Manual Tracing

Manual tracing gives you complete control over span creation, attributes, and lifecycle. Use it when you need to trace custom operations, add detailed metadata, or track usage and costs.

Getting Started

To start manual tracing, you’ll need to:

Import the required classes from Netra
Create a new span using start_span()
Track your operations within the span
Add relevant attributes and events

Creating Spans

Use start_span() to create a span that wraps a block of code. In Python, use it as a context manager. In TypeScript, explicitly call end() when done.

from netra import Netra

# Use as context manager (recommended)
with Netra.start_span("process-document") as span:
    result = process_document(doc)
    span.set_attribute("document.pages", result.page_count)
    # Span automatically ends when exiting the context

Span Parameters

Parameter	Type	Description
`name`	string	Name of the span (required)
`attributes`	dict/object	Initial attributes to set on the span
`module_name` / `moduleName`	string	Module or component name for organization
`as_type` / `asType`	SpanType	Type of span (SPAN, GENERATION, TOOL, etc.)

from netra import Netra, SpanType

with Netra.start_span(
    "generate-summary",
    attributes={
        "input.length": len(document),
        "model": "gpt-4",
    },
    module_name="summarization",
    as_type=SpanType.GENERATION,
) as span:
    # Your code here
    pass

Span Types

Use the as_type parameter to categorize spans. This helps Netra display them correctly and enables type-specific features.

Type	Use For
`SpanType.GENERATION`	LLM completions, image generation
`SpanType.EMBEDDING`	Vector embedding operations
`SpanType.TOOL`	Function calls, API requests, DB queries
`SpanType.AGENT`	AI agent reasoning and decisions
`SpanType.SPAN`	General operations (default)

See Spans for detailed guidance on when to use each type.

Local Span Blocking

You can block specific spans locally within a particular span scope. This is useful when you want to filter out noisy child spans (like HTTP requests) within a specific operation.

from netra import Netra

# Block POST spans within this scope
with Netra.start_span("image-generation", attributes={"blocked_spans": ["POST", "GET"]}) as span:
    # HTTP spans named "POST" or "GET" created within this scope will be filtered
    generate_image(prompt)

This is different from global blocked_spans in Netra.init() which blocks spans across the entire application. Local blocking only affects spans created within the specific parent span’s scope.

SpanWrapper Methods

The start_span() function returns a SpanWrapper object with methods for adding context to your spans.

Setting Span Attributes

Add custom key-value pairs to provide context about the operation:

with Netra.start_span("search-products") as span:
    span.set_attribute("query", user_query)
    span.set_attribute("filters.category", category)
    span.set_attribute("filters.price_range", [min_price, max_price])
    span.set_attribute("results.count", len(results))

LLM-Specific Attributes

For LLM operations, use dedicated methods to set prompts, models, and system information:

from netra import Netra, SpanType

with Netra.start_span("generate-response", as_type=SpanType.GENERATION) as span:
    span.set_prompt(user_message)
    span.set_negative_prompt("blurry, low quality")  # For image generation
    span.set_model("gpt-4-turbo")
    span.set_llm_system("openai")

    response = generate_response(user_message)

    span.set_attribute("completion", response.content)

Recording Events

Track significant moments within a span’s lifecycle:

with Netra.start_span("order-processing") as span:
    span.add_event("validation-started")
    validate_order(order)
    span.add_event("validation-completed", {"valid": True})

    span.add_event("payment-started")
    payment = process_payment(order)
    span.add_event("payment-completed", {
        "transaction_id": payment.id,
        "amount": payment.amount,
    })

Tracking Usage Data

Use UsageModel to track token usage and costs for LLM operations:

from netra import Netra, SpanType, UsageModel

with Netra.start_span("llm-call", as_type=SpanType.GENERATION) as span:
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
    )

    # Track usage
    span.set_usage([
        UsageModel(
            model="gpt-4",
            cost_in_usd=calculate_cost(response.usage),
            usage_type="chat",
            units_used=1,
        )
    ])

UsageModel Fields

Field	Type	Description
`model`	string	Model name used
`cost_in_usd` / `costInUsd`	float	Calculated cost in USD
`usage_type` / `usageType`	string	Type of usage (e.g., “chat”, “image_generation”)
`units_used` / `unitsUsed`	int	Number of units consumed

Adding Action Tracking

Use ActionModel to track discrete actions, tool calls, or database operations within a span:

from netra import Netra, ActionModel

with Netra.start_span("agent-execution") as span:
    # Record actions taken by the agent
    span.set_action([
        ActionModel(
            action="DB",
            action_type="INSERT",
            affected_records=[
                {"record_id": "user_123", "record_type": "user"},
                {"record_id": "profile_456", "record_type": "profile"},
            ],
            metadata={
                "table": "users",
                "operation_id": "tx_789",
                "duration_ms": "45",
            },
            success=True,
        ),
        ActionModel(
            action="API",
            action_type="CALL",
            metadata={
                "endpoint": "/api/v1/process",
                "method": "POST",
                "status_code": "200",
            },
            success=True,
        ),
    ])

ActionModel Fields

Field	Type	Description
`action`	string	Action category (e.g., “DB”, “API”, “CACHE”)
`action_type` / `actionType`	string	Action subtype (e.g., “INSERT”, “SELECT”, “CALL”)
`affected_records` / `affectedRecords`	array	List of affected records with `record_id` and `record_type`
`metadata`	dict/object	Additional metadata as key-value pairs
`success`	boolean	Whether the action succeeded

Error Handling

Mark spans as errors when operations fail:

from netra import Netra

with Netra.start_span("risky-operation") as span:
    try:
        result = risky_operation()
        span.set_success()
    except Exception as e:
        span.set_error(str(e))
        raise

When using Python’s context manager, exceptions are automatically recorded and the span is marked as an error. You can still explicitly call set_error() for custom error messages.

