{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-mlflow-anthropic","slug":"mlflow-anthropic","name":"mlflow-anthropic","type":"framework","url":"https://mlflow.org/","page_url":"https://unfragile.ai/mlflow-anthropic","categories":["model-training"],"tags":["mlflow","tracing","observability","opentelemetry","llm","anthropic","claude","javascript","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-mlflow-anthropic__cap_0","uri":"capability://automation.workflow.anthropic.claude.api.call.tracing.with.opentelemetry.instrumentation","name":"anthropic claude api call tracing with opentelemetry instrumentation","description":"Automatically captures and instruments Anthropic Claude API calls using OpenTelemetry standards, creating structured trace spans that record request/response payloads, token counts, latency, and model metadata. Integrates with the Anthropic JavaScript SDK through wrapper instrumentation that intercepts API calls before they reach the network layer, extracting call context and embedding trace IDs into request headers for distributed tracing correlation.","intents":["I need to trace every Claude API call my application makes to understand latency and token usage patterns","I want to correlate Claude API calls with upstream application events in a unified observability platform","I need to debug why a specific Claude interaction failed by reviewing the exact request/response that was sent"],"best_for":["TypeScript/JavaScript teams building LLM applications with Claude and using MLflow for experiment tracking","AI engineers debugging multi-step agentic workflows that call Claude multiple times","Teams migrating from ad-hoc logging to structured OpenTelemetry-based observability"],"limitations":["Only instruments Anthropic JavaScript SDK — no Python support for Claude tracing","Requires MLflow server running separately; no embedded/local-only tracing option","Trace data is sent to MLflow backend synchronously, which can add latency to Claude API calls if MLflow is slow or unreachable","Does not capture streaming response chunks individually — only final aggregated response is traced"],"requires":["Node.js 14+ with npm or yarn","Anthropic JavaScript SDK (latest version compatible with instrumentation hooks)","MLflow 2.0+ with OpenTelemetry collector configured","Valid Anthropic API key in environment or passed to SDK","Network access to MLflow tracking server"],"input_types":["Anthropic SDK client configuration","Claude API request parameters (messages, model, temperature, etc.)","MLflow tracking URI and credentials"],"output_types":["OpenTelemetry trace spans (JSON/protobuf format)","MLflow trace artifacts with request/response payloads","Structured metadata (token counts, latency, model name, API version)"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mlflow-anthropic__cap_1","uri":"capability://memory.knowledge.mlflow.trace.artifact.storage.and.retrieval.for.claude.interactions","name":"mlflow trace artifact storage and retrieval for claude interactions","description":"Persists complete Claude API request/response payloads and metadata as MLflow trace artifacts, enabling historical replay, audit trails, and retrieval of past interactions. Uses MLflow's artifact store abstraction (local filesystem, S3, GCS, etc.) to durably store trace data keyed by trace ID, with automatic indexing for querying by timestamp, model, or token usage. Provides APIs to fetch and reconstruct full conversation context from stored traces.","intents":["I need to audit what prompts were sent to Claude and what responses were received for compliance or debugging","I want to replay a past Claude interaction to understand why it produced a particular output","I need to analyze patterns in Claude API usage across my application (token spend, latency, error rates) over time"],"best_for":["Teams with compliance or audit requirements for LLM interactions","AI engineers analyzing Claude behavior across thousands of API calls","Developers building RAG or agentic systems who need to debug multi-turn conversations"],"limitations":["Artifact storage latency depends on configured backend (S3 can add 100-500ms per write)","No built-in data retention policies — requires manual cleanup or external lifecycle management","Trace artifacts are immutable once written; no editing or redaction of stored payloads","Querying traces requires MLflow API calls; no direct SQL or full-text search over stored payloads"],"requires":["MLflow 2.0+ with artifact store configured (local, S3, GCS, Azure Blob, etc.)","Write permissions to configured artifact store","MLflow tracking server running and accessible","Sufficient storage capacity for trace artifacts (estimate ~1-5KB per Claude API call)"],"input_types":["Claude API request objects (messages, model, parameters)","Claude API response objects (content, usage, stop_reason)","Trace metadata (timestamp, user ID, session ID, etc.)"],"output_types":["Stored trace artifacts (JSON files in artifact store)","MLflow trace records with artifact references","Retrieved trace data for replay or analysis"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mlflow-anthropic__cap_2","uri":"capability://automation.workflow.distributed.trace.correlation.across.multi.step.llm.workflows","name":"distributed trace correlation across multi-step