Tracing Core Concepts

Opik’s tracing system gives you full visibility into what your LLM application or agent is doing — every call, every step, every intermediate result. There are three building blocks you need to understand:

1. Trace: A complete execution path for a single interaction with an LLM or agent
2. Span: An individual operation or step within a trace
3. Thread: A collection of related traces that form a conversation or multi-turn workflow

Traces

A trace represents a complete execution path for a single interaction with an LLM or agent. Think of it as a detailed record of everything that happened during one request-response cycle — inputs, outputs, timing, token usage, and any intermediate steps.

Key characteristics

• Unique identity: Each trace has a unique identifier for tracking and referencing
• Complete context: All information needed to understand what happened during the interaction
• Timing information: When the interaction started, ended, and how long each part took
• Input/output data: The exact prompts sent to the LLM and the responses received
• Metadata: Model used, temperature settings, custom tags, and more

Common uses

• Debugging: When an LLM produces unexpected output, examine the trace to understand what went wrong
• Performance analysis: Identify slow operations by analyzing trace timing
• Cost tracking: Monitor token usage and costs for each interaction
• Quality assurance: Review traces to ensure expected behavior

Spans

A span represents an individual operation or step within a trace. While a trace shows the complete picture, spans break it down into measurable components. Spans are hierarchical — they can contain other spans, creating a tree structure within the trace.

Key characteristics

• Hierarchical structure: Spans can contain child spans, forming a tree within a trace
• Specific operations: Each span represents a distinct action — an API call, a function, a data transformation
• Precise timing: Start and end times for each operation
• Custom attributes: Additional metadata specific to the operation

Common span types

• LLM Calls: Individual requests to language models
• Function Calls: Tool or function invocations within an agent
• Data Processing: Transformations or manipulations of data
• External API Calls: Requests to third-party services

Example span hierarchy

Trace: "Customer Support Chat"
├── Span: "Parse User Intent"
├── Span: "Query Knowledge Base"
│   ├── Span: "Search Vector Database"
│   └── Span: "Rank Results"
├── Span: "Generate Response"
│   ├── Span: "LLM Call: GPT-4"
│   └── Span: "Post-process Response"
└── Span: "Log Interaction"

Threads

A thread is a collection of related traces that form a coherent conversation or multi-turn workflow. Threads are essential for chat applications and agents where context evolves across multiple interactions.

Key characteristics

• Conversation context: Maintains the flow of multi-turn interactions
• Trace grouping: Organizes related traces under a single thread identifier
• Chronological ordering: Traces within a thread are ordered by time
• Cross-trace analysis: Enables analysis of patterns across related interactions

When to use threads

• Chat applications: Group all messages in a conversation
• Multi-step workflows: Track complex processes that span multiple LLM calls
• User sessions: Organize all interactions from a single user session
• Agent conversations: Follow the complete interaction between an agent and a user

Threads are created by setting a thread_id on your traces:

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import opik
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client = opik.Opik()
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client.trace(
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    name="chat-turn-1",
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    thread_id="user-session-abc123",
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    input={"message": "Hello"},
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    output={"response": "Hi! How can I help?"},
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)

Best practices

Next steps

• Log traces — Capture traces in your application
• Log agent graphs — Trace agent-based applications with full span trees
• Annotate traces — Add scores and feedback to traces
• Cost tracking — Monitor token usage and costs