTelemetry Collector to web clients via WebSocket or Server-Sent Events (SSE). Displays live LLM calls, token usage, latency, and costs as they occur without requiring page refresh. Dashboard includes filtering, search, and drill-down capabilities to explore telemetry in real-time. Enables developers to monitor LLM applications during development and debugging.","intents":["Monitor LLM application behavior in real-time during development and testing","Debug LLM calls and framework operations as they execute","Visualize token usage and costs in real-time to identify optimization opportunities","Explore telemetry data with filtering and search without waiting for batch processing"],"best_for":["Developers debugging LLM applications during development","Teams monitoring LLM application behavior during testing and deployment","Operators monitoring LLM inference services in production"],"limitations":["Real-time streaming adds ~50-100ms latency to telemetry visibility","Dashboard performance degrades with high telemetry volume (>1000 spans/second); requires sampling","Browser-based dashboard requires WebSocket or SSE support; some firewalls may block these protocols","Historical data is not persisted in the dashboard; requires backend storage for long-term analysis"],"requires":["OpenLIT platform instance with dashboard deployed","Web browser with WebSocket or SSE support","Network connectivity from browser to OpenLIT platform"],"input_types":["OpenTelemetry traces and metrics from the Collector","User interactions (filtering, searching, drilling down)"],"output_types":["Real-time dashboard visualization (HTML/JavaScript)","Filtered and searched telemetry data (JSON)"],"categories":["automation-workflow","observability","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_13","uri":"capability://data.processing.analysis.batch.evaluation.and.historical.analysis.of.llm.traces","name":"batch evaluation and historical analysis of llm traces","description":"Provides batch evaluation capabilities to analyze historical LLM traces stored in the platform, including cost analysis, performance trends, prompt effectiveness, and policy compliance. Supports SQL-like queries on trace data to aggregate metrics by model, provider, user, or custom dimensions. Enables teams to identify optimization opportunities, track performance over time, and audit LLM usage for compliance.","intents":["Analyze historical LLM costs and identify optimization opportunities","Track LLM performance trends over time (latency, token usage, error rates)","Evaluate prompt effectiveness by comparing performance across prompt versions","Audit LLM usage for compliance with policies and regulations"],"best_for":["Teams analyzing LLM costs and performance trends over time","Developers evaluating prompt effectiveness through historical data","Organizations auditing LLM usage for compliance and governance"],"limitations":["Batch queries may take seconds to minutes depending on data volume; not suitable for real-time analysis","Query language is limited to predefined aggregations; complex custom queries require SQL knowledge","Historical data retention is limited by storage capacity; older traces may be archived or deleted","Batch evaluation does not support real-time streaming of new traces"],"requires":["OpenLIT platform instance with historical trace storage (ClickHouse or similar)","Access to the batch evaluation API or UI","Understanding of trace schema and available dimensions for aggregation"],"input_types":["Historical LLM traces stored in the platform","Query parameters (time range, filters, aggregation dimensions)"],"output_types":["Aggregated metrics (cost, latency, token usage, error rates)","Trend analysis (performance over time, cost trends)","Compliance audit reports (policy violations, usage patterns)"],"categories":["data-processing-analysis","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_2","uri":"capability://automation.workflow.ai.framework.instrumentation.for.langchain.langgraph.and.agent.frameworks","name":"ai framework instrumentation for langchain, langgraph, and agent frameworks","description":"Auto-instruments AI frameworks (LangChain, LangGraph, AutoGen, CrewAI) to capture framework-level operations: chain execution, tool calls, agent reasoning steps, and memory interactions. Instruments at the framework abstraction layer (e.g., LangChain's Runnable interface, LangGraph's StateGraph) to create hierarchical spans that represent the logical flow of AI applications. Automatically correlates framework operations with underlying LLM and vector database calls.","intents":["Monitor end-to-end execution of LangChain chains and LangGraph workflows","Track agent reasoning steps, tool calls, and decision points in multi-step AI