{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langfuse--langfuse","slug":"langfuse--langfuse","name":"langfuse","type":"repo","url":"https://langfuse.com/docs","page_url":"https://unfragile.ai/langfuse--langfuse","categories":["observability"],"tags":["analytics","autogen","evaluation","langchain","large-language-models","llama-index","llm","llm-evaluation","llm-observability","llmops","monitoring","observability","open-source","openai","playground","prompt-engineering","prompt-management","self-hosted","ycombinator"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langfuse--langfuse__cap_0","uri":"capability://data.processing.analysis.distributed.trace.capture.and.reconstruction.with.multi.sdk.integration","name":"distributed trace capture and reconstruction with multi-sdk integration","description":"Captures LLM interaction traces across heterogeneous SDKs (Langchain, LiteLLM, OpenAI SDK, LlamaIndex) via unified ingestion API endpoints that normalize events into a PostgreSQL-backed trace graph. Uses event enrichment and masking pipelines to standardize observations (LLM calls, retrievals, tool executions) into parent-child relationships, enabling full execution path reconstruction without modifying user application code.","intents":["I want to trace LLM calls across my Langchain + LiteLLM application without rewriting my code","I need to see the full execution graph of a multi-step agent interaction","I want to capture traces from multiple LLM providers in a single unified view"],"best_for":["Teams building multi-provider LLM applications with Langchain, LiteLLM, or OpenAI SDK","Developers debugging complex agent workflows with nested tool calls","Organizations requiring vendor-agnostic LLM observability"],"limitations":["Trace reconstruction depends on correct parent-child relationship tagging; missing trace IDs result in orphaned observations","Event enrichment adds ~50-100ms latency per ingestion batch","PostgreSQL schema requires careful indexing on trace_id and timestamp for sub-second query performance at scale (>1M traces/day)"],"requires":["PostgreSQL 12+ for trace storage","Python SDK (langfuse>=2.0) or TypeScript SDK (langfuse>=2.0) or REST API client","Network connectivity to Langfuse ingestion endpoints or self-hosted instance"],"input_types":["JSON trace events with trace_id, parent_observation_id, timestamps","LLM call metadata (model, tokens, latency)","Tool execution logs with input/output payloads"],"output_types":["Reconstructed trace graph (DAG structure)","Trace timeline view with nested observations","Exportable trace JSON with full execution context"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_1","uri":"capability://data.processing.analysis.opentelemetry.native.trace.ingestion.with.semantic.convention.mapping","name":"opentelemetry-native trace ingestion with semantic convention mapping","description":"Accepts OpenTelemetry Protocol (OTLP) traces via gRPC/HTTP endpoints and maps OTel semantic conventions (span attributes, events, status codes) to Langfuse trace domain model (observations, scores, metadata). Implements dual-write architecture to PostgreSQL and ClickHouse for real-time querying and historical analytics, with automatic schema validation and attribute masking for PII.","intents":["I want to send traces from my OpenTelemetry instrumentation directly to Langfuse","I need to correlate OTel traces with LLM-specific metrics like token counts and model names","I want to use standard OTel exporters without writing custom Langfuse SDK code"],"best_for":["Teams already using OpenTelemetry in their observability stack","Organizations with heterogeneous services (some LLM, some traditional) needing unified tracing","Developers building custom LLM frameworks wanting standards-based instrumentation"],"limitations":["OTel semantic conventions don't map 1:1 to LLM concepts (e.g., token_count is custom attribute, not standard)","Dual-write to PostgreSQL + ClickHouse requires eventual consistency handling; ClickHouse may lag by 5-30 seconds","Attribute masking rules must be pre-configured; dynamic masking based on span content not supported"],"requires":["OpenTelemetry SDK/API compatible with OTLP exporter (Python, Node.js, Go, Java, etc.)","gRPC or HTTP endpoint connectivity to Langfuse OTLP receiver","PostgreSQL 12+ and ClickHouse 21.8+ for dual-write architecture"],"input_types":["OTLP ExportTraceServiceRequest (protobuf or JSON)","OTel span attributes with semantic conventions","Custom attributes for LLM-specific metadata (model, tokens, temperature)"],"output_types":["Normalized Langfuse observations in PostgreSQL","Time-series analytics in ClickHouse for aggregations","Trace timeline with OTel span hierarchy preserved"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_10","uri":"capability://automation.workflow.batch.trace.operations.with.async.processing.and.error.recovery","name":"batch trace operations with async processing and error recovery","description":"Supports batch operations on multiple traces (export, delete, tag, score, assign to dataset) via async job queue with progress tracking and error recovery. Uses Redis-backed job queue for reliable