Kellogg LLM Batch
Reviewable LLM batch pipelines for research coding and structured extraction.
kllm-batch turns tabular research data into provider-ready requests for
OpenAI or Anthropic, preserves the material needed to reproduce each run, and
normalizes the responses for analysis. Preparation is deliberately separate
from execution so that researchers can inspect prompts, selected records, and
the estimated maximum cost before making a provider call.
Start here
New users should follow these pages in order:
Installation — install the command and configure provider credentials.
Basic workflow — create a project, run a pilot, and process a complete batch.
Project settings — understand and edit the generated configuration.
Grant-coding walkthrough — practice the workflow with ten synthetic grants.
The Advanced workflows guide covers configuration, retries, comparisons, recovery, and provenance. Use the CLI reference page as a compact command index. For per-record PNG or PDF inputs, see Attach one PNG or PDF per prepared request.
Important
init, validate, prepare, attach-files, audit, retry,
merge, and compare are local commands. submit contacts a provider and may incur
model charges. For batch runs, status, sync, and cancel also
contact the provider.
What the package protects
Source files are not overwritten.
Prepared requests are immutable and inspectable before submission.
Cost estimates and provider limits are checked during preparation.
Raw responses and retry attempts remain separate.
Results retain record-level provenance, while run-wide provenance is saved with the manifest.