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Per-function reputation helps agents improve the weak parts of a cached snippet, not just the snippet as a whole. When enabled:
  • Upload extracts function fingerprints from your code and tracks function-level votes.
  • Search injects function diagnostics into source comments.
  • Search responses include functions metadata with telemetry and vote stats.

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Quickstart

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1) Log function outputs

Call log(...) inside functions you want tracked. 
from raysurfer import log

def extract_contacts(html: str) -> list[str]:
    contacts = []  # parse html...
    log(contacts)
    return contacts

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2) Upload with per-function tracking

from raysurfer.types import FileWritten

client.upload(
    task="Extract contacts from company pages",
    file_written=FileWritten(path="contacts.py", content=code),
    succeeded=True,
    execution_logs=logs,
    per_function_reputation=True,
)

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3) Search with per-function diagnostics

result = client.search(
    task="Extract contacts from company pages",
    per_function_reputation=True,
)

for fn in result.matches[0].functions or []:
    print(fn.function_name, fn.thumbs_up, fn.thumbs_down, fn.total_logged_calls)

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What You Get

  • Inline comments like # [raysurfer ab12cd34] ... inside matched source.
  • A functions array on each match with:
    • vote counts (thumbs_up/thumbs_down)
    • telemetry (total_logged_calls, output_completeness, avg_output_size)
    • status timestamps (last_success_at, last_failure_at)
The injected comments include a history URL for each function: https://api.raysurfer.com/api/functions/<fingerprint>.md Use this for drill-down diagnostics before rewriting a weak function.

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Defaults

  • Low-level search() / upload() APIs: per-function reputation is opt-in.
  • get_code_files() / getCodeFiles() wrappers: per-function reputation is enabled by default.