processing with automatic retry logic and dead-letter queue for failed jobs. Implements batch selection UI with checkbox filtering and action confirmation, supporting 1k+ trace selections without UI blocking.","intents":["I want to export 5000 traces to a CSV file for external analysis","I need to delete old traces from my project to manage storage costs","I want to bulk-tag traces with a quality score or environment label"],"best_for":["Teams managing large trace datasets with bulk operations","Developers automating trace cleanup and archival workflows","Organizations exporting trace data for external analysis or compliance"],"limitations":["Batch operations are async; no real-time feedback on operation progress (updates every 5-10 seconds)","Large batch exports (>100k traces) may timeout or consume significant memory; recommend chunking into smaller batches","Failed batch operations are queued for retry but may eventually fail permanently; no automatic escalation or alerting","Batch selection is limited to current filtered view; no cross-view batch selection"],"requires":["Redis 6.0+ for job queue management","Worker process for async job execution","PostgreSQL 12+ for batch operation state storage"],"input_types":["Trace selection (trace IDs or filter criteria)","Batch operation type (export, delete, tag, score, assign)","Operation parameters (export format, tag name, score value)"],"output_types":["Batch job status (queued, processing, completed, failed)","Export file (CSV, JSON, JSONL) for export operations","Operation summary (traces processed, errors, duration)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_11","uri":"capability://automation.workflow.automated.data.retention.and.archival.with.configurable.policies","name":"automated data retention and archival with configurable policies","description":"Implements configurable data retention policies at project level, automatically archiving or deleting traces based on age, cost, or custom criteria. Uses background scheduled jobs to enforce retention policies without manual intervention. Supports tiered storage (hot PostgreSQL, cold ClickHouse, archive S3) with automatic data migration based on retention tier. Provides audit trail of deleted traces for compliance.","intents":["I want to automatically delete traces older than 90 days to manage storage costs","I need to archive expensive traces to cold storage while keeping recent traces in hot storage","I want to maintain an audit trail of deleted data for compliance purposes"],"best_for":["Organizations with large trace volumes and storage cost constraints","Teams with compliance requirements (GDPR, HIPAA) for data retention and deletion","Developers managing multi-environment deployments with different retention policies"],"limitations":["Retention policies are project-scoped; no cross-project or account-level policies","Archival to S3 requires manual setup and AWS credentials; no automatic cloud storage integration","Deleted traces cannot be recovered; no soft-delete or recovery window","Retention policy enforcement runs on fixed schedule (typically daily); no real-time enforcement"],"requires":["PostgreSQL 12+ for retention policy storage","Background job scheduler (cron or similar) for policy enforcement","Optional: AWS S3 or similar cloud storage for archival"],"input_types":["Retention policy configuration (retention period, archive tier, deletion criteria)","Trace metadata (created_at, cost, custom properties) for policy evaluation"],"output_types":["Archived traces in cold storage (S3, ClickHouse)","Audit trail of deleted traces with deletion timestamp and reason","Storage usage statistics (hot, cold, archived)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_12","uri":"capability://automation.workflow.real.time.trace.streaming.and.live.dashboard.updates","name":"real-time trace streaming and live dashboard updates","description":"Streams new traces to connected clients via WebSocket or Server-Sent Events (SSE), enabling live dashboard updates without polling. Implements efficient delta updates (only changed fields) to minimize bandwidth. Uses tRPC subscriptions for real-time updates with automatic reconnection and backpressure handling. Supports filtering live streams by project, trace status, or custom criteria.","intents":["I want to see new traces appear in my dashboard in real-time as they're captured","I need to monitor my LLM application's health with live metrics updates","I want to set up alerts that trigger immediately when traces with errors are captured"],"best_for":["Teams monitoring LLM application health in real-time","Developers debugging issues with live trace visibility","Organizations with SLA requirements needing immediate error detection"],"limitations":["WebSocket connections have memory overhead; supporting 1000+ concurrent connections requires significant server resources","Live streaming is limited to recent traces; historical trace queries still require full database scans","Delta updates require client-side state management; complex for large trace objects","Network latency may cause 1-5 second delays in live updates depending on connection quality"],"requires":["WebSocket