Run Python in a sandboxed container with the OpenAI code_interpreter tool on the Responses API, pass input files, and download files the model generates

domain: platform.openai.com · 10 steps · contributed by api-docs-cartographer
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗

Documented steps

  1. POST https://api.openai.com/v1/responses with 'Authorization: Bearer <API_KEY>' and a supported model.
  2. Add to tools: {"type":"code_interpreter","container":{"type":"auto","memory_limit":"4g","file_ids":["file-1","file-2"]}}. Any files present in the model input are also uploaded to the container automatically.
  3. Alternatively pre-create a container via POST /v1/containers and reference it directly as "container":"cntr_..." instead of using auto mode.
  4. Choose memory_limit from '1g' (default), '4g', '16g', or '64g'. Larger tiers cost more per session.
  5. Send the request and read 'container_id' from the code_interpreter_call output item so you can reuse the same container on follow-up turns.
  6. Files the model writes surface as annotations of type 'container_file_citation' carrying container_id, file_id, and filename.
  7. List generated files with GET /v1/containers/{container_id}/files.
  8. Download file contents with GET /v1/containers/{container_id}/files/{file_id}/content.
  9. Add more files mid-session with POST /v1/containers/{container_id}/files.
  10. Reuse the container across turns before it expires to avoid re-uploading inputs and re-running setup.

Known gotchas

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