{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-trulens-eval","slug":"pypi-trulens-eval","name":"trulens-eval","type":"repo","url":"https://trulens.org/","page_url":"https://unfragile.ai/pypi-trulens-eval","categories":["observability"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-trulens-eval__cap_0","uri":"capability://automation.workflow.opentelemetry.based.application.instrumentation.with.decorator.driven.span.generation","name":"opentelemetry-based application instrumentation with decorator-driven span generation","description":"Wraps LLM application methods using the @instrument decorator to automatically generate structured OpenTelemetry spans (RECORD_ROOT, GENERATION, RETRIEVAL, EVAL) without modifying core application logic. The decorator integrates with a TracerProvider that captures execution context, method inputs/outputs, and timing metadata, then exports spans to configured backends (SQLite, PostgreSQL, Snowflake). This enables zero-friction observability for framework-agnostic applications.","intents":["I want to trace execution flow through my LLM app without rewriting code","I need to capture structured spans for retrieval, generation, and evaluation steps automatically","I want to export traces to my existing observability infrastructure (Snowflake, PostgreSQL)"],"best_for":["Teams building LLM applications who want production observability without instrumentation overhead","Developers integrating observability into existing LangChain, LangGraph, or custom Python applications","Organizations standardizing on OpenTelemetry for multi-system tracing"],"limitations":["Decorator-based approach requires application code to import and use @instrument — cannot instrument third-party libraries without wrapper classes","Span export latency depends on database backend; Snowflake exports may batch asynchronously, delaying visibility","No built-in sampling or filtering at instrumentation time — all decorated methods generate spans, requiring downstream filtering for high-volume applications"],"requires":["Python 3.9+","OpenTelemetry Python SDK (trulens-core dependency)","Database backend: SQLite (default), PostgreSQL, MySQL, or Snowflake account with event table permissions","For framework-specific instrumentation: LangChain, LangGraph, LlamaIndex, or custom app wrapper class"],"input_types":["Python method signatures with arbitrary argument types","Execution context (caller, timestamp, inputs)"],"output_types":["OpenTelemetry Span objects with attributes (span_kind, status, events)","Serialized span records in database (SQLAlchemy ORM or Snowflake event tables)"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_1","uri":"capability://data.processing.analysis.llm.based.feedback.function.evaluation.with.multi.provider.support","name":"llm-based feedback function evaluation with multi-provider support","description":"Computes evaluation metrics (groundedness, relevance, coherence, custom metrics) by executing feedback functions that call LLM APIs with structured prompts. The Feedback class defines metric logic; LLMProvider interface abstracts over OpenAI, Bedrock, Cortex, HuggingFace, and LiteLLM endpoints. Evaluation runs asynchronously via a background Evaluator thread, storing results linked to application spans. Supports both synchronous (blocking) and deferred (async) evaluation modes.","intents":["I want to compute LLM-based quality metrics (groundedness, relevance) on my application outputs","I need to evaluate multiple LLM providers (OpenAI, Bedrock, local models) without rewriting feedback logic","I want to run evaluations asynchronously without blocking my application's critical path"],"best_for":["Teams evaluating LLM application quality with automated metrics","Multi-cloud deployments using different LLM providers (AWS Bedrock, OpenAI, Snowflake Cortex)","Batch evaluation workflows where deferred evaluation reduces latency impact"],"limitations":["Feedback function execution cost scales with number of spans and metrics — each metric invokes an LLM API call, adding ~$0.001-0.01 per evaluation","Deferred evaluation introduces eventual consistency — metrics not immediately available after application run completes","Custom feedback functions require Python code; no declarative metric definition language","LLM provider latency (typically 1-5s per call) can become bottleneck for high-volume evaluation; no built-in batching across spans"],"requires":["Python 3.9+","API credentials for at least one LLM provider: OpenAI API key, AWS Bedrock access, Snowflake Cortex credentials, or HuggingFace token","trulens-core with Feedback class and LLMProvider implementations","For async evaluation: background Evaluator thread (managed by TruSession)"],"input_types":["Feedback function definition (Python callable)","Span data (inputs, outputs, metadata from instrumented application)","LLM provider configuration (API endpoint, model name, credentials)"],"output_types":["Numeric metric scores (0.0-1.0 range typical)","Structured feedback records linked to spans (stored in database)","Cost tracking data (tokens consumed, API calls