Nested Spans

Create hierarchical traces by nesting spans. Child spans automatically inherit the parent context:

from netra import Netra

def process_order(order: dict):
    with Netra.start_span("process-order") as parent_span:
        parent_span.set_attribute("order.id", order["id"])

        # Child span for validation
        with Netra.start_span("validate-order"):
            validate_order(order)

        # Child span for payment
        with Netra.start_span("process-payment") as payment_span:
            payment = process_payment(order)
            payment_span.set_attribute("payment.id", payment.id)

        # Child span for fulfillment
        with Netra.start_span("fulfill-order"):
            fulfill_order(order)

Accessing the Current Span

Get the currently active span to add attributes from anywhere in your code:

from netra import Netra

def log_user_action(action: str):
    current_span = Netra.get_current_span()
    if current_span:
        current_span.add_event("user-action", {"action": action})

# Usage within a traced operation
with Netra.start_span("user-session"):
    # ... somewhere deep in the call stack ...
    log_user_action("clicked-submit")

Example: RAG Pipeline

This example demonstrates nested spans with multiple span types - a common pattern for AI pipelines.

from netra import Netra, SpanType, UsageModel

def rag_pipeline(query: str):
    with Netra.start_span("rag-pipeline") as pipeline_span:
        pipeline_span.set_attribute("query", query)

        # Step 1: Generate embedding
        with Netra.start_span(
            "generate-embedding", as_type=SpanType.EMBEDDING
        ) as embed_span:
            embedding = embed_model.embed(query)
            embed_span.set_usage([
                UsageModel(
                    model="text-embedding-3-small",
                    usage_type="embedding",
                    units_used=1
                )
            ])

        # Step 2: Retrieve documents
        with Netra.start_span(
            "retrieve-documents", as_type=SpanType.TOOL
        ) as retrieve_span:
            documents = vector_store.search(embedding, top_k=5)
            retrieve_span.set_attribute("documents.count", len(documents))

        # Step 3: Generate response
        with Netra.start_span(
            "generate-response", as_type=SpanType.GENERATION
        ) as generate_span:
            generate_span.set_prompt(query)
            generate_span.set_model("gpt-4")
            generate_span.set_llm_system("openai")

            response = openai.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": build_context(documents)},
                    {"role": "user", "content": query},
                ],
            )

            generate_span.set_usage([
                UsageModel(
                    model="gpt-4",
                    cost_in_usd=calculate_cost(response.usage),
                    usage_type="chat",
                    units_used=1,
                )
            ])

        pipeline_span.set_success()
        return response.choices[0].message.content

Best Practices

Use context managers in Python - They ensure spans are properly closed even when exceptions occur.
End spans in TypeScript - Always call span.end() in both success and error paths, preferably in a finally block.
Add meaningful attributes - Include information that will help you debug and analyze traces later.
Track usage for LLM calls - Use setUsage() to monitor token consumption and costs.
Use appropriate span types - Set as_type to categorize spans correctly (GENERATION for LLM calls, TOOL for function calls, etc.).
Handle errors explicitly - Call setError() with descriptive messages to make debugging easier.
Use local span blocking - Filter noisy child spans when you only care about the parent operation.
Add events for milestones - Use addEvent() to mark important points in long-running operations.

Learn More

Decorators - Simpler instrumentation with decorators
Auto Instrumentation - Zero-code tracing
Initialization - Configure tracing behavior

Get Started

Observability

Evaluation

Simulation

Prompt Management

Monitoring & Dashboard

Account settings

Migrations

Getting Started

Creating Spans

Span Parameters

Span Types

Local Span Blocking

SpanWrapper Methods

Setting Span Attributes

LLM-Specific Attributes

Recording Events

Tracking Usage Data

UsageModel Fields

Adding Action Tracking

ActionModel Fields

Error Handling

Nested Spans

Accessing the Current Span

Example: RAG Pipeline

Best Practices

Learn More

Get Started

Observability

Evaluation

Simulation

Prompt Management

Monitoring & Dashboard

Account settings

Migrations

Documentation Index

​Getting Started

​Creating Spans

​Span Parameters

​Span Types

​Local Span Blocking

​SpanWrapper Methods

​Setting Span Attributes

​LLM-Specific Attributes

​Recording Events

​Tracking Usage Data

​UsageModel Fields

​Adding Action Tracking

​ActionModel Fields

​Error Handling

​Nested Spans

​Accessing the Current Span

​Example: RAG Pipeline

​Best Practices

​Learn More

Getting Started

Creating Spans

Span Parameters

Span Types

Local Span Blocking

SpanWrapper Methods

Setting Span Attributes

LLM-Specific Attributes

Recording Events

Tracking Usage Data

UsageModel Fields

Adding Action Tracking

ActionModel Fields

Error Handling

Nested Spans

Accessing the Current Span

Example: RAG Pipeline

Best Practices

Learn More