Trimwise

Need to fit a long document into a small AI prompt budget? Trimwise picks excerpts from across it that fit the space you set aside for source text, instead of just cutting off the end. Add a question to steer the choice; the selected passages keep their original wording and order.

Documentation · PyPI · Forever free public API

Try it

You can use Trimwise without installing it via the forever free public API - https://trimwise.aatbit.com

Install Trimwise with Python 3.10–3.14:

python -m pip install trimwise

Here's the input and the question:

from trimwise import Trimmer

report = """# Incident report

The site went down at 09:14. Initial checks focused on the network.

## Root cause

An expired credential blocked the service.

## Prevention

Credentials will now rotate every 30 days.
"""

result = Trimmer().trim(
    report,
    limit=24,
    query="What caused the outage and how will it be prevented?",
)
print(result.text)
print(f"{result.output_count}/{result.limit} tokens")

Output from running that code:

## Root cause

An expired credential blocked the service.

## Prevention

Credentials will now rotate every 30 days.

23/24 tokens

The limit is a ceiling, not a target to fill. It can be measured in tokens (the default), words, or characters. result.spans also tells you where each kept piece came from in the original Python string.

Have more than one source?

Use trim_context() when several sources must share one limit. This input has an initial report and a follow-up:

from trimwise import ContextSource, Trimmer

sources = [
    ContextSource(
        text="Initial checks focused on the network. The network was healthy.",
        prefix="Incident: ",
    ),
    ContextSource(
        text="A retry loop ignored backoff settings. Service recovered at 14:32 UTC.",
        prefix="Follow-up: ",
    ),
]

result = Trimmer().trim_context(
    sources,
    limit=20,
    query="Which retry loop caused the outage, and when did service recover?",
)
print(result.text)
print(f"{result.output_count}/{result.limit} tokens")

Output from running that code:

Follow-up: A retry loop ignored backoff settings. Service recovered at 14:32 UTC.
20/20 tokens

Only the follow-up contributes to this result. You still get one result row per input source, including an empty row for the incident report. Source labels count toward this limit; your surrounding prompt instructions and answer space do not. See Many Sources, One Shared Limit for the full behavior.

Which strategy should you use?

Start with the default, auto. With a question, it matches relevant words (lexical). Without one, it makes a broader document excerpt (structural). Neither downloads a model.

If the answer may use different wording from your question, try semantic. hybrid combines word matching and semantic matching. Those two need your own embedding callback or the optional semantic model. The strategy guide has examples and tradeoffs.

What Trimwise does—and doesn't

Trimwise works on text you already have. It does not search for documents or rewrite them. Unlike token-pruning compressors, it keeps readable source pieces; see the research comparison for the tradeoff.

A tight limit can leave out evidence needed to answer a question. The limit applies to Trimwise's returned text, not your entire prompt, so leave room for instructions and the model's answer. Read the guarantees and limitations before relying on an excerpt for a high-stakes task.

How it did in one test

In a frozen 160-case test using the published 0.2.0 release, Trimwise Hybrid kept every annotated source passage in 66.9% of cases at a 512-token limit. This measures passage survival on that test—not answer quality or performance on every document. The benchmark report explains the setup, comparisons, and limits.

In a separate 93-case short-answer check at 256 tokens, GPT-5.4 Nano matched 43 reference answers with Hybrid's output, versus 41 with the full source (46.2% vs. 44.1%). GPT-5.4 Mini scored 46.2% vs. 45.2%; GPT-5.6 Luna tied at 45.2%. Each context received one model response. This automatic text match is a useful diagnostic, not a human correctness judgment or proof that trimming generally improves answers.

Open the full-size answer-match graph · How the answer check was scored

At 512 tokens, Hybrid's median warm trim time was 42.8 ms on the benchmark machine (Lexical: 6.4 ms). Those timings exclude cold model loading and are not portable speed guarantees.

For more detail, see Getting Started, Configuration and API, or the API Reference.

Trimwise is available under the MIT License and maintained by AATBIT Labs.