prompt-token-trim-py 0.1.0

Trim prompt messages to fit a token budget while preserving priority. Python port of @mukundakatta/prompt-token-trim.

prompt-token-trim-py

Trim prompt messages to fit a token budget while preserving priority. Sort messages by priority (descending), accept each if it fits, then re-emit them in original order. Zero runtime dependencies.

Python port of @mukundakatta/prompt-token-trim.

Note: see also agentfit-py. They look similar; this one is message-level priority trimming (drop a single message if it doesn't fit). agentfit-py is whole-history strategy fitting (drop-oldest, drop-middle, priority, with system-message preservation and partial-result modes).

Install

pip install prompt-token-trim-py

Quick start

from prompt_token_trim import trim

messages = [
    {"role": "system", "content": "You are a helpful assistant.", "priority": 10},
    {"role": "user",   "content": "Tell me about Pluto.",          "priority": 5},
    {"role": "assistant", "content": "Pluto is a dwarf planet.",   "priority": 5},
    {"role": "user",   "content": "And Mars?",                     "priority": 1},
]

result = trim(messages, budget=20, preserve_system=True)

result.messages  # list[dict] in original order, kept under budget
result.tokens    # int -- tokens consumed
result.dropped   # int -- count of messages dropped

API

trim(messages, budget, *, preserve_system=True) -> TrimResult

• messages: list of dicts with role, content, optional priority (defaults to 0).
• budget: token budget (ceil(len(content) / 4) heuristic per message).
• preserve_system: keep all role == "system" messages even if they don't fit (they consume budget anyway). Defaults to True.

TrimResult is a dataclass with:

• messages: kept messages, in their original order.
• tokens: total tokens consumed.
• dropped: count of messages that did not survive.

The JS sibling exposes trimMessages({maxTokens}) -- the trim_messages(messages, *, max_tokens=...) alias is provided for parity.

License

MIT