> ## 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.

# Multi-Chain Prompt with Traces

> Chain multiple prompts using traces to group related completions.

### 1. Initialization of Freeplay & Clients

### 2. Session & Trace Creation

Create the session and trace, optionally passing additional information to each such as metadata and agent name

### 3. Prompt #1 - Call & Record

### 4. Prompt #2 - Call & Record

### 5. Record the final output to the Trace

Recording the final output to the trace allows you to record evals across the whole trace and the final output (needs to be a str)

## Examples

<CodeGroup>
  ```python Python theme={null}
  import os
  import time
  from freeplay import Freeplay, CallInfo, RecordPayload
  from openai import OpenAI
  import re

  # Configure environment variables for API access
  project_id = os.environ.get("FREEPLAY_PROJECT_ID")
  freeplay_api_key = os.environ.get("FREEPLAY_API_KEY")
  freeplay_url = os.environ.get("FREEPLAY_URL") # ie "https://app.freeplay.ai/api"
  openai_api_key = os.environ.get("OPENAI_API_KEY")

  # Initialize OpenAI client
  openai = OpenAI(api_key=openai_api_key)

  # Initialize Freeplay client with development API endpoint
  fp_client = Freeplay(
      freeplay_api_key=freeplay_api_key,
      api_base=freeplay_url
  )

  # Create a new Freeplay session to group related completions
  session = fp_client.sessions.create({})

  user_input = "My favorite artist is Taylor Swift"

  # Create a trace to combine multiple prompts into a single workflow
  trace_info = session.create_trace(
      input=user_input, # Commonly user question but any str input to a trace
      agent_name="musicAgent", # Optionally pass an agent name
      custom_metadata={
          "version": "1.0.8"
      }
  )

  # =============================================================================
  # Prompt 1: Generate Album Title
  # =============================================================================

  # Define variables for the album title generation prompt
  prompt_vars_a = {"pop_star": "Taylor Swift"}

  # Fetch and format the album generation prompt template
  formatted_prompt_a = fp_client.prompts.get_formatted(
      project_id=project_id,           # Freeplay project identifier
      template_name="album_bot",       # Name of the prompt template
      environment="latest",            # Environment tag for prompt versioning
      variables=prompt_vars_a          # Variables to interpolate into the prompt
  )

  # Execute the OpenAI completion for album title generation
  start = time.time()
  chat_completion_a = openai.chat.completions.create(
      messages=formatted_prompt_a.llm_prompt,
      model=formatted_prompt_a.prompt_info.model,
      **formatted_prompt_a.prompt_info.model_parameters
  )
  end = time.time()

  # Extract the generated album name from the response
  album_name = chat_completion_a.choices[0].message.content
  print(f"Album Name: {album_name}")

  # Record the first completion to Freeplay for tracking and analysis
  fp_client.recordings.create(
      RecordPayload(
          all_messages=formatted_prompt_a.all_messages(new_message={
              "role": chat_completion_a.choices[0].message.role,
              "content": chat_completion_a.choices[0].message.content
          }),
          inputs=prompt_vars_a,
          session_info=session.session_info,
          prompt_info=formatted_prompt_a.prompt_info,
          call_info=CallInfo.from_prompt_info(formatted_prompt_a.prompt_info, start, end),
          trace_info=trace_info
      ),
  )

  # =============================================================================
  # Prompt 2: Generate Song List for Album
  # =============================================================================

  # Define variables for the song list generation prompt (includes generated album name)
  prompt_vars_b = {"album_name": album_name, "pop_star": "Taylor Swift"}

  # Fetch and format the song list generation prompt template
  formatted_prompt_b = fp_client.prompts.get_formatted(
      project_id=project_id,
      template_name="song_bot",
      environment="latest",
      variables=prompt_vars_b
  )

  # Execute the OpenAI completion for song list generation
  start = time.time()
  chat_completion_b = openai.chat.completions.create(
      messages=formatted_prompt_b.llm_prompt,
      model=formatted_prompt_b.prompt_info.model,
      **formatted_prompt_b.prompt_info.model_parameters
  )
  end = time.time()

  song_track = chat_completion_b.choices[0].message.content

  # Record the second completion to Freeplay for tracking and analysis
  fp_client.recordings.create(
    RecordPayload(
  				project_id=project_id,
          all_messages=formatted_prompt_b.all_messages(new_message={
              "role": chat_completion_b.choices[0].message.role,
              "content": song_track
          }),
          inputs=prompt_vars_b,
          session_info=session.session_info,
          prompt_version_info=formatted_prompt_b.prompt_info,
          call_info=CallInfo.from_prompt_info(formatted_prompt_b.prompt_info, start, end),
          trace_info=trace_info
      )
  )

  # Record the final output to complete the trace
  trace_info.record_output(
      project_id=project_id,
      output=song_track, # Final trace output (str)
      # Optional code evals logged to the trace
      eval_results={
        "sentiment": 0.7,
        "songTrackLength": len(re.findall(r'\n', song_track)) + 1, # count number of lines
      }
  )
  ```
</CodeGroup>