or SSE support in web browser","tRPC server with subscription support","Network connectivity with low latency for real-time updates"],"input_types":["Stream filter criteria (project_id, trace status, custom properties)","Update frequency (real-time, batched every N seconds)"],"output_types":["New trace events with full metadata","Delta updates for changed trace fields","Aggregated metrics (traces per second, error rate, avg latency)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_2","uri":"capability://planning.reasoning.real.time.llm.as.judge.evaluation.with.configurable.scoring.rubrics","name":"real-time llm-as-judge evaluation with configurable scoring rubrics","description":"Executes automated evaluations on captured traces using LLM-as-Judge pattern via Redis-backed job queue (evalExecutionQueue, llmAsJudgeExecutionQueue). Supports configurable scoring rubrics with multi-step evaluation logic, integrates with OpenAI/Anthropic/custom LLM providers for judgment, and stores scores as observations linked to traces. Uses background worker processes to parallelize evaluation across multiple traces with configurable retry logic and error handling.","intents":["I want to automatically score LLM outputs against custom rubrics without manual review","I need to run evaluations on 10k+ traces in parallel without blocking my application","I want to use Claude or GPT-4 as a judge to evaluate my LLM's responses against criteria like helpfulness, accuracy, and tone"],"best_for":["Teams evaluating LLM application quality at scale (100+ traces/day)","Developers building feedback loops for prompt optimization","Organizations needing automated quality gates before production deployment"],"limitations":["LLM-as-Judge evaluations add cost (API calls to OpenAI/Anthropic); no built-in cost estimation or budgeting","Evaluation latency depends on LLM provider response time (typically 2-10 seconds per trace)","Rubric configuration is manual; no automatic rubric generation or optimization","Scores are stored as observations; querying aggregated scores across 1M+ traces requires ClickHouse analytics queries"],"requires":["Redis 6.0+ for job queue management","Worker process running (Node.js/TypeScript) with access to Redis and database","API key for LLM provider (OpenAI, Anthropic, or custom endpoint)","Evaluation job configuration with rubric definition and model selection"],"input_types":["Trace observations (LLM outputs, inputs, metadata)","Evaluation rubric JSON with scoring criteria","LLM provider configuration (model, API key, temperature)"],"output_types":["Numeric scores (0-1 or custom range) linked to observations","Evaluation reasoning/explanation from LLM judge","Aggregated score statistics per trace, session, or time window"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_3","uri":"capability://safety.moderation.multi.tenant.rbac.with.api.key.and.sso.authentication","name":"multi-tenant rbac with api key and sso authentication","description":"Implements multi-tenant isolation via project-scoped API keys and role-based access control (RBAC) with configurable permissions per user role. Supports SSO integration (OIDC, SAML) for enterprise deployments and API key management with automatic rotation and scoping. Uses tRPC internal API with authentication middleware and PostgreSQL-backed permission checks to enforce access control across all endpoints.","intents":["I want to give my team members access to specific projects without exposing all data","I need to integrate Langfuse with our company's SSO provider (Okta, Auth0, etc.)","I want to rotate API keys and revoke access without redeploying my application"],"best_for":["Enterprise teams with multiple projects and users requiring fine-grained access control","Organizations with SSO/SAML requirements for compliance (SOC2, ISO27001)","Teams managing multiple LLM applications with different access levels per team"],"limitations":["RBAC is project-scoped; no cross-project role inheritance or hierarchical permissions","SSO configuration requires manual setup per identity provider; no auto-discovery","API key rotation requires manual intervention; no automatic rotation policies","Permission checks add ~5-10ms latency per API request due to database lookups"],"requires":["PostgreSQL 12+ for user and permission storage","OIDC/SAML identity provider for SSO (optional but recommended for enterprise)","API key management UI or REST API for key generation and rotation"],"input_types":["User credentials (email/password or SSO token)","API key with project scope","Role assignment (admin, member, viewer, etc.)"],"output_types":["JWT or session token for authenticated requests","API key with project scope and permissions","User profile with assigned roles and permissions"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_4","uri":"capability://memory.knowledge.prompt.versioning.and.a.b.testing.with.experiment.tracking","name":"prompt versioning and a/b testing with experiment tracking","description":"Stores prompt templates with version control, enabling side-by-side comparison of prompt variants via experiment framework. Integrates with trace capture to automatically