llm workflows","description":"Propagates trace context (trace ID, span ID) across multiple Claude API calls and upstream application code using OpenTelemetry context propagation standards (W3C Trace Context headers). Automatically links Claude API spans as children of parent application spans, creating a unified trace tree that shows the full execution path from initial user request through multiple Claude interactions and downstream processing. Supports both synchronous and asynchronous context propagation.","intents":["I need to see how a user request flows through my application, including all Claude API calls it triggers, in a single trace view","I want to measure end-to-end latency for a multi-step workflow that calls Claude multiple times and correlate it with individual Claude latencies","I need to debug a complex agentic workflow where Claude calls trigger additional Claude calls or external tools"],"best_for":["Teams building agentic systems with multiple Claude API calls per user request","Organizations using distributed tracing infrastructure (Jaeger, Datadog, New Relic) alongside MLflow","Developers debugging latency issues in multi-step LLM pipelines"],"limitations":["Context propagation requires explicit instrumentation of application code — not automatic for all async patterns","Async context propagation in JavaScript can be fragile with certain patterns (e.g., Promise.all with untracked promises)","Trace context is lost if application code doesn't explicitly pass context to Claude SDK calls","No automatic context propagation across service boundaries — requires manual header injection for microservices"],"requires":["OpenTelemetry API and SDK for JavaScript installed","Application code instrumented with OpenTelemetry context management","MLflow configured with OpenTelemetry exporter","Anthropic SDK version with context propagation support"],"input_types":["Parent span context (trace ID, span ID, trace flags)","Claude API call parameters","Application execution context"],"output_types":["Linked trace spans with parent-child relationships","Unified trace tree in MLflow UI","Trace context headers in Claude API requests"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mlflow-anthropic__cap_3","uri":"capability://data.processing.analysis.token.usage.and.cost.tracking.for.claude.api.calls","name":"token usage and cost tracking for claude api calls","description":"Automatically extracts token count data from Claude API responses (input tokens, output tokens, cache read/write tokens) and stores them as span attributes in MLflow traces. Provides aggregation APIs to calculate total token usage and estimated costs across multiple Claude calls, filtered by model, time range, or user. Integrates with MLflow's metrics system to enable cost-based experiment comparison and budget monitoring.","intents":["I need to track how many tokens my application is using with Claude to understand API costs","I want to compare the token efficiency of different prompts or model versions in my experiments","I need to set up alerts when token usage exceeds a budget threshold"],"best_for":["Teams with cost-conscious LLM deployments who need to track spending per feature or user","AI engineers optimizing prompts to reduce token usage and API costs","Organizations running multiple Claude models and needing cost attribution per model"],"limitations":["Token counts are extracted from Claude API responses — no pre-call estimation of token usage","Cost calculation requires manual configuration of Claude pricing (prices change with new models)","No built-in budget enforcement — only tracking and alerting, not rate limiting","Cache token tracking is only available for Claude 3.5+ models with prompt caching enabled"],"requires":["Anthropic SDK that returns token usage in API responses","MLflow 2.0+ with metrics support","Configuration of Claude pricing data (input/output token rates per model)"],"input_types":["Claude API response objects with usage metadata","Model name and pricing configuration","Time range or filter criteria for aggregation"],"output_types":["Token count metrics (input, output, cache tokens) as MLflow span attributes","Aggregated cost reports (total tokens, estimated cost, cost per call)","Time-series metrics for cost trending"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mlflow-anthropic__cap_4","uri":"capability://safety.moderation.error.and.failure.tracking.for.claude.api.interactions","name":"error and failure tracking for claude api interactions","description":"Captures and records Claude API errors (rate limits, authentication failures, model unavailability, invalid requests) as span events in MLflow traces, including error type, message, and retry metadata. Automatically detects transient vs. permanent failures and tracks retry attempts. Provides error aggregation and analysis APIs to identify common failure patterns and correlate them with request characteristics (model, prompt length, parameters).","intents":["I need to understand why Claude API calls are failing and identify patterns in failures","I want to track how often rate limiting occurs and adjust my retry strategy accordingly","I need to correlate API failures with specific prompt characteristics to identify problematic inputs"],"best_for":["Teams running production LLM