comet-ml--opik/apps/opik-documentation/documentation/fern/docs/tracing/log_chat_conversations.mdx

---
headline: Log conversations | Opik Documentation
og:description: Capture and analyze chat conversations on Opik, enabling better tracking
  of user interactions and improving chatbot performance.
og:site_name: Opik Documentation
og:title: Log Conversations Effectively - Opik
title: Log conversations
canonical-url: https://www.comet.com/docs/opik/tracing/advanced/log_chat_conversations
---

You can log chat conversations to the Opik platform and track the full conversations
your users are having with your chatbot. Threads allow you to group related traces together, creating a conversational flow that makes it easy to review multi-turn interactions and track user sessions.

<Frame>
  <img src="/img/tracing/chat_conversations.png" />
</Frame>

## Understanding Threads

Threads in Opik are collections of traces that are grouped together using a unique `thread_id`. This is particularly useful for:

- **Multi-turn conversations**: Track complete chat sessions between users and AI assistants
- **User sessions**: Group all interactions from a single user session
- **Conversational agents**: Follow the flow of agent interactions and tool usage
- **Workflow tracking**: Monitor complex workflows that span multiple function calls

The `thread_id` is a user-defined identifier that must be unique per project. All traces with the same `thread_id` will be grouped together and displayed as a single conversation thread in the Opik UI.

## Logging conversations

You can log chat conversations by specifying the `thread_id` parameter when using either the low level SDK, Python decorators, or integration libraries:

<CodeBlocks>
    ```typescript title="Typescript SDK" language="typescript"
    import { Opik } from "opik";

    const client = new Opik({
    apiUrl: "https://www.comet.com/opik/api", // Only required if you are using Opik Cloud
    apiKey: "your-api-key",
    projectName: "your-project-name",
    workspaceName: "your-workspace-name", // Optional
    });

    const threadId = "your-thread-id"; // any unique string per conversation

    // Option A: set on trace creation
    const trace = client.trace({
        name: "chat turn",
        input: { user: "Hi there" },
        output: { assistant: "Hello!" },
        threadId
    });
    ```

    ```python title="Python decorators" language="python"
    import opik
    from opik import opik_context

    @opik.track
    def chat_message(input):
        return "Opik is an Open Source GenAI platform"

    chat_message("What is Opik ?", opik_args={"trace": {"thread_id": "f174a"}})

    # Alternatively, using the opik_context module
    @opik.track
    def chat_message(input):
        thread_id = "f174a"
        opik_context.update_current_trace(
            thread_id=thread_id
        )
        return "Opik is an Open Source GenAI platform"

    chat_message("What is Opik ?", thread_id)
    ```

    ```python title="Python SDK"
    import opik

    opik_client = opik.Opik()

    thread_id = "55d84"

    # Log a first message
    trace = opik_client.trace(
        name="chat_conversation",
        input="What is Opik?",
        output="Opik is an Open Source GenAI platform",
        thread_id=thread_id
    )
    ```

    ```python title="LangGraph integration"
    # LangGraph automatically uses its thread_id as Opik thread_id
    from langgraph.graph import StateGraph
    from opik.integrations.langchain import OpikTracer

    # Create your LangGraph workflow
    graph = StateGraph(...)
    compiled_graph = graph.compile()

    # Configure with thread_id for conversation tracking
    thread_id = "user-conversation-789"
    config = {
        "callbacks": [OpikTracer(project_name="langgraph-conversations")],
        "configurable": {"thread_id": thread_id}
    }

    # First turn in conversation
    result1 = compiled_graph.invoke(
        {"messages": [{"role": "user", "content": "What is machine learning?"}]},
        config=config
    )

    # Follow-up turn in same conversation thread
    result2 = compiled_graph.invoke(
        {"messages": [{"role": "user", "content": "Can you give me an example?"}]},
        config=config
    )
    ```

    ```python title="ADK integration"
    # ADK automatically maps session_id to thread_id
    from opik.integrations.adk import OpikTracer
    from google.adk import sessions as adk_sessions, runners as adk_runners

    # Create ADK session for conversation tracking
    session_service = adk_sessions.InMemorySessionService()
    session = session_service.create_session_sync(
        app_name="my_chatbot",
        user_id="user_123",
        session_id="conversation_456"  # This becomes the thread_id in Opik
    )

    opik_tracer = OpikTracer(project_name="adk-conversations")
    runner = adk_runners.Runner(
        agent=your_agent,
        app_name="my_chatbot",
        session_service=session_service
    )