tag observations with prompt version and experiment ID, enabling statistical analysis of prompt performance. Uses PostgreSQL for prompt storage and ClickHouse for aggregated experiment metrics (success rate, latency, cost per variant).","intents":["I want to test two different prompts and see which one produces better outputs","I need to version my prompts and roll back to a previous version if a new one performs worse","I want to run A/B tests on prompt variations and get statistical significance results"],"best_for":["Teams iterating on prompt engineering with data-driven optimization","Developers managing multiple prompt versions across environments (dev, staging, prod)","Organizations running continuous prompt experimentation pipelines"],"limitations":["Experiment statistical significance requires minimum sample size (typically 100+ observations per variant); small experiments may show false positives","Prompt versioning is manual; no automatic version creation or rollback based on performance thresholds","A/B test results are available only after traces are captured and evaluated; no real-time experiment dashboards","Prompt templates are stored as text; no built-in prompt syntax validation or linting"],"requires":["PostgreSQL 12+ for prompt version storage","ClickHouse 21.8+ for experiment metrics aggregation","Trace capture integration to tag observations with prompt version and experiment ID"],"input_types":["Prompt template text (string)","Experiment configuration (variant names, traffic split, duration)","Trace observations with prompt version tag"],"output_types":["Prompt version history with metadata (created_at, created_by, description)","Experiment results with per-variant metrics (success rate, latency, cost)","Statistical significance test results (p-value, confidence interval)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_5","uri":"capability://text.generation.language.interactive.llm.playground.with.multi.provider.model.selection","name":"interactive llm playground with multi-provider model selection","description":"Web-based playground for testing LLM calls with live model switching across OpenAI, Anthropic, Ollama, and custom endpoints. Supports prompt templating with variable substitution, message history management, and parameter tuning (temperature, top_p, max_tokens). Captures all playground interactions as traces for debugging and evaluation, with side-by-side model comparison and response streaming.","intents":["I want to quickly test a prompt against multiple LLM models without writing code","I need to compare Claude and GPT-4 responses to the same prompt in real-time","I want to debug why my LLM is producing unexpected outputs by testing variations interactively"],"best_for":["Prompt engineers and non-technical team members testing LLM behavior","Developers debugging LLM application issues without local setup","Teams evaluating different LLM providers for cost and quality trade-offs"],"limitations":["Playground is browser-based; large responses (>100k tokens) may cause UI lag or memory issues","Model switching requires API key configuration per provider; no automatic provider selection based on cost or latency","Prompt templating supports basic variable substitution only; no complex logic or conditional rendering","Playground interactions are captured as traces but not automatically linked to experiments or datasets"],"requires":["Web browser with JavaScript support (Chrome, Firefox, Safari, Edge)","API keys for LLM providers (OpenAI, Anthropic, etc.) or self-hosted Ollama instance","Network connectivity to Langfuse web application and LLM provider endpoints"],"input_types":["Prompt text with variable placeholders (e.g., {{variable_name}})","LLM parameters (model, temperature, top_p, max_tokens)","Message history (system, user, assistant messages)"],"output_types":["LLM response text with streaming support","Token usage statistics (input_tokens, output_tokens, total_cost)","Captured trace with full interaction metadata","Side-by-side comparison of responses from multiple models"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_6","uri":"capability://data.processing.analysis.dataset.management.with.annotation.queues.and.human.in.the.loop.labeling","name":"dataset management with annotation queues and human-in-the-loop labeling","description":"Manages datasets of LLM inputs/outputs with annotation queue system for human review and labeling. Supports batch creation from captured traces, manual annotation workflows with configurable label schemas, and export to training/evaluation formats. Uses PostgreSQL for dataset storage and annotation state management, with optional LLM-assisted annotation suggestions via LLM-as-Judge pattern.","intents":["I want to create a dataset from my production traces for fine-tuning or evaluation","I need my team to manually review and label 1000 LLM outputs with quality scores","I want to export labeled data in a format compatible with my training pipeline"],"best_for":["Teams building training datasets from production LLM interactions","Organizations with human annotation workflows for data labeling","Developers creating evaluation datasets