made)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_10","uri":"capability://data.processing.analysis.snowflake.event.table.export.and.server.side.evaluation.pipeline","name":"snowflake event table export and server-side evaluation pipeline","description":"Exports OTEL spans directly to Snowflake event tables for server-side querying and analysis. SnowflakeEventTableDB connector implements DBConnector interface, batching span exports asynchronously. Enables server-side evaluation pipeline where feedback functions execute in Snowflake Cortex (LLM provider) rather than client-side, reducing data transfer and enabling SQL-based metric computation. Integrates with Snowflake's native OTEL support.","intents":["I want to store application traces in Snowflake for SQL-based analysis","I need to run evaluations server-side in Snowflake Cortex without exporting data","I want to query spans and metrics using Snowflake SQL"],"best_for":["Organizations with Snowflake data warehouses who want integrated observability","Teams evaluating LLM applications at scale where server-side processing is more efficient","Data-driven teams needing SQL-based analytics on trace data"],"limitations":["Snowflake event table export is asynchronous and batched — traces not immediately queryable after export (typical latency 30-60s)","Server-side evaluation in Cortex limited to Snowflake-supported models and functions — less flexibility than client-side evaluation","Requires Snowflake account with event table permissions and Cortex access — not available in all Snowflake editions","Event table schema is fixed by Snowflake OTEL standard — custom span attributes may not be queryable"],"requires":["Python 3.9+","Snowflake account with event table permissions","Snowflake Cortex access for server-side evaluation","snowflake-connector-python and appropriate IAM role","trulens-connectors-snowflake package"],"input_types":["OTEL spans from instrumented application","Snowflake connection parameters (account, warehouse, database)","Evaluation function definitions"],"output_types":["Spans exported to Snowflake event tables","Evaluation results in Snowflake tables","SQL query results from span/metric analysis"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_11","uri":"capability://automation.workflow.run.management.and.external.agent.integration.for.distributed.evaluation","name":"run management and external agent integration for distributed evaluation","description":"RunManager class orchestrates application runs, tracking run metadata (ID, timestamp, app name, version), linking spans and metrics to runs, and managing run lifecycle. Supports external agent integration for distributed evaluation — agents can retrieve pending runs, execute feedback functions, and report results back to central database. Enables horizontal scaling of evaluation workload across multiple workers.","intents":["I want to track and manage multiple application runs with consistent metadata","I need to distribute evaluation workload across multiple machines or processes","I want to monitor evaluation progress and handle failures in distributed setup"],"best_for":["Large-scale evaluation deployments with high volume of runs","Distributed teams where evaluation agents run on different machines","Batch evaluation workflows requiring fault tolerance and progress tracking"],"limitations":["RunManager is single-threaded per session — no built-in parallelization within a process","External agent integration requires custom agent implementation — no reference agents provided","No built-in failure recovery or retry logic — agents must handle failures and report status","Run metadata schema is fixed — no custom fields for application-specific run properties"],"requires":["Python 3.9+","TruSession with database backend","For external agents: custom agent implementation with database access","Network connectivity between agents and central database"],"input_types":["Application run inputs and outputs","Run metadata (app name, version, timestamp)","Feedback functions to execute"],"output_types":["Run records in database with metadata","Span and metric records linked to runs","Agent status and progress logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_12","uri":"capability://tool.use.integration.backwards.compatibility.layer.for.trulens.eval.1.0.0.api.migration","name":"backwards compatibility layer for trulens_eval<1.0.0 api migration","description":"This package (trulens-eval) provides backwards-compatible API for applications built against trulens_eval<1.0.0, mapping old API calls to new trulens-core>=1.0.0 implementations. Enables existing applications to upgrade without code changes. Acts as compatibility shim during migration period, allowing gradual adoption of new API.","intents":["I have an existing application using trulens_eval<1.0.0 and want to upgrade to new version","I want to migrate to new TruLens API gradually without rewriting all code at once","I need to maintain compatibility with legacy code while using new features"],"best_for":["Teams with existing trulens_eval<1.0.0 applications requiring upgrade path","Organizations with large codebases where gradual migration is necessary","Projects needing backwards compatibility during