import os
from dataframer import Dataframer

client = Dataframer(
    api_key=os.environ.get("DATAFRAMER_API_KEY"),  # This is the default and can be omitted
)
run = client.dataframer.runs.retrieve(
    "run_id",
)
print(run.id)

{
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "status": "PENDING",
  "spec_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "spec_name": "<string>",
  "spec_version": 123,
  "dataset_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "dataset_name": "<string>",
  "dataset_type": "SINGLE_FILE",
  "samples_completed": 123,
  "samples_failed": 123,
  "total_cost": 123,
  "created_at": "2023-11-07T05:31:56Z",
  "created_by_email": "<string>",
  "completed_at": "2023-11-07T05:31:56Z",
  "runtime_params": {
    "number_of_samples": 123,
    "generation_model": "<string>",
    "outline_model": "<string>",
    "revision_model": "<string>",
    "revision_types": [
      "<string>"
    ],
    "filtering_types": [
      "<string>"
    ],
    "max_revision_cycles": 123,
    "seed_shuffling_level": "none",
    "max_examples_in_prompt": 123,
    "unified_multifield": true,
    "generation_thinking_budget": 123,
    "outline_thinking_budget": 123,
    "revision_thinking_budget": 123,
    "skip_outline": true,
    "tools": [
      "<string>"
    ]
  },
  "generated_files": [
    {
      "id": "<string>",
      "path": "<string>",
      "file_type": "<string>",
      "size_bytes": 123
    }
  ]
}