for model comparison"],"limitations":["Annotation queue is sequential; no parallel annotation workflows or conflict resolution for overlapping reviews","Label schemas are manually defined; no automatic schema inference from annotation patterns","Export formats are limited to JSON and CSV; no direct integration with training frameworks (Hugging Face, PyTorch)","Annotation state is stored in PostgreSQL; no built-in audit trail or version history of label changes"],"requires":["PostgreSQL 12+ for dataset and annotation storage","User accounts with annotation permissions for team members","Optional: LLM API key for LLM-assisted annotation suggestions"],"input_types":["Trace observations (LLM inputs, outputs, metadata)","Label schema definition (label names, types, required/optional)","Annotation instructions (markdown or plain text)"],"output_types":["Annotated dataset with labels and metadata","Export formats: JSON, CSV, JSONL","Annotation statistics (coverage, inter-annotator agreement, label distribution)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_7","uri":"capability://memory.knowledge.session.and.conversation.tracking.with.multi.turn.context.preservation","name":"session and conversation tracking with multi-turn context preservation","description":"Groups related traces into sessions representing multi-turn conversations or user interactions. Automatically links observations across turns using session ID, preserving conversation context for debugging and analysis. Supports session-level metrics (total cost, latency, user satisfaction) and filtering by session properties (user_id, environment, model). Uses PostgreSQL for session storage and ClickHouse for session-level aggregations.","intents":["I want to see the full conversation history for a user who reported an issue","I need to analyze how my LLM performs across multi-turn conversations vs single-turn interactions","I want to track session-level metrics like total cost and user satisfaction across all conversations"],"best_for":["Teams building conversational AI applications (chatbots, assistants)","Developers debugging user-reported issues requiring full conversation context","Organizations analyzing conversation quality and user satisfaction metrics"],"limitations":["Sessions are manually created via session_id parameter; no automatic session detection based on user ID or conversation patterns","Session context is limited to linked observations; no built-in conversation summarization or key point extraction","Session-level metrics require ClickHouse queries; real-time session dashboards may lag by 5-30 seconds","No built-in session timeout or automatic session closure; sessions remain open indefinitely unless manually closed"],"requires":["PostgreSQL 12+ for session storage","ClickHouse 21.8+ for session-level aggregations","Session ID generation and management in application code"],"input_types":["Session ID (string, typically UUID or user-provided identifier)","Session metadata (user_id, environment, model, custom properties)","Trace observations linked to session"],"output_types":["Session timeline with all linked observations","Session-level metrics (total cost, latency, turn count, user satisfaction)","Session comparison views (side-by-side conversation analysis)","Aggregated session statistics per user, environment, or time window"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_8","uri":"capability://data.processing.analysis.cost.tracking.and.token.usage.analytics.with.multi.provider.pricing.models","name":"cost tracking and token usage analytics with multi-provider pricing models","description":"Automatically calculates and aggregates LLM costs based on token usage and configurable pricing models for OpenAI, Anthropic, and other providers. Stores token counts (input, output, total) per observation and aggregates costs at trace, session, and project levels. Uses ClickHouse for time-series cost analytics and cost trend analysis. Supports custom pricing models for fine-tuned models or enterprise pricing agreements.","intents":["I want to understand how much my LLM application costs per user interaction","I need to track cost trends over time and identify cost optimization opportunities","I want to set cost budgets and alerts for my LLM application"],"best_for":["Teams managing LLM application costs and optimizing for cost-efficiency","Organizations with cost allocation requirements across projects or teams","Developers evaluating different LLM providers based on cost-quality trade-offs"],"limitations":["Pricing models are static; no automatic updates when provider pricing changes","Custom pricing models require manual configuration; no automatic pricing discovery from provider APIs","Cost calculations are based on token counts only; no support for other cost factors (API calls, storage, compute)","Cost alerts require manual setup; no automatic cost anomaly detection or budget enforcement"],"requires":["Token count data in captured observations (typically provided by LLM SDK)","Pricing model configuration for each LLM provider","ClickHouse 21.8+ for cost analytics and aggregations"],"input_types":["Token counts (input_tokens, output_tokens) per