> ## Documentation Index
> Fetch the complete documentation index at: https://docs.freeplay.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Common Integration Patterns

> Implement common patterns like multi-turn chat, agent workflows, tool calls, and customer feedback.

### Multi-Turn Conversations

For chatbots and assistants, pass conversation history when fetching prompts:

<CodeGroup>
  ```python python theme={null}
  # Your conversation history
  history = [
      {'role': 'user', 'content': 'What is pasta?'},
      {'role': 'assistant', 'content': 'Pasta is an Italian dish...'}
  ]

  # Fetch prompt with history

  formatted_prompt = fp_client.prompts.get_formatted(
  project_id=project_id,
  template_name="chat-assistant",
  environment="latest",
  variables={"user_question": "How do I make it?"},
  history=history # Freeplay handles formatting
  )

  ```
</CodeGroup>

[See full multi-turn example →](/developer-resources/recipes/continuous-chat)

### Tool Calling

<CodeGroup>
  ```python python theme={null}
  if completion.choices[0].message.tool_calls:
      for tool_call in completion.choices[0].message.tool_calls:
          if tool_call.function.name == "weather_of_location":
              args = json.loads(tool_call.function.arguments)
              temperature = get_temperature(args["location"])

              tool_response_message = {
                  "tool_call_id": tool_call.id,
                  "role": "tool",
                  "content": str(temperature),
              }
              messages.append(tool_response_message)
  ```

  ```typescript typescript theme={null}
  // Append the completion to list of messages
  const messages = formattedPrompt.allMessages(completion.choices[0].message);

  if (completion.choices[0].message.tool_calls) {
    for (const toolCall of completion.choices[0].message.tool_calls) {
      if (toolCall.function.name === "weather_of_location") {
        const args = JSON.parse(toolCall.function.arguments);
        const temperature = getTemperature(args.location);

        const toolResponseMessage = {
          tool_call_id: toolCall.id,
          role: "tool",
          content: temperature.toString(),
        };

        messages.push(toolResponseMessage);
      }
    }
  }
  ```
</CodeGroup>

[See full tool calling example →](/developer-resources/recipes/using-tools-with-openai)

### Adding Custom Metadata

Track user IDs, feature flags, or any custom data:

<CodeGroup>
  ```python python theme={null}
  # Create session with metadata
  session = fp_client.sessions.create(
      custom_metadata={
          "user_id": "user_123",
          "environment": "production",
          "feature_flag": "new_ui_enabled"
      }
  )
  ```
</CodeGroup>

### Logging User Feedback

Capture thumbs up/down or other user reactions:

<CodeGroup>
  ```python python theme={null}
  # After the user rates your response
  fp_client.customer_feedback.update(
      completion_id=completion.completion_id,
      feedback={
          'thumbs_up': True,
          'user_comment': 'Very helpful!'
      }
  )
  ```
</CodeGroup>

[See full feedback example →](/freeplay-sdk/recording-completions#log-customer-feedback)

### Tracking Multi-Step Workflows

For agents and complex workflows, use traces to group related completions:

<CodeGroup>
  ```python python theme={null}
  # Create a trace for multi-step workflow
  trace_info = session.create_trace(
      input="Research and write a blog post about AI",
      agent_name="blog_writer",
      custom_metadata={"version": "2.0"}
  )

  # Log each LLM call with the trace_id

  # ... your LLM calls here ...

  # Record final output

  trace_info.record_output(
  project_id=project_id,
  output="[Final blog post content]"
  )

  ```
</CodeGroup>

[See full agent example →](/practical-guides/agents)

```
```