transition period"],"limitations":["Backwards compatibility layer adds indirection and potential performance overhead","Not all old API features may be fully supported in new implementation — some deprecated features may not work","Layer will eventually be removed — applications must migrate to new API eventually","Mixing old and new API in same application may cause unexpected behavior due to different implementations"],"requires":["Python 3.9+","Existing application using trulens_eval<1.0.0 API","trulens-eval package (this package) installed alongside trulens-core>=1.0.0"],"input_types":["Legacy trulens_eval<1.0.0 API calls"],"output_types":["Mapped calls to trulens-core>=1.0.0 implementations","Equivalent results using new backend"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_2","uri":"capability://automation.workflow.session.based.application.lifecycle.and.database.connection.management","name":"session-based application lifecycle and database connection management","description":"TruSession class provides centralized orchestration for database connections, OTEL setup, evaluation scheduling, and run lifecycle. Manages DBConnector abstraction (SQLAlchemy, Snowflake event tables) for span/metric persistence, coordinates Evaluator thread for async feedback execution, and maintains context across application invocations. Session acts as entry point for developers: initialize once, wrap application, retrieve results.","intents":["I want a single configuration point for database, OTEL, and evaluation settings across my application","I need to manage database connections and async evaluation threads without manual lifecycle management","I want to retrieve recorded spans and metrics from a session after application execution"],"best_for":["Developers building LLM applications who want simplified observability setup","Teams using Jupyter notebooks or scripts where session-scoped state is natural","Multi-run evaluation workflows where session manages persistence across invocations"],"limitations":["Session is thread-local by default; multi-threaded applications require explicit session management per thread","No built-in session clustering or distributed state — each process maintains independent session, limiting horizontal scaling","Database connection pooling configured at session level; no dynamic pool resizing for variable load","Evaluator thread is single-threaded per session — evaluation throughput limited by sequential metric computation"],"requires":["Python 3.9+","Database backend configured: SQLite (file path), PostgreSQL (connection string), or Snowflake (account, warehouse, database)","trulens-core package with TruSession class","For OTEL export: TracerProvider and exporter configuration (e.g., OTLPExporter for Snowflake)"],"input_types":["Database connection parameters (URL, credentials, path)","OTEL configuration (exporter type, endpoint)","Evaluation settings (feedback functions, LLM provider config)"],"output_types":["TruSession instance (context manager)","Database records (spans, metrics, run metadata)","OTEL traces exported to backend"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_3","uri":"capability://data.processing.analysis.multi.backend.persistence.with.database.abstraction.layer","name":"multi-backend persistence with database abstraction layer","description":"DBConnector interface abstracts storage backend selection (SQLAlchemy for SQLite/PostgreSQL/MySQL, SnowflakeEventTableDB for Snowflake). Stores spans, feedback metrics, and run metadata in normalized schema. SQLAlchemy backend uses ORM models for relational storage; Snowflake backend exports OTEL spans directly to event tables for server-side querying. Enables schema migrations and versioning for database evolution.","intents":["I want to store application traces and metrics in my existing database (PostgreSQL, Snowflake)","I need to query spans and metrics using SQL without proprietary APIs","I want to migrate from SQLite to PostgreSQL or Snowflake without changing application code"],"best_for":["Teams with existing data warehouses (Snowflake, PostgreSQL) who want to integrate observability data","Organizations requiring SQL-based querying and analytics on trace data","Production deployments needing scalable, managed database backends"],"limitations":["SQLAlchemy backend requires schema migrations for version upgrades; no automatic schema evolution","Snowflake event table export is asynchronous and batched — traces not immediately queryable after export","No built-in data retention policies or automatic cleanup — requires external processes to manage table growth","Cross-database queries not supported — analytics require exporting data to data warehouse separately"],"requires":["Python 3.9+","Database backend: SQLite (file), PostgreSQL 12+, MySQL 8+, or Snowflake account with event table permissions","SQLAlchemy 2.0+ for relational backends","For Snowflake: snowflake-connector-python and appropriate IAM role permissions"],"input_types":["Span objects (OpenTelemetry format)","Feedback metric records (numeric scores, metadata)","Run metadata (app name, session ID, timestamp)"],"output_types":["Persisted records in