    # First message - automatically grouped by session_id
    result1 = runner.run(
        user_id="user_123",
        session_id="conversation_456",
        new_message="What is machine learning?"
    )

    # Follow-up message - same conversation thread
    result2 = runner.run(
        user_id="user_123",
        session_id="conversation_456",
        new_message="Can you give me an example?"
    )
    ```

    ```python title="OpenAI Agents"
    # Using trace context manager with group_id for threading
    import uuid
    from opik import trace

    thread_id = str(uuid.uuid4())

    # First conversation turn
    with trace(workflow_name="Agent Conversation", group_id=thread_id):
        result1 = await Runner.run(agent, "What is machine learning?")
        print(result1.final_output)

    # Follow-up turn in same conversation
    with trace(workflow_name="Agent Conversation", group_id=thread_id):
        # Continue conversation with context
        new_input = result1.to_input_list() + [
            {"role": "user", "content": "Can you give me an example?"}
        ]
        result2 = await Runner.run(agent, new_input)
        print(result2.final_output)
    ```

</CodeBlocks>

<Note>
  The input to each trace will be displayed as the user message while the output will be displayed as the AI assistant
  response.
</Note>

## Thread ID Best Practices

### Generating Thread IDs

Choose a thread ID strategy that fits your application:

<CodeBlocks>
    ```python title="User session based"
    import uuid
    import opik

    # Generate unique thread ID per user session
    user_id = "user_12345"
    session_start_time = "2024-01-15T10:30:00Z"
    thread_id = f"{user_id}-{session_start_time}"

    @opik.track
    def process_user_message(message, user_id):
        return "Response to: " + message

    process_user_message("What is Opik ?", opik_args={"trace": {"thread_id": thread_id}})
    ```

    ```python title="UUID based"
    import uuid
    import opik

    # Generate random UUID for each conversation
    thread_id = str(uuid.uuid4())  # e.g., "f47ac10b-58cc-4372-a567-0e02b2c3d479"

    @opik.track
    def start_conversation(initial_message):
        return f"Processing: {initial_message}"

    start_conversation("What is Opik ?", opik_args={"trace": {"thread_id": thread_id}})
    ```

    ```python title="Timestamp based"
    import time
    import opik

    # Use timestamp for time-based grouping
    thread_id = f"conversation-{int(time.time())}"

    @opik.track
    def handle_conversation_turn(message):
        return f"Response to: {message}"

    handle_conversation_turn("What is Opik ?", opik_args={"trace": {"thread_id": thread_id}})
    ```

</CodeBlocks>

### Integration-Specific Threading

Different integrations handle thread IDs in various ways:

<CodeBlocks>
    ```python title="LangChain"
    from opik.integrations.langchain import OpikTracer

    # Set thread_id at tracer level - applies to all traces
    opik_tracer = OpikTracer(
        project_name="my-chatbot",
        thread_id="conversation-123"
    )

    # Or pass dynamically via metadata
    chain.invoke(
        {"input": "Hello"},
        config={
            "callbacks": [opik_tracer],
            "metadata": {"thread_id": "dynamic-conversation-456"}
        }
    )
    ```

    ```python title="LangGraph"
    from opik.integrations.langchain import OpikTracer

    # LangGraph automatically uses its thread_id as Opik thread_id
    thread_id = "langgraph-conversation-789"
    config = {
        "callbacks": [OpikTracer()],
        "configurable": {"thread_id": thread_id}
    }

    result = compiled_graph.invoke(input_data, config=config)
    ```

    ```python title="OpenAI Agents"
    import uuid
    from opik import trace

    # Use trace context manager with group_id for threading
    thread_id = str(uuid.uuid4())

    with trace(workflow_name="Agent Conversation", group_id=thread_id):
        # All agent interactions within this context share the thread_id
        result1 = await Runner.run(agent, "First question")
        result2 = await Runner.run(agent, "Follow-up question")
    ```

    ```python title="GenAI"
    from google import genai
    from opik.integrations.genai import track_genai

    client = genai.Client()
    gemini_client = track_genai(client, project_name="opik_args demo")

    response = gemini_client.models.generate_content(
        model="gemini-2.0-flash",
        contents="What is Opik?",
        opik_args={"trace": {"thread_id": "f174a"}}
    )
    ```

    ```python title="OpenAI"
    import openai

    from opik.integrations.openai import track_openai

    client = openai.OpenAI()
    wrapped_client = track_openai(
        openai_client=client,
        project_name="opik_args demo",
    )
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Opik?"},
    ]

    _ = wrapped_client.responses.create(
        model="gpt-4o-mini",
        input=messages,
        opik_args={"trace": {"thread_id": "f174a"}}
    )
    ```

</CodeBlocks>

## Reviewing conversations

Conversations can be viewed at a project level in the `threads` tab. All conversations are tracked and by clicking on the thread ID you will be able to
view the full conversation.

