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Documentation Index

Fetch the complete documentation index at: https://docs.orq.ai/llms.txt

Use this file to discover all available pages before exploring further.

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What are Traces

Traces let you dive into the workflow of each model generation and understand the inner workings of an LLM call on orq.ai. Traces correspond to events within the generations. Events within a Deployment can be various: Traces let you dive into the events, understanding the inputs, outputs and execution of each step.

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Creating Traces

Traces are automatically generated when using Orq.ai Deployments. You can also create custom traces for your application code using framework instrumentation or the Orq.ai SDK.

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Framework Instrumentation

For popular AI frameworks and libraries, use automatic instrumentation with OpenTelemetry:

Framework Integrations

Explore automatic instrumentation for OpenAI, LangChain, LlamaIndex, CrewAI, Autogen, and 15+ other frameworks with OpenTelemetry integration.

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Custom Tracing using the @traced decorator

For custom functions or application workflows, use the @traced decorator from the Python SDK. It works with both synchronous and async functions: 
import os
from orq_ai_sdk import Orq
from orq_ai_sdk.traced import traced

@traced(name="process_document", type="agent")
def process_document(doc_id: str):
    result = {"status": "processed", "doc_id": doc_id}
    return result

@traced(name="fetch_context", type="retrieval")
def fetch_relevant_context(query: str):
    return {"context": "relevant information", "sources": ["doc1", "doc2"]}

@traced(name="format_response", type="function")
def format_response(raw_output: str, metadata: dict):
    return {"formatted": raw_output.strip(), "metadata": metadata}

with Orq(api_key=os.getenv("ORQ_API_KEY")) as orq:
    doc = process_document("doc-123")
    context = fetch_relevant_context("user query")

    response = orq.deployments.invoke(
        key="summarizer",
        context={"environments": ["production"]},
        inputs={"text": doc["status"], "context": context}
    )

    formatted = format_response(response.choices[0].message.content, {"doc_id": "doc-123"})
The @traced decorator supports the following span types:
TypeDescription
agentA high-level orchestration step or agent workflow
embeddingAn embedding generation operation
functionA general-purpose function or processing step
llmA direct LLM API call
retrievalA retrieval or knowledge lookup operation
toolAn external tool call
You can also capture or suppress inputs/outputs with capture_input and capture_output, and attach custom metadata with the attributes parameter:
@traced(
    name="process_document",
    type="agent",
    capture_input=False,
    capture_output=False,
    attributes={"version": "1.0", "env": "production"}
)
def process_document(doc_id: str):
    ...

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How to Lookup Traces

To find traces, head to the AI Studio and to the Traces section.
By looking up events, you can explore traces for each action taken during a generation:
  • The left column shows the list of Traces available.
  • By selecting a trace, the middle column opens, showing the hierarchy of events shown in order of execution.
  • By selecting a single trace, the right column opens, showing details for the specific step.

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Trace Views

Each trace can be inspected in three views to suit different debugging and analysis needs.
The Trace view shows the full execution tree for a single run. Each step is displayed hierarchically, including LLM calls, tool invocations, knowledge retrievals, and memory interactions. Use this view to inspect inputs, outputs, token usage, and latency at every step.
You can manually evaluate responses using Human Reviews directly on individual spans. Human Reviews defined in the project are available on all spans automatically. Learn more in Human Review.

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Filtering Traces

Click next to the search bar to open the filter menu. Select a category to expand a searchable list of values and check one or more to apply.
Trace filters
When working with Agents, you can access traces directly from the Agent page with automatic filtering for that specific agent.
The following filter categories are available:
FilterDescription
AnnotationsFilter by annotation labels applied to traces.
EvalsFilter by evaluator results attached to traces.
IdentityFilter by the identity that triggered the trace.
MetadataFilter by metadata fields attached to traces.
ModelFilter by the model used in the trace.
NameFilter by trace name.
ProjectFilter by the project the trace belongs to.
ProviderFilter by the model provider.
Span IDFilter by a specific span identifier.
Thread IDFilter by the thread the trace belongs to.
Total CostFilter by the total cost of the trace.
Total TokensFilter by the total token count of the trace.
Trace IDFilter by a specific trace identifier.
Multiple filters can be combined. To remove a filter, click next to it in the filter bar.

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Configuring Column

Columns can be enabled and toggled to display more available data in the traces list.

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Creating Custom Views

Save frequently used filter combinations as reusable views:
  1. Set your desired filters
  2. Click All Rows (top right)
  3. Select Create New View
  4. Give your view a title
  5. Optionally check Set view private (default is shared with project members)
  6. Your filtered view is now saved and accessible from the All Rows dropdown

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Threads

You can visualize conversation past as Threads, to learn more see Threads.