trulens-eval
RepositoryFreeBackwards-compatibility package for API of trulens_eval<1.0.0 using API of trulens-*>=1.0.0.
Capabilities13 decomposed
opentelemetry-based application instrumentation with decorator-driven span generation
Medium confidenceWraps 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.
Uses a decorator-based instrumentation model that generates structured OTEL spans with semantic span kinds (GENERATION, RETRIEVAL, EVAL) specific to LLM workflows, rather than generic HTTP/RPC spans. Integrates directly with TruSession for unified span collection and evaluation lifecycle management.
Simpler than manual OTEL instrumentation and more LLM-aware than generic APM tools; requires less boilerplate than Langsmith's tracing while maintaining OTEL standard compliance.
llm-based feedback function evaluation with multi-provider support
Medium confidenceComputes 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.
Abstracts LLM provider selection behind LLMProvider interface, enabling same feedback function to run against OpenAI, Bedrock, Cortex, or local models without code changes. Integrates evaluation lifecycle with span collection via RunManager, enabling automatic metric computation on application traces.
More flexible than Langsmith's built-in metrics (supports custom LLM providers and deferred evaluation); more integrated than standalone evaluation frameworks (metrics tied directly to application spans and session lifecycle).
snowflake event table export and server-side evaluation pipeline
Medium confidenceExports 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.
Exports OTEL spans directly to Snowflake event tables and enables server-side evaluation in Snowflake Cortex, avoiding data export and enabling native SQL querying. Tighter integration than generic OTEL exporters.
More efficient than client-side evaluation for large-scale deployments; enables SQL-based analytics on trace data within data warehouse.
run management and external agent integration for distributed evaluation
Medium confidenceRunManager 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.
Provides RunManager for tracking run lifecycle and metadata, with support for external agents to execute distributed evaluation. Enables horizontal scaling of evaluation workload.
More integrated than generic job queues; provides run-level abstraction specific to LLM evaluation workflows.
backwards compatibility layer for trulens_eval<1.0.0 api migration
Medium confidenceThis 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.
Provides compatibility shim mapping trulens_eval<1.0.0 API to trulens-core>=1.0.0 implementations, enabling zero-change upgrades for existing applications.
Enables gradual migration path vs requiring immediate rewrite; reduces upgrade friction for existing users.
session-based application lifecycle and database connection management
Medium confidenceTruSession 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.
Centralizes database, OTEL, and evaluation orchestration in single TruSession object that manages DBConnector abstraction, Evaluator thread lifecycle, and run context. Enables context manager pattern (with statement) for automatic resource cleanup.
Simpler than manual OTEL setup and database connection management; more integrated than standalone database libraries because it couples persistence with evaluation scheduling and span collection.
multi-backend persistence with database abstraction layer
Medium confidenceDBConnector 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.
Provides DBConnector abstraction that supports both relational (SQLAlchemy) and cloud-native (Snowflake event tables) backends with unified API. Snowflake backend exports OTEL spans directly to event tables, enabling server-side querying without ETL.
More flexible than single-backend solutions; Snowflake integration is deeper than generic OTEL exporters because it uses event table schema optimized for trace data.
framework-specific application wrapping with semantic span kinds
Medium confidenceProvides 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.
Provides framework-specific wrappers that generate semantically-typed spans (GENERATION, RETRIEVAL, EVAL) tailored to LLM workflows, rather than generic function call spans. Wrappers intercept framework-level operations (LLM calls, vector search) to assign correct span kinds automatically.
More semantic than generic OTEL instrumentation; more framework-aware than manual span creation; preserves original framework APIs unlike some observability solutions that require code rewriting.
streamlit-based interactive dashboard for trace visualization and leaderboard comparison
Medium confidenceProvides 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.
Provides Streamlit-based dashboard tightly integrated with TruLens database backend, enabling interactive trace exploration and run comparison without custom SQL. trulens_leaderboard() function simplifies common comparison workflows.
Simpler than building custom dashboards; more integrated than generic OTEL visualization tools because it understands LLM-specific metrics and span semantics.
deferred and synchronous evaluation mode selection with background processing
Medium confidenceSupports 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.
Provides explicit mode selection (sync vs deferred) for evaluation lifecycle, with background Evaluator thread managing async metric computation. Enables applications to choose latency vs immediate feedback tradeoff.
More flexible than always-async or always-sync evaluation; better latency characteristics than synchronous-only approaches for production workloads.
custom instrumentation with @instrument decorator for arbitrary python methods
Medium confidence@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.
Provides @instrument decorator that generates OTEL spans for arbitrary Python methods with configurable span kinds and attributes, enabling fine-grained custom instrumentation beyond framework wrappers.
More flexible than framework-specific wrappers; simpler than manual OTEL span creation; enables developers to instrument custom logic without boilerplate.
cost tracking and endpoint management for multi-provider llm evaluation
Medium confidenceTracks 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).
Integrates cost tracking directly into feedback function execution, capturing provider-specific costs (tokens, API calls) and storing alongside evaluation metrics. Enables cost-aware evaluation optimization.
More integrated than external cost monitoring tools; provides cost data at evaluation granularity rather than aggregate provider billing.
virtual runs and log ingestion for external application evaluation
Medium confidenceEnables 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.
Enables evaluation of non-instrumented applications by ingesting structured logs and creating virtual run records, then applying feedback functions retroactively. Supports batch processing of historical logs.
Enables evaluation of external systems without instrumentation; more flexible than requiring direct application integration.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with trulens-eval, ranked by overlap. Discovered automatically through the match graph.
TruLens
LLM app instrumentation and evaluation with feedback functions.
OpenLLMetry
OpenTelemetry-based LLM observability with automatic instrumentation.
@traceloop/instrumentation-mcp
MCP (Model Context Protocol) Instrumentation
DeepEval
LLM evaluation framework — 14+ metrics, faithfulness/hallucination detection, Pytest integration.
deepeval
The LLM Evaluation Framework
Parea AI
LLM debugging, testing, and monitoring developer platform.
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
- ✓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
- ✓Organizations with Snowflake data warehouses who want integrated observability
- ✓Teams evaluating LLM applications at scale where server-side processing is more efficient
Known 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
- ⚠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
Requirements
Input / Output
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Backwards-compatibility package for API of trulens_eval<1.0.0 using API of trulens-*>=1.0.0.
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