applications","Debug framework-level failures and performance bottlenecks in complex agent systems","Correlate framework operations with underlying LLM and tool calls for full visibility"],"best_for":["Teams building complex multi-step AI agents with LangChain or LangGraph","Developers debugging agent reasoning and tool selection behavior","Organizations monitoring cost and latency of orchestrated AI workflows"],"limitations":["Framework instrumentation adds ~10-20ms overhead per chain/graph execution due to span creation","Custom chain implementations require manual instrumentation if they don't inherit from framework base classes","Agent memory (conversation history, context) is not captured by default to avoid PII exposure","Tool call arguments and results are captured but may contain sensitive data"],"requires":["Python 3.8+ with LangChain, LangGraph, or other supported framework installed","OpenTelemetry SDK and OTLP exporter configured","Framework version compatibility (LangChain 0.1+, LangGraph 0.1+, etc.)"],"input_types":["Framework objects (LangChain Chains, LangGraph StateGraphs, Agent instances)","Tool definitions and configurations","User inputs and prompts passed to frameworks"],"output_types":["OpenTelemetry traces (hierarchical spans for chains, tools, agent steps)","OpenTelemetry metrics (counters: tool calls, chain executions; histograms: execution latency)"],"categories":["automation-workflow","planning-reasoning","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_3","uri":"capability://data.processing.analysis.gpu.resource.monitoring.and.nvidia.metrics.collection","name":"gpu resource monitoring and nvidia metrics collection","description":"Collects GPU metrics (utilization, memory usage, temperature, power consumption) from NVIDIA GPUs using the OpenTelemetry GPU Collector and exposes them as OpenTelemetry metrics. Integrates with the Python SDK to correlate GPU metrics with LLM inference operations, enabling visibility into hardware resource consumption during model serving. Supports Kubernetes environments via the OpenLIT Operator for automated GPU metric collection across clusters.","intents":["Monitor GPU utilization and memory consumption during LLM inference","Correlate GPU metrics with LLM latency and throughput for performance optimization","Track GPU power consumption and thermal metrics for cost and reliability analysis","Detect GPU bottlenecks and resource contention in multi-model serving environments"],"best_for":["Teams running LLM inference on NVIDIA GPUs in production","ML engineers optimizing GPU utilization and inference performance","Organizations monitoring hardware costs and thermal constraints in GPU clusters"],"limitations":["Requires NVIDIA CUDA Toolkit and nvidia-ml-py library, not available on non-NVIDIA hardware","GPU metrics collection adds ~2-5% overhead to inference latency","Kubernetes GPU monitoring requires the OpenLIT Operator to be deployed in the cluster","Historical GPU metrics are not persisted by default; requires external time-series database"],"requires":["NVIDIA GPU with CUDA Compute Capability 3.5+","NVIDIA CUDA Toolkit 11.0+ and nvidia-ml-py library installed","Python 3.8+ with OpenLIT SDK configured","For Kubernetes: OpenLIT Operator deployed in the cluster"],"input_types":["NVIDIA GPU device IDs and CUDA context information","LLM inference operations (model names, batch sizes, sequence lengths)"],"output_types":["OpenTelemetry metrics (gauges: GPU utilization, memory usage, temperature; counters: power consumption)","Correlated traces linking GPU metrics to LLM inference operations"],"categories":["data-processing-analysis","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_4","uri":"capability://memory.knowledge.prompt.management.and.versioning.with.prompt.hub","name":"prompt management and versioning with prompt hub","description":"Provides a centralized Prompt Hub interface for storing, versioning, and managing prompts used in LLM applications. Enables teams to version prompts, track changes, and retrieve specific prompt versions at runtime via API. Integrates with the Python SDK to automatically capture prompts from LLM calls and store them in the Prompt Hub, enabling prompt-level observability and A/B testing of prompt variations.","intents":["Version and track changes to prompts used in production LLM applications","Retrieve specific prompt versions at runtime for reproducibility and rollback","Compare prompt performance across versions using captured telemetry","Manage prompt templates and variables across multiple applications and teams"],"best_for":["Teams iterating on prompt engineering and A/B testing prompt variations","Organizations requiring prompt governance and change tracking for compliance","Developers debugging LLM