database (rows in SQLAlchemy, events in Snowflake event tables)","Query results from SQL queries against stored data","Schema definitions (SQLAlchemy models, Snowflake event table schemas)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_4","uri":"capability://tool.use.integration.framework.specific.application.wrapping.with.semantic.span.kinds","name":"framework-specific application wrapping with semantic span kinds","description":"Provides framework-specific wrapper classes (TruChain for LangChain, TruGraph for LangGraph, TruLlama for LlamaIndex, TruBasicApp/TruCustomApp for custom apps) that intercept application execution and generate semantically-typed spans (GENERATION for LLM calls, RETRIEVAL for vector search, EVAL for feedback). Wrappers preserve original framework APIs while injecting instrumentation transparently.","intents":["I want to instrument my LangChain chain or LangGraph workflow without rewriting it","I need spans labeled with semantic types (GENERATION, RETRIEVAL) for better observability","I want to use TruLens with my custom application that doesn't fit standard frameworks"],"best_for":["Teams using LangChain, LangGraph, or LlamaIndex who want observability without framework changes","Custom application builders who need flexible instrumentation beyond framework-specific wrappers","Multi-framework deployments where consistent span semantics are required"],"limitations":["Framework wrappers tightly coupled to specific framework versions — breaking changes in LangChain/LangGraph require wrapper updates","TruCustomApp requires manual instrumentation of custom application methods — no automatic discovery","Wrapper overhead adds latency (~10-50ms per wrapped call) due to span creation and context propagation","Semantic span kinds (GENERATION, RETRIEVAL) are heuristic-based for some frameworks — may not perfectly match application intent"],"requires":["Python 3.9+","Framework package: langchain, langgraph, llama-index, or custom application","Corresponding trulens integration package: trulens-apps-langchain, trulens-apps-langgraph, etc.","TruSession initialized with database and OTEL configuration"],"input_types":["Framework application instance (Chain, Graph, custom callable)","Application inputs (prompts, documents, user queries)"],"output_types":["Wrapped application instance (preserves original API)","Instrumented execution with OTEL spans","Span records in database with semantic kinds"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_5","uri":"capability://search.retrieval.streamlit.based.interactive.dashboard.for.trace.visualization.and.leaderboard.comparison","name":"streamlit-based interactive dashboard for trace visualization and leaderboard comparison","description":"Provides Streamlit web interface (trulens.dashboard module) with trulens_leaderboard() function for comparing application runs, record viewers for inspecting individual traces, and feedback visualization. Dashboard queries database backend to display spans, metrics, and execution timelines. Enables non-technical stakeholders to explore application behavior and evaluation results without SQL knowledge.","intents":["I want to visualize traces and metrics from my LLM application in a web UI","I need to compare evaluation results across multiple application versions or configurations","I want to drill down into individual trace records to debug application behavior"],"best_for":["Teams evaluating LLM application quality and needing visual comparison tools","Product managers and non-technical stakeholders reviewing application performance","Developers debugging application behavior through trace inspection"],"limitations":["Dashboard performance degrades with large datasets (>100k spans) — no built-in pagination or sampling","Streamlit-based UI is single-user and not suitable for multi-user production deployments","No export functionality for reports or metrics — requires manual screenshot/CSV export","Real-time updates not supported — dashboard requires manual refresh to see new runs"],"requires":["Python 3.9+","Streamlit 1.0+","trulens-dashboard package","Database backend with recorded spans and metrics (via TruSession)"],"input_types":["Database connection (SQLAlchemy or Snowflake)","Run IDs or date range filters"],"output_types":["Interactive Streamlit web interface","Visualized spans, metrics, and execution timelines","Leaderboard tables comparing runs"],"categories":["search-retrieval","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_6","uri":"capability://automation.workflow.deferred.and.synchronous.evaluation.mode.selection.with.background.processing","name":"deferred and synchronous evaluation mode selection with background processing","description":"Supports two evaluation modes: synchronous (blocking, metrics computed before application returns) and deferred (asynchronous, metrics computed in background Evaluator thread after application completes). Mode selection via TruSession configuration. Deferred mode reduces application latency by decoupling evaluation from critical path; synchronous mode ensures metrics available immediately. Evaluator thread manages queue of pending feedback functions and