Trulens Instrumentation

TruLens Instrumentation

Instrument your LLM application to capture traces for evaluation and debugging. This skill covers everything from initial setup to iterative improvement of trace quality.

When to Use This Skill

• Setting up instrumentation for a new app
• Adding custom spans to framework-wrapped apps
• Improving trace readability (unclear span names, missing context)
• Debugging why evaluations aren't working (missing attributes)
• Optimizing what gets captured for visualization

Part 1: Setup

Instrument your LLM application to capture traces for evaluation and debugging.

Interactive Instrumentation Setup

Let's identify what you need to instrument for visualization and/or evaluation.

Question 1: What framework are you using?

→ If using a framework, the wrapper handles basic instrumentation automatically. Continue to Question 2 to add custom attributes.

Question 2: What data do you want to capture?

Tell me what's important to track in your app. This could be for:

• Visualization: Understanding execution flow in the dashboard
• Evaluation: Feeding data into feedback functions

Common attributes to instrument:

What specific data do you want to capture that isn't listed above?

Examples:

• "I want to capture the similarity scores from my retriever"
• "I need to track which documents were filtered out"
• "I want to see the intermediate chain-of-thought reasoning"
• "I need to capture metadata about each retrieved chunk (source, page number)"

Question 3: Do you have custom functions that need instrumentation?

If you have functions that aren't automatically instrumented, list them:

Example response:

• retrieve_documents(query) - returns list of documents
• rerank_results(query, docs) - reranks and filters documents
• generate_response(query, context) - calls LLM to generate answer

For each function, I'll help you add the right @instrument() decorator with appropriate span types and attributes.

Template: Instrumenting Your Custom Function

Tell me about your function and I'll generate the instrumentation:

Function name: _______________
What it does: _______________
Input parameters: _______________
What it returns: _______________
What data should be captured for eval/visualization: _______________

Example:

Function name: retrieve_documents
What it does: Searches vector store for relevant documents
Input parameters: query (str), top_k (int)
What it returns: List of document dicts with 'text', 'source', 'score' keys
What data should be captured: The query text and the document texts (not scores/sources)

→ Generated instrumentation:

@instrument(
    span_type=SpanAttributes.SpanType.RETRIEVAL,
    attributes={
        SpanAttributes.RETRIEVAL.QUERY_TEXT: "query",
        SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: "return",
    },
)
def retrieve_documents(query: str, top_k: int = 5) -> list:
    # If you need to extract just the text from complex returns:
    pass


# Or with lambda for complex extraction:
@instrument(
    span_type=SpanAttributes.SpanType.RETRIEVAL,
    attributes=lambda ret, exception, *args, **kwargs: {
        SpanAttributes.RETRIEVAL.QUERY_TEXT: kwargs.get("query", args[0]),
        SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: [
            doc["text"] for doc in ret
        ],
    },
)
def retrieve_documents(query: str, top_k: int = 5) -> list:
    pass

Overview

TruLens provides two approaches to instrumentation:

1. Framework Wrappers: Auto-instrument apps built with LangChain, LangGraph, or LlamaIndex
2. Custom Instrumentation: Use @instrument() decorator for custom apps or to add additional spans to framework apps

Prerequisites

pip install trulens
# For framework-specific support:
pip install trulens-apps-langchain  # LangChain/LangGraph
pip install trulens-apps-llamaindex  # LlamaIndex

Instructions

Step 1: Initialize TruSession

from trulens.core import TruSession

session = TruSession()

Step 2: Choose Your Instrumentation Approach

Option A: Framework Wrappers (Recommended for Framework Apps)

For LangChain apps:

from trulens.apps.langchain import TruChain

tru_recorder = TruChain(chain, app_name="MyLangChainApp", app_version="v1")

with tru_recorder as recording:
    result = chain.invoke("your query")

For LangGraph apps:

from trulens.apps.langgraph import TruGraph

# TruGraph auto-detects graph nodes and @task decorators
tru_recorder = TruGraph(graph, app_name="MyLangGraphAgent", app_version="v1")

with tru_recorder as recording:
    result = graph.invoke({"messages": [HumanMessage(content="your query")]})

Deep Agents / LangGraph Instrumentation

LangChain's Deep Agents framework is built on LangGraph. Use TruGraph for full instrumentation:

from deepagents import create_deep_agent
from trulens.apps.langgraph import TruGraph
from trulens.core import TruSession

# Create the Deep Agent
agent = create_deep_agent(
    model=model, tools=[your_tools], system_prompt="Your prompt"
)

# Wrap with TruGraph - captures all internal nodes, tool calls, planning steps
tru_agent = TruGraph(
    agent,
    app_name="DeepAgent",
    app_version="v1",
    feedbacks=[f_answer_relevance],
)

with tru_agent as recording:
    result = agent.invoke({"messages": [{"role": "user", "content": query}]})