behavior by comparing prompts across versions"],"limitations":["Prompt Hub is a centralized service that requires network connectivity; offline prompt management is not supported","Prompt versioning is manual; no automatic version creation on prompt changes","Large prompts (>100KB) may exceed storage limits; requires external prompt storage for very large templates","No built-in prompt optimization or suggestion features; requires manual prompt engineering"],"requires":["OpenLIT platform instance deployed (Docker, Kubernetes, or cloud)","Python SDK with Prompt Hub integration enabled","Network connectivity to the Prompt Hub API endpoint"],"input_types":["Prompt text (strings, templates with variables)","Prompt metadata (version tags, descriptions, model associations)"],"output_types":["Prompt versions (versioned prompt text with metadata)","Prompt retrieval API responses (JSON with prompt content and metadata)"],"categories":["memory-knowledge","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_5","uri":"capability://data.processing.analysis.cost.tracking.and.attribution.across.llm.providers.and.models","name":"cost tracking and attribution across llm providers and models","description":"Automatically calculates and tracks costs for LLM API calls, embedding operations, and vector database queries based on provider pricing models. Supports cost attribution by model, provider, user, application, and custom dimensions. Integrates with the instrumentation pipeline to capture token counts and model metadata, then applies provider-specific pricing rules to calculate per-call costs. Exports cost metrics as OpenTelemetry metrics for aggregation and analysis.","intents":["Track total LLM API costs across multiple providers and models in production","Attribute costs to specific applications, users, or business units for chargeback","Identify cost optimization opportunities by analyzing per-model and per-provider costs","Set up cost alerts and budgets to prevent unexpected spending"],"best_for":["Organizations running multi-provider LLM applications with cost accountability requirements","Developers optimizing LLM costs by comparing model pricing and performance","Teams implementing chargeback models for LLM usage across departments"],"limitations":["Cost calculations are based on published provider pricing; actual costs may differ due to volume discounts or custom agreements","Pricing rules must be manually updated when providers change their pricing models","Token counting for streaming responses requires buffering, which adds latency","Custom models and fine-tuned models require manual pricing configuration"],"requires":["Python 3.8+ or TypeScript SDK with cost tracking enabled","LLM provider API keys for the providers being tracked","OpenTelemetry Collector or OTLP endpoint for metrics export"],"input_types":["LLM API calls with token counts and model metadata","Embedding operations with token counts and model names","Vector database queries with operation types and result cardinality"],"output_types":["OpenTelemetry metrics (gauges: total cost, cost by model/provider; counters: cost per call)","Cost attribution data (JSON with cost breakdown by dimension)"],"categories":["data-processing-analysis","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_6","uri":"capability://safety.moderation.evaluations.and.guardrails.with.rule.based.policy.enforcement","name":"evaluations and guardrails with rule-based policy enforcement","description":"Provides a rule engine for defining and enforcing policies on LLM outputs, including content filtering, PII detection, toxicity detection, and custom validation rules. Evaluations are defined as rules that can be applied to captured prompts and completions in the OpenLIT platform. Integrates with the instrumentation pipeline to automatically evaluate LLM outputs against defined rules and flag violations. Supports both real-time evaluation during inference and batch evaluation of historical traces.","intents":["Detect and flag PII, toxicity, or policy violations in LLM outputs","Enforce content policies and safety guardrails on production LLM applications","Audit LLM outputs for compliance with regulatory requirements","Identify patterns of policy violations for prompt engineering improvements"],"best_for":["Organizations deploying LLMs in regulated industries (healthcare, finance) with compliance requirements","Teams implementing safety guardrails for public-facing LLM applications","Developers debugging policy violations and improving prompt engineering"],"limitations":["Rule-based evaluation is limited to pattern matching and simple heuristics; no ML-based semantic evaluation","PII detection relies on regex patterns and named entity recognition, which may have false