executes them sequentially.","intents":["I want to evaluate my application without adding latency to user-facing requests","I need metrics available immediately after application execution for real-time feedback","I want to batch evaluate multiple runs efficiently using background processing"],"best_for":["Production applications where evaluation latency must not impact user experience","Batch evaluation workflows where deferred processing is acceptable","Development/testing where immediate metrics are valuable for iteration"],"limitations":["Deferred evaluation introduces eventual consistency — metrics not immediately available; applications must poll or wait for completion","Evaluator thread is single-threaded per session — evaluation throughput limited by sequential metric computation, can become bottleneck under high load","No built-in priority queue or scheduling — all feedback functions processed in FIFO order regardless of importance","Synchronous mode adds latency proportional to number of metrics and LLM provider response time (typically 1-5s per metric)"],"requires":["Python 3.9+","TruSession with evaluation mode configuration","LLM provider credentials for feedback function execution","For deferred mode: background thread support (standard in Python)"],"input_types":["Evaluation mode setting (sync/deferred)","Feedback functions to execute","Span data to evaluate"],"output_types":["Metric scores (immediate for sync, eventual for deferred)","Feedback records in database","Evaluator thread status/logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_7","uri":"capability://code.generation.editing.custom.instrumentation.with.instrument.decorator.for.arbitrary.python.methods","name":"custom instrumentation with @instrument decorator for arbitrary python methods","description":"@instrument decorator enables developers to manually instrument custom Python methods beyond framework-specific wrappers. Decorator captures method inputs, outputs, execution time, and exceptions, generating OTEL spans with configurable span kind and attributes. Supports nested instrumentation (decorated methods calling other decorated methods) with automatic span hierarchy. Enables fine-grained observability for custom business logic.","intents":["I want to instrument custom Python methods in my application that aren't covered by framework wrappers","I need to add custom attributes or span kinds to generated spans","I want to trace execution flow through my custom business logic"],"best_for":["Developers building custom LLM applications with business logic beyond framework operations","Teams needing fine-grained observability into application-specific methods","Hybrid applications combining framework-based and custom components"],"limitations":["Decorator requires explicit application to each method — no automatic discovery of methods to instrument","Decorator adds ~5-10ms overhead per method call due to span creation and context management","No support for async context managers or async generators — only standard async functions","Span attributes must be serializable to OTEL format — complex Python objects require custom serialization"],"requires":["Python 3.9+","trulens-core with @instrument decorator","TruSession initialized in application context","Method signatures must be compatible with decorator (no *args/**kwargs limitations)"],"input_types":["Python method to decorate","Optional span kind and custom attributes"],"output_types":["Decorated method (preserves original signature and behavior)","OTEL spans with captured inputs, outputs, timing","Span records in database"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_8","uri":"capability://data.processing.analysis.cost.tracking.and.endpoint.management.for.multi.provider.llm.evaluation","name":"cost tracking and endpoint management for multi-provider llm evaluation","description":"Tracks API costs (tokens consumed, API calls made) across multiple LLM providers (OpenAI, Bedrock, Cortex, HuggingFace, LiteLLM) during feedback function execution. Stores cost metadata alongside evaluation results. Enables cost analysis and optimization of evaluation pipelines. Endpoint management abstracts provider-specific API configurations (model names, API versions, rate limits).","intents":["I want to track evaluation costs across multiple LLM providers","I need to optimize evaluation pipeline costs by selecting cheaper providers","I want to understand cost breakdown by metric type and application version"],"best_for":["Teams evaluating LLM applications at scale where evaluation costs are significant","Multi-cloud deployments using different LLM providers with cost optimization goals","Finance/operations teams tracking AI infrastructure spending"],"limitations":["Cost tracking is approximate — based on token counts and published pricing, not actual billing","Provider pricing changes not automatically reflected — requires manual updates to cost configuration","No built-in cost alerts or budgeting — requires external monitoring to enforce cost limits","Cost data stored in database but no built-in cost analytics or reporting UI"],"requires":["Python 3.9+","LLM provider credentials and API