The thread view supports markdown making it easier for you to review the content that was returned to the user. If you would like to dig in deeper, you
can click on the `View trace` button to deepdive into how the AI assistant response was generated.

By clicking on the thumbs up or thumbs down icons, you can quickly rate the AI assistant response. This feedback score will be logged and associated to
the relevant trace. By switching to the trace view, you can review the full trace as well as add additional feedback scores through the annotation
functionality.

<Frame>
  <img src="/img/tracing/chat_conversations_actions.png" />
</Frame>

## Scoring conversations

You can assign conversation-level feedback scores to threads at any time. Threads are aggregated traces
that are created when tracking agents or simply traces interconnected by a `thread_id`.

<Frame>
  <img src="/img/tracing/chat_conversations_score.png" />
</Frame>

In the conversation list, you can see the feedback scores associated to each thread.

<Frame>
  <img src="/img/tracing/chat_conversations_score_list.png" />
</Frame>

You can also tag a thread and add comments to it. This is useful to add additional context during the review process or investigate a specific conversation.

<Frame>
  <img src="/img/tracing/chat_conversation_tags_comments.png" />
</Frame>

### Thread Online Scoring Rule Cooldown Period

For thread-level online evaluation rules (automatic scoring), Opik waits for a "cooldown period" after the last activity
in a thread before running the rules. This gives conversations time to settle before automatic evaluation.

<Note>
  By default, the cooldown period is 15 minutes. You can change this value by setting the
  `OPIK_TRACE_THREAD_TIMEOUT_TO_MARK_AS_INACTIVE` environment variable (if you are using the Opik self-hosted version).
  On cloud, you can change this setting at workspace level under "Thread online scoring rule cooldown period".
</Note>

#### Behavior When Adding Traces to Existing Threads

When a new trace is added to an existing thread, the following happens:

- **Existing feedback scores are preserved**: Any manual feedback scores or online evaluation scores you have added remain intact.
- **The cooldown timer restarts**: The timer resets from the moment the new trace is added, ensuring online evaluation waits for the full cooldown period before scoring the updated thread.
- **Online evaluation re-runs**: Once the cooldown period expires, thread-level online scoring rules will automatically evaluate the complete conversation again. If a new score is logged with the same name as an existing score, the existing score is updated.

## Advanced Thread Features

### Filtering and Searching Threads

You can filter threads using the `thread_id` field in various Opik features:

#### In Data Export

When exporting data, you can filter by `thread_id` using these operators:

- `=` (equals), `!=` (not equals)
- `contains`, `not_contains`
- `starts_with`, `ends_with`
- `>`, `<` (lexicographic comparison)

#### In Thread Evaluation

You can evaluate entire conversation threads using the thread evaluation features. This is particularly useful for:

- Conversation quality assessment
- Multi-turn coherence evaluation
- User satisfaction scoring across complete interactions

### Thread Management

Threads can have traces added to them at any time, and you can add feedback scores, comments, and tags
to threads regardless of whether new traces are still being added.

### Programmatic Thread Management

You can also manage threads programmatically using the Opik SDK:

<CodeBlocks>
    ```python title="Python" language="python"
    import opik

    # Initialize client
    client = opik.Opik()

    # Search for threads by various criteria
    threads = client.search_traces(
        project_name="my-chatbot",
        filter_string='thread_id contains "user-session"'
    )

    # Get specific thread content
    for trace in threads:
        if trace.thread_id:
            thread_content = client.get_trace_content(trace.id)
            print(f"Thread: {trace.thread_id}")
            print(f"Input: {thread_content.input}")
            print(f"Output: {thread_content.output}")

    # Add feedback scores to thread traces
    for trace in threads:
        trace.log_feedback_score(
            name="conversation_quality",
            value=0.8,
            reason="Good multi-turn conversation flow"
        )
    ```

</CodeBlocks>

## Next steps

Once you have added observability to your multi-turn agent, why not:

1. [Run offline multi-turn conversation evaluation](/v1/evaluation/evaluate_threads)
2. [Create online evaluation rules](/v1/production/rules) to score your multi-turn conversations in
   production