positives/negatives","Real-time evaluation adds ~50-200ms latency per LLM call depending on rule complexity","Custom rules require manual definition; no automatic rule generation from examples"],"requires":["OpenLIT platform instance with evaluations module enabled","Python SDK with guardrails integration configured","Rule definitions (JSON or YAML format) for policies to enforce"],"input_types":["LLM prompts and completions (text)","Rule definitions (regex patterns, entity types, custom validation logic)"],"output_types":["Evaluation results (JSON with rule violations, severity levels, flagged content)","OpenTelemetry spans with evaluation metadata (pass/fail, violation type)"],"categories":["safety-moderation","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_7","uri":"capability://automation.workflow.multi.sdk.instrumentation.with.python.typescript.and.go.support","name":"multi-sdk instrumentation with python, typescript, and go support","description":"Provides language-specific SDKs (Python, TypeScript, Go) that implement the same instrumentation architecture and semantic conventions, enabling consistent telemetry collection across polyglot AI applications. Each SDK uses language-native instrumentation patterns (Python decorators/monkey-patching, TypeScript wrappers, Go middleware) to intercept LLM and framework calls. All SDKs export to the same OpenTelemetry backend, enabling unified observability across languages.","intents":["Instrument LLM applications written in Python, TypeScript, or Go with consistent telemetry","Correlate LLM calls across microservices written in different languages","Migrate observability infrastructure from language-specific tools to OpenTelemetry-native platform"],"best_for":["Organizations with polyglot AI applications (Python ML backends, TypeScript frontends, Go services)","Teams standardizing on OpenTelemetry for observability across multiple languages","Developers building language-agnostic AI infrastructure and platforms"],"limitations":["Feature parity across SDKs is not guaranteed; some features may be available only in specific languages","Go SDK has limited framework support compared to Python (no LangChain instrumentation)","TypeScript SDK requires Node.js 16+; browser-based JavaScript is not supported","Cross-language trace correlation requires explicit trace context propagation (W3C Trace Context headers)"],"requires":["Python 3.8+ (Python SDK), Node.js 16+ (TypeScript SDK), or Go 1.18+ (Go SDK)","OpenTelemetry Collector or OTLP endpoint for telemetry export","Language-specific package managers (pip, npm, go get) for SDK installation"],"input_types":["Language-specific LLM client libraries (openai, anthropic, etc.)","Framework objects (LangChain Chains, LangGraph StateGraphs, etc.)"],"output_types":["OpenTelemetry traces and metrics (consistent across all languages)","Unified telemetry in OpenTelemetry Collector or backend"],"categories":["automation-workflow","tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_8","uri":"capability://automation.workflow.kubernetes.operator.for.automated.instrumentation.and.deployment","name":"kubernetes operator for automated instrumentation and deployment","description":"Provides a Kubernetes Operator that automates deployment of OpenLIT components (collector, platform) and injects instrumentation into Kubernetes workloads via webhooks. The operator watches for Kubernetes resources (Deployments, StatefulSets) and automatically injects OpenLIT SDKs and OTLP exporter configuration into pod specifications. Enables zero-touch instrumentation of AI applications running in Kubernetes without modifying application code or Helm charts.","intents":["Deploy OpenLIT components (collector, platform) to Kubernetes clusters with a single operator","Automatically inject OpenLIT instrumentation into Kubernetes workloads without code changes","Manage OpenLIT configuration and updates across multiple Kubernetes clusters","Enable observability for AI applications in Kubernetes without modifying deployment manifests"],"best_for":["Organizations running AI applications on Kubernetes with infrastructure-as-code practices","DevOps teams automating observability deployment across multiple clusters","Teams requiring zero-touch instrumentation without modifying application code"],"limitations":["Operator requires cluster-admin permissions to install and manage webhooks","Webhook injection adds ~500ms to pod startup time due to mutation webhook processing","Operator only supports Kubernetes 1.19+; older versions are not supported","Custom resource definitions (CRDs) must be installed before deploying the operator"],"requires":["Kubernetes 1.19+ cluster with webhook support enabled","Helm 3+ for operator installation","Cluster-admin permissions to