LLM API for repository refactoring

Repository refactoring in a production application

An engineering team is extracting billing logic from a monolith while keeping existing HTTP and event contracts stable. Sol first produces an impact map: owners, callers, data writes, migrations, tests, and rollback points. No code is requested until that map is approved.

The implementation then runs as several small requests. Each request changes one dependency boundary, produces a patch, and ends with the exact commands required to prove compatibility.

How the repository refactoring workflow operates

• Generate and approve the impact map.
• Freeze public interfaces and baseline behavior tests.
• Implement one dependency boundary per request.
• Run compatibility and migration checks after every patch.
• Stop and re-plan when an unlisted dependency appears.

Call gpt-5.6-sol through LLMFly AI

Send the request from your server. Replace the example content and placeholder tool schema with data and tools from your application.

curl https://app.llmfly.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMFLY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.6-sol",
    "messages": [
      {"role": "system", "content": "Plan a repository refactor before editing. Preserve the listed public contracts. First return an impact map and reversible checkpoints; do not produce code until a checkpoint is selected."},
      {"role": "user", "content": "Extract billing from the monolith while preserving the attached HTTP and event contracts."}
    ]
  }'

Why gpt-5.6-sol is the primary model

Sol is appropriate because a repository-scale refactor depends on long-range dependency reasoning and consistent decisions across checkpoints.

Use Terra for each bounded implementation checkpoint after Sol has produced the plan. There is no lower model for changing unreviewed architecture boundaries.

Acceptance checks for repository refactoring

Failures to handle before deployment

• Sending the entire repository without retrieval
• Mixing refactors with feature work
• Skipping intermediate tests
• Judging quality only by diff size

Returned output and run records

An approved impact map followed by one reversible unified diff per checkpoint.

Record the model ID, request ID, token usage, retries, validation result, and final disposition for every production run.