For LlamaIndex apps

from trulens.apps.llamaindex import TruLlama

tru_recorder = TruLlama(query_engine, app_name="MyRAG", app_version="v1")

with tru_recorder as recording:
    result = query_engine.query("your query")
**For LlamaIndex query engines:**

```python
from trulens.apps.llamaindex import TruLlama

query_engine = index.as_query_engine()

tru_recorder = TruLlama(
    query_engine,
    app_name="MyLlamaIndexApp",
    app_version="v1"
)

with tru_recorder as recording:
    result = query_engine.query("your query")

For LlamaIndex workflows:

from trulens.apps.llamaindex import TruLlamaWorkflow

tru_recorder = TruLlamaWorkflow(
    workflow, app_name="MyLlamaWorkflow", app_version="v1"
)

with tru_recorder as recording:
    result = await workflow.run(query="your query")

Option B: Custom Instrumentation with @instrument()

For custom apps or to add spans to framework apps:

from trulens.apps.app import TruApp
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:
        # Your retrieval logic
        return contexts

    @instrument(span_type=SpanAttributes.SpanType.GENERATION)
    def generate(self, query: str, contexts: list) -> str:
        # Your generation logic
        return response

    @instrument(
        span_type=SpanAttributes.SpanType.RECORD_ROOT,
        attributes={
            SpanAttributes.RECORD_ROOT.INPUT: "query",
            SpanAttributes.RECORD_ROOT.OUTPUT: "return",
        },
    )
    def query(self, query: str) -> str:
        contexts = self.retrieve(query)
        return self.generate(query, contexts)


rag = MyRAG()
tru_app = TruApp(rag, app_name="MyCustomRAG", app_version="v1")

with tru_app as recording:
    result = rag.query("your query")

Step 3: Combining Wrappers with Custom Instrumentation

Use @instrument() alongside framework wrappers to add custom span attributes for evaluation:

from trulens.apps.langgraph import TruGraph
from trulens.core.otel.instrument import instrument
from trulens.otel.semconv.trace import SpanAttributes


@instrument()
def preprocess_input(topic: str) -> str:
    """Custom preprocessing - will appear in traces."""
    return f"Preprocessed: {topic}"


@instrument(
    span_type=SpanAttributes.SpanType.RETRIEVAL,
    attributes={
        SpanAttributes.RETRIEVAL.QUERY_TEXT: "query",
        SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: "return",
    },
)
def custom_retrieve(query: str) -> list:
    """Custom retrieval with semantic attributes for evaluation."""
    return ["context1", "context2"]


# TruGraph will capture both auto-instrumented spans and your @instrument spans
tru_recorder = TruGraph(graph, app_name="EnhancedAgent", app_version="v1")

Step 4: Lambda-Based Attribute Extraction

For complex data structures, use a lambda to extract attributes:

@instrument(
    span_type=SpanAttributes.SpanType.RETRIEVAL,
    attributes=lambda ret, exception, *args, **kwargs: {
        SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: [
            doc["text"] for doc in ret
        ],
        SpanAttributes.RETRIEVAL.QUERY_TEXT: kwargs.get(
            "query", args[0] if args else ""
        ),
    },
)
def retrieve_documents(query: str) -> list:
    return [{"text": "doc1", "score": 0.9}, {"text": "doc2", "score": 0.8}]

Step 5: Instrumenting Third-Party Classes

When you can't modify source code, use instrument_method():

from trulens.core.otel.instrument import instrument_method
from some_library import ExternalRetriever

instrument_method(
    cls=ExternalRetriever,
    method_name="search",
    span_type=SpanAttributes.SpanType.RETRIEVAL,
    attributes={
        SpanAttributes.RETRIEVAL.QUERY_TEXT: "query",
        SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: "return",
    },
)

Common Patterns

RAG Application

@instrument(span_type=SpanAttributes.SpanType.RETRIEVAL, attributes={...})
def retrieve(self, query): ...


@instrument(span_type=SpanAttributes.SpanType.GENERATION)
def generate(self, query, context): ...


@instrument(span_type=SpanAttributes.SpanType.RECORD_ROOT, attributes={...})
def query(self, query): ...

Agent Application

@instrument(span_type=SpanAttributes.SpanType.AGENT)
def run_agent(self, task): ...


@instrument(span_type=SpanAttributes.SpanType.TOOL)
def call_tool(self, tool_name, args): ...


@instrument(span_type=SpanAttributes.SpanType.WORKFLOW)
def execute_workflow(self, steps): ...

Why TruGraph instead of TruApp + @instrument?

• TruGraph automatically captures all LangGraph nodes and transitions
• TruGraph creates RECORD_ROOT spans required for .on_input()/.on_output() shortcuts
• Manual @instrument(span_type=SpanType.AGENT) will NOT work with feedback selector shortcuts

Critical: Span Types and Feedback Selectors

The .on_input() and .on_output() feedback selector shortcuts look for spans with type RECORD_ROOT:

# This WORKS - TruGraph creates RECORD_ROOT spans automatically
tru_agent = TruGraph(agent, feedbacks=[f_answer_relevance])


# This also WORKS - explicit RECORD_ROOT
@instrument(
    span_type=SpanAttributes.SpanType.RECORD_ROOT,
    attributes={
        SpanAttributes.RECORD_ROOT.INPUT: "query",
        SpanAttributes.RECORD_ROOT.OUTPUT: "return",
    },
)
def query(self, query: str) -> str: ...


# This WILL NOT WORK with .on_input()/.on_output() shortcuts!
@instrument(span_type=SpanAttributes.SpanType.AGENT)  # Wrong span type
def run_agent(self, task): ...

If your evaluations show empty feedback columns, check that your root span uses RECORD_ROOT span type.

Troubleshooting

• Spans not appearing: Ensure you're using @instrument() with parentheses (not @instrument)
• Missing context in evaluations: Add semantic attributes to map function args/returns
• Framework not detected: Verify the correct wrapper is imported (TruChain vs TruGraph vs TruLlama)
• Feedback columns empty/evaluations not running: Your root span must use SpanType.RECORD_ROOT for .on_input()/.on_output() shortcuts to work. Use framework wrappers (TruGraph, TruChain) which handle this automatically.