Don't just vibe-check your LLM app! Systematically evaluate and track your LLM experiments with TruLens. As you develop your app including prompts, models, retrievers, knowledge sources and more, TruLens is the tool you need to understand its performance.

Fine-grained, stack-agnostic instrumentation and comprehensive evaluations help you to identify failure modes & systematically iterate to improve your application.

Read more about the core concepts behind TruLens including Feedback Functions, The RAG Triad, and Honest, Harmless and Helpful Evals.

Build your first prototype then connect instrumentation and logging with TruLens. Decide what feedbacks you need, and specify them with TruLens to run alongside your app. Then iterate and compare versions of your app in an easy-to-use user interface 👇

Install the trulens pip package from PyPI.

pip install trulens-core

Install with a specific LLM provider for feedback evaluation:

pip install trulens trulens-providers-openai   # OpenAI / Azure OpenAI
pip install trulens trulens-providers-litellm  # LiteLLM (Anthropic, Cohere, Mistral, …)
pip install trulens trulens-providers-google   # Google Gemini
pip install trulens trulens-providers-bedrock  # AWS Bedrock
pip install trulens trulens-providers-cortex   # Snowflake Cortex
pip install trulens trulens-providers-huggingface  # HuggingFace
pip install trulens trulens-providers-langchain    # LangChain models

Install with a specific app framework integration:

pip install trulens trulens-apps-langchain    # LangChain / LangGraph
pip install trulens trulens-apps-llamaindex  # LlamaIndex

Walk through how to instrument and evaluate a RAG built from scratch with TruLens.

TruLens instrumentation is built on OpenTelemetry. Every function call, LLM generation, retrieval, and tool invocation is captured as a structured OTEL span. This makes TruLens interoperable with existing observability infrastructure — export traces to Jaeger, Grafana Tempo, Datadog, or any OTLP-compatible backend.

from trulens.core.otel.instrument import instrument
from trulens.otel.semconv.trace import SpanAttributes

class MyRAG:
    @instrument(
        span_type=SpanAttributes.SpanType.RETRIEVAL,
        attributes={
            SpanAttributes.RETRIEVAL.QUERY_TEXT: "query",
            SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: "return",
        },
    )
    def retrieve(self, query: str) -> list:
        ...

Seven purpose-built evaluators for agentic systems — each measuring a distinct aspect of agent behavior:

Run evaluations alongside your app, on existing data, or in offline batch mode:

# Inline — evaluate as the app runs
with tru_recorder as recording:
    response = my_app.query("What is TruLens?")

# Batch — evaluate a pre-collected dataset using the TruLens 2.8 Run API
from trulens.core.run import RunConfig

run_config = RunConfig(
    run_name="batch_eval_v1",
    dataset_name="eval_questions",
    source_type="TABLE",
    dataset_spec={"input": "QUESTION"},
    invocation_max_workers=8,
    metric_max_workers=4,
)
run = tru_app.add_run(run_config=run_config)
run.start()
run.compute_metrics([relevance, groundedness])

Instrument Model Context Protocol tool calls with the MCP span type to capture tool name, arguments, output, and latency:

@instrument(span_type=SpanAttributes.SpanType.MCP)
def call_mcp_tool(self, tool_name: str, arguments: dict) -> str:
    ...

Target any span attribute for evaluation using the flexible Selector API:

from trulens.core import Metric, Selector

f_context_relevance = Metric(
    name="Context Relevance",
    implementation=provider.context_relevance,
    selectors={
        "input": Selector.select_record_input(),
        "context": Selector.select_context(),
    },
)

Interested in contributing? See our contributing guide for more details.

The best way to support TruLens is to give us a ⭐ on GitHub and join our discourse community!