install the operator and webhooks","OpenLIT platform instance accessible from the Kubernetes cluster"],"input_types":["Kubernetes resources (Deployments, StatefulSets, DaemonSets)","OpenLIT operator configuration (CRDs, Helm values)"],"output_types":["Mutated Kubernetes pod specifications with OpenLIT SDK injection","OpenLIT platform and collector deployments in the cluster"],"categories":["automation-workflow","tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openlit__cap_9","uri":"capability://tool.use.integration.opentelemetry.backend.integration.with.grafana.new.relic.and.signoz","name":"opentelemetry backend integration with grafana, new relic, and signoz","description":"Exports telemetry (traces, metrics, logs) to any OpenTelemetry-compatible backend via OTLP protocol, including Grafana Cloud, New Relic, SigNoz, Datadog, and self-hosted OpenTelemetry Collector. Configures OTLP exporter in the SDK initialization to specify backend endpoint, authentication credentials, and export batching parameters. Enables teams to use existing observability infrastructure instead of requiring a separate OpenLIT platform deployment.","intents":["Export LLM telemetry to existing observability platforms (Grafana, New Relic, SigNoz)","Integrate OpenLIT instrumentation with existing monitoring and alerting infrastructure","Avoid vendor lock-in by using OpenTelemetry-compatible backends","Centralize AI and application observability in a single platform"],"best_for":["Organizations with existing OpenTelemetry-compatible observability infrastructure","Teams avoiding vendor lock-in by using open standards","Developers integrating AI observability with existing application monitoring"],"limitations":["OTLP export adds ~5-10ms latency per batch of telemetry due to network I/O","Backend-specific features (dashboards, alerts) require manual configuration in each backend","Large telemetry volumes may exceed backend ingestion limits; requires sampling or filtering","Authentication credentials must be managed securely; no built-in secret management"],"requires":["OpenTelemetry-compatible backend (Grafana Cloud, New Relic, SigNoz, etc.)","OTLP endpoint URL and authentication credentials (API key, token, etc.)","Network connectivity from the application to the backend endpoint"],"input_types":["OpenTelemetry traces (spans with attributes)","OpenTelemetry metrics (counters, gauges, histograms)","OpenTelemetry logs (structured log records)"],"output_types":["Telemetry exported to backend via OTLP protocol (gRPC or HTTP)","Backend-specific dashboards and alerts (configured separately)"],"categories":["tool-use-integration","automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ (SDK), Node.js 16+ (TypeScript SDK), or Go 1.18+ (Go SDK)","OpenTelemetry Collector or compatible OTLP endpoint (Grafana Cloud, New Relic, SigNoz, etc.)","LLM provider API keys (OpenAI, Anthropic, etc.) for the providers being instrumented","Python 3.8+ or TypeScript SDK with vector database client library installed","Vector database instance (Qdrant, Chroma, Pinecone, etc.) with network connectivity","OpenTelemetry Collector or OTLP endpoint for telemetry export","OpenTelemetry SDK and semantic conventions library","Understanding of OpenTelemetry concepts (spans, attributes, metrics)","OpenTelemetry SDK with trace context propagation support","HTTP/gRPC clients that support header injection (most modern clients do)"],"failure_modes":["Instrumentation overhead adds ~5-15ms per LLM call due to telemetry export","Streaming responses require buffering for token counting, increasing memory usage","Vision/audio model payloads (images, audio bytes) are not captured by default to avoid storage bloat","Local model instrumentation (Ollama, vLLM) requires manual endpoint configuration","Vector payloads (embeddings themselves) are not captured to avoid storage overhead","Batch retrieval operations are tracked as single spans, limiting granularity for large batches","Custom vector database implementations require manual instrumentation","Embedding model costs are estimated based on token counts, not actual provider billing","Semantic conventions are evolving; some AI operations may not have standardized conventions yet","Custom AI operations require manual mapping to semantic conventions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=openlit","compare_url":"https://unfragile.ai/compare?artifact=openlit"}},"signature":"ehxQL+oRo83+Bo+XQu07R8AQFJEbKwsdlNwg0TFFEO6VskQZKUknmon8iITjLx4vRPzqwgDp3MYskpWHdG4QDw==","signedAt":"2026-06-22T03:50:33.111Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openlit","artifact":"https://unfragile.ai/openlit","verify":"https://unfragile.ai/api/v1/verify?slug=openlit","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"}}