# Log conversations

> Capture and analyze chat conversations on Opik, enabling better tracking of user interactions and improving chatbot performance.

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="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversations.png" />
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## 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", // Only required if you are using Opik Cloud
  });

  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="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversations_actions.png" />
</Frame>

## Scoring conversations

It is possible to assign conversation level feedback scores. For that, you need to understand how threads work in Opik. Threads are aggregated traces
that are created when tracking agents or simply traces interconnected by a `thread_id`. In order to score a conversation, we need to ensure that the
thread is inactive, meaning that no new traces are being created.

<Note>
  By default, threads are marked as inactive after 15 minutes of inactivity. 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.
</Note>

Threads are automatically marked as inactive after the timeout period and you can also manually mark a thread as inactive via UI using the `Status` button on top right corner of the thread view.

<Frame>
  <img src="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversations_mark_as_inactive.png" />
</Frame>

Once a thread is inactive, you can assign a feedback score to the thread. This score will be associated to the thread and will be displayed in the thread view.

<Frame>
  <img src="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversations_score.png" />
</Frame>

And in the conversation list, you can see the feedback score associated to the thread.

<Frame>
  <img src="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversations_score_list.png" />
</Frame>

<Note>
  Important: The human feedback scores are removed in case the thread is re-activated. This is to avoid any confusion
  and to ensure that the feedback scores are always based on the full context of the conversation.
</Note>

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="https://files.buildwithfern.com/https://opik.docs.buildwithfern.com/docs/opik/2026-01-30T17:33:07.444Z/img/tracing/chat_conversation_tags_comments.png" />
</Frame>

## 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 Lifecycle Management

Threads have a lifecycle that affects how you can interact with them:

1. **Active**: New traces can be added to the thread
2. **Inactive**: No new traces can be added, thread can be scored

<Note>
  Threads automatically become inactive after 15 minutes of no new trace activity. This timeout can be configured: -
  **Self-hosted**: Set `OPIK_TRACE_THREAD_TIMEOUT_TO_MARK_AS_INACTIVE` environment variable - **Cloud**: Configure at
  workspace level in settings
</Note>

### 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>

You can mark a thread as inactive by calling [`TracesClient.close_trace_thread`](https://www.comet.com/docs/opik/python-sdk-reference/rest_api/clients/traces.html#opik.rest_api.traces.client.TracesClient.close_trace_thread)

## Next steps

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

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