Integrations
Parallel AI
Trace Parallel AI task and search operations in Muster via the @observe decorator.
Parallel AI runs research and search tasks at scale. Wrap its calls with Muster's @observe() decorator to capture inputs, outputs, and execution metrics.
Setup
%pip install langfuse parallel-web openaiimport os
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_BASE_URL"] = "https://app.getmuster.io"
os.environ["PARALLEL_API_KEY"] = "..."Trace a Parallel task
from parallel import Parallel
from langfuse import observe
@observe(as_type="retriever")
def parallel_task(input: str):
client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
task_run = client.task_run.create(input=input)
return client.task_run.result(task_run.run_id)Combined: search + summarise
from langfuse.openai import OpenAI
@observe()
def search_and_summarize(objective: str):
parallel_client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
results = parallel_client.beta.search(objective=objective)
openai_client = OpenAI()
return openai_client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": f"Summarise:\n{results}"}],
)