applications who need to monitor API reliability","Developers debugging intermittent Claude API failures in complex workflows","Operations teams setting up alerts for API error rates or specific failure types"],"limitations":["Error tracking is only as good as the Anthropic SDK's error reporting — some network-level failures may not be captured","No automatic retry logic — only tracking of retries if application code implements them","Error messages from Claude API are opaque and may not contain actionable debugging information","Rate limit detection requires parsing error messages, which may change across SDK versions"],"requires":["Anthropic SDK with error handling that propagates exceptions","MLflow 2.0+ with span event support","Application code that catches and logs Claude API exceptions"],"input_types":["Claude API exception objects","HTTP status codes and error responses","Request parameters that triggered the error"],"output_types":["MLflow span events with error details","Error aggregation reports (error type, frequency, affected requests)","Correlation analysis (errors vs. model, prompt length, parameters)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mlflow-anthropic__cap_5","uri":"capability://automation.workflow.real.time.trace.streaming.and.live.monitoring.dashboard","name":"real-time trace streaming and live monitoring dashboard","description":"Streams Claude API traces to MLflow in near-real-time as they complete, enabling live monitoring of API calls without waiting for batch aggregation. Provides MLflow UI integration to display live trace feeds, showing request/response payloads, latency, and token usage as they occur. Supports filtering and searching live traces by model, user, or error status.","intents":["I want to monitor Claude API calls happening right now in my production application","I need to quickly spot anomalies or failures in real-time Claude interactions","I want to see live token usage and latency metrics as my application runs"],"best_for":["Operations teams monitoring production LLM applications","Developers debugging live issues in Claude-powered features","Teams with high-volume Claude API usage who need real-time visibility"],"limitations":["Real-time streaming adds network overhead — each Claude API call triggers a network write to MLflow","MLflow UI may lag or become slow if trace volume is very high (1000+ traces/second)","Live traces are only retained in MLflow for a configurable window (default 24 hours) before archival","Streaming is best-effort — if MLflow is unavailable, traces are buffered but may be lost on application restart"],"requires":["MLflow 2.0+ with trace streaming support","Network connectivity from application to MLflow server (low-latency preferred)","MLflow UI access for live monitoring"],"input_types":["Claude API request/response data","Trace metadata (timestamp, user, session)"],"output_types":["Real-time trace events streamed to MLflow","Live trace feed in MLflow UI","Searchable trace index"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ with npm or yarn","Anthropic JavaScript SDK (latest version compatible with instrumentation hooks)","MLflow 2.0+ with OpenTelemetry collector configured","Valid Anthropic API key in environment or passed to SDK","Network access to MLflow tracking server","MLflow 2.0+ with artifact store configured (local, S3, GCS, Azure Blob, etc.)","Write permissions to configured artifact store","MLflow tracking server running and accessible","Sufficient storage capacity for trace artifacts (estimate ~1-5KB per Claude API call)","OpenTelemetry API and SDK for JavaScript installed"],"failure_modes":["Only instruments Anthropic JavaScript SDK — no Python support for Claude tracing","Requires MLflow server running separately; no embedded/local-only tracing option","Trace data is sent to MLflow backend synchronously, which can add latency to Claude API calls if MLflow is slow or unreachable","Does not capture streaming response chunks individually — only final aggregated response is traced","Artifact storage latency depends on configured backend (S3 can add 100-500ms per write)","No built-in data retention policies — requires manual cleanup or external lifecycle management","Trace artifacts are immutable once written; no editing or redaction of stored payloads","Querying traces requires MLflow API calls; no direct SQL or full-text search over stored payloads","Context propagation requires explicit instrumentation of application code — not automatic for all async patterns","Async context propagation in JavaScript can be fragile with certain patterns (e.g., Promise.all with untracked promises)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.0414973347970818,"quality":0.22,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":130,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mlflow-anthropic","compare_url":"https://unfragile.ai/compare?artifact=mlflow-anthropic"}},"signature":"kuAd2faKMx8axMjcISXJJPUVbk3PnZ7/Xml4TBGLskIKDXkmiEjBPs3utSI8xX9XlKrZ/9qaDctdWLK8GXiZAw==","signedAt":"2026-06-22T07:02:15.779Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mlflow-anthropic","artifact":"https://unfragile.ai/mlflow-anthropic","verify":"https://unfragile.ai/api/v1/verify?slug=mlflow-anthropic","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"}}