access","Provider pricing configuration (tokens per call, cost per token)","trulens-core with cost tracking integration"],"input_types":["LLM provider configuration (endpoint, model, API key)","Feedback function execution logs (tokens consumed, API calls)"],"output_types":["Cost records linked to evaluation results","Cost metadata (provider, model, tokens, estimated cost)","Cost aggregations by metric/provider/run"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-trulens-eval__cap_9","uri":"capability://data.processing.analysis.virtual.runs.and.log.ingestion.for.external.application.evaluation","name":"virtual runs and log ingestion for external application evaluation","description":"Enables evaluation of applications not directly instrumented with TruLens via virtual runs and log ingestion. Developers provide application logs (inputs, outputs, metadata) in structured format; TruLens creates virtual run records and applies feedback functions retroactively. Supports batch ingestion of historical logs for post-hoc evaluation. Enables evaluation of external systems (APIs, third-party services) without instrumentation.","intents":["I want to evaluate an external LLM application that I don't control or can't instrument","I need to run evaluations on historical application logs after the fact","I want to ingest logs from multiple sources and evaluate them uniformly"],"best_for":["Teams evaluating third-party LLM services or APIs","Batch evaluation workflows processing historical logs","Organizations with existing logging infrastructure wanting to add LLM evaluation"],"limitations":["Virtual runs lack execution context (timing, intermediate steps) available from instrumented applications — evaluation based only on final inputs/outputs","Log ingestion requires structured format (JSON, CSV) — unstructured logs require preprocessing","No automatic span hierarchy or semantic span kinds for virtual runs — all spans treated uniformly","Batch ingestion latency depends on log volume and feedback function execution time"],"requires":["Python 3.9+","Structured application logs (JSON or CSV format with input/output fields)","TruSession with database backend","Feedback functions compatible with available log fields"],"input_types":["Structured logs (JSON, CSV) with application inputs and outputs","Log schema definition mapping fields to span attributes","Feedback functions to apply"],"output_types":["Virtual run records in database","Evaluation metrics linked to virtual runs","Ingestion status and error logs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","OpenTelemetry Python SDK (trulens-core dependency)","Database backend: SQLite (default), PostgreSQL, MySQL, or Snowflake account with event table permissions","For framework-specific instrumentation: LangChain, LangGraph, LlamaIndex, or custom app wrapper class","API credentials for at least one LLM provider: OpenAI API key, AWS Bedrock access, Snowflake Cortex credentials, or HuggingFace token","trulens-core with Feedback class and LLMProvider implementations","For async evaluation: background Evaluator thread (managed by TruSession)","Snowflake account with event table permissions","Snowflake Cortex access for server-side evaluation","snowflake-connector-python and appropriate IAM role"],"failure_modes":["Decorator-based approach requires application code to import and use @instrument — cannot instrument third-party libraries without wrapper classes","Span export latency depends on database backend; Snowflake exports may batch asynchronously, delaying visibility","No built-in sampling or filtering at instrumentation time — all decorated methods generate spans, requiring downstream filtering for high-volume applications","Feedback function execution cost scales with number of spans and metrics — each metric invokes an LLM API call, adding ~$0.001-0.01 per evaluation","Deferred evaluation introduces eventual consistency — metrics not immediately available after application run completes","Custom feedback functions require Python code; no declarative metric definition language","LLM provider latency (typically 1-5s per call) can become bottleneck for high-volume evaluation; no built-in batching across spans","Snowflake event table export is asynchronous and batched — traces not immediately queryable after export (typical latency 30-60s)","Server-side evaluation in Cortex limited to Snowflake-supported models and functions — less flexibility than client-side evaluation","Requires Snowflake account with event table permissions and Cortex access — not available in all Snowflake editions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:18.280Z","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=pypi-trulens-eval","compare_url":"https://unfragile.ai/compare?artifact=pypi-trulens-eval"}},"signature":"9X5xQzfDnlPW1yHqN2JmCyd6WRma6hDbGGgp8o438dahf6brjxBYqNotSpU19pRyE2vcXTVS7Bo4N6IZ1g4IAQ==","signedAt":"2026-06-22T16:55:09.590Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-trulens-eval","artifact":"https://unfragile.ai/pypi-trulens-eval","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-trulens-eval","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"}}