observation","Model name and provider (e.g., gpt-4, claude-3-opus)","Pricing model configuration (cost per 1k input tokens, cost per 1k output tokens)"],"output_types":["Cost per observation, trace, session, and project","Cost trends over time (daily, weekly, monthly aggregations)","Cost breakdown by model, provider, and user","Cost comparison across different models or time periods"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langfuse--langfuse__cap_9","uri":"capability://search.retrieval.filtered.trace.search.and.analytics.with.custom.view.creation","name":"filtered trace search and analytics with custom view creation","description":"Provides advanced filtering and search across captured traces using PostgreSQL full-text search and ClickHouse analytics queries. Supports complex filter combinations (trace status, model, cost range, latency, user properties) with saved views for reusable filter sets. Implements virtualized table rendering for efficient display of 10k+ traces with sorting, pagination, and batch actions. Uses tRPC internal API for filter execution and ClickHouse for aggregated analytics (histograms, percentiles, distributions).","intents":["I want to find all traces where my LLM failed or returned low-quality outputs","I need to analyze latency distribution across different models and identify performance bottlenecks","I want to create a saved view that shows only traces from a specific user or environment"],"best_for":["Teams debugging LLM application issues using trace filtering and search","Developers analyzing performance metrics and identifying optimization opportunities","Organizations creating custom dashboards and reports from trace data"],"limitations":["Full-text search is limited to text fields; no semantic search or similarity-based filtering","Complex filter combinations may result in slow queries on large datasets (>10M traces); requires careful index tuning","Virtualized table rendering has memory limits; filtering 100k+ traces may cause browser lag","Saved views are user-specific; no sharing of views across team members or projects"],"requires":["PostgreSQL 12+ with full-text search enabled","ClickHouse 21.8+ for analytics queries","Web browser with JavaScript support for virtualized table rendering"],"input_types":["Filter criteria (trace status, model, cost range, latency, user properties, custom metadata)","Sort order (timestamp, cost, latency, etc.)","View configuration (name, filters, columns to display)"],"output_types":["Filtered trace list with pagination and sorting","Aggregated analytics (count, sum, avg, percentiles, distributions)","Saved view configuration for reuse","Batch action results (export, delete, tag, score)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12+ for trace storage","Python SDK (langfuse>=2.0) or TypeScript SDK (langfuse>=2.0) or REST API client","Network connectivity to Langfuse ingestion endpoints or self-hosted instance","OpenTelemetry SDK/API compatible with OTLP exporter (Python, Node.js, Go, Java, etc.)","gRPC or HTTP endpoint connectivity to Langfuse OTLP receiver","PostgreSQL 12+ and ClickHouse 21.8+ for dual-write architecture","Redis 6.0+ for job queue management","Worker process for async job execution","PostgreSQL 12+ for batch operation state storage","PostgreSQL 12+ for retention policy storage"],"failure_modes":["Trace reconstruction depends on correct parent-child relationship tagging; missing trace IDs result in orphaned observations","Event enrichment adds ~50-100ms latency per ingestion batch","PostgreSQL schema requires careful indexing on trace_id and timestamp for sub-second query performance at scale (>1M traces/day)","OTel semantic conventions don't map 1:1 to LLM concepts (e.g., token_count is custom attribute, not standard)","Dual-write to PostgreSQL + ClickHouse requires eventual consistency handling; ClickHouse may lag by 5-30 seconds","Attribute masking rules must be pre-configured; dynamic masking based on span content not supported","Batch operations are async; no real-time feedback on operation progress (updates every 5-10 seconds)","Large batch exports (>100k traces) may timeout or consume significant memory; recommend chunking into smaller batches","Failed batch operations are queued for retry but may eventually fail permanently; no automatic escalation or alerting","Batch selection is limited to current filtered view; no cross-view batch selection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7618804027734736,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:24.501Z","last_commit":"2026-05-02T02:27:56Z"},"community":{"stars":26502,"forks":2684,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langfuse--langfuse","compare_url":"https://unfragile.ai/compare?artifact=langfuse--langfuse"}},"signature":"t8ud4mc+peJbKlK58FjquuFdUUHu/p/QMIsNIfFDK460h872ot0oa0UT39Ua+YHh1k7Q01txbUJ1OJLSTd7VAw==","signedAt":"2026-06-21T10:27:17.386Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langfuse--langfuse","artifact":"https://unfragile.ai/langfuse--langfuse","verify":"https://unfragile.ai/api/v1/verify?slug=langfuse--langfuse","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"}}