What does provenance-aware priority handling do?

It prevents untrusted RAG, tool, and assistant content from gaining protected priority solely through imperative or safety-critical wording.

Does LLMSlim prevent prompt injection?

No. It mitigates compression-induced instruction elevation and applications still need defense in depth.

Does LLMSlim require remote calls?

The default extractive strategy runs locally. Rewrite and hybrid strategies use a caller-supplied provider.

Now part of the Sarvam Startup Program Now part of Zoho for Startups

Your context.
Only what matters.

Less noise between your ideas and your AI.
An open-source Python library for thoughtful context compression.

Start building Try it in Studio

pip install llmslim

LOCAL BY DEFAULTOPEN SOURCE, ALWAYSA closer look ↓

A little less goes a long way.

Good context gives your model direction.
The rest just takes up space.

The deployment is scheduled for Thursday at 14:00 UTC. The engineering team discussed the deployment during the weekly planning meeting. All API keys must be stored in environment variables. The discussion also covered the general background of the project and a review of previous meetings. Roll back if the error rate exceeds 2 percent.

Illustrative excerpt · word counts, not tokensA deployment note with room to trim.

Keep it relevant.

Rank and select content around your question, before it reaches the model.

Keep its origins.

Treat instructions, retrieved documents, and tool responses according to their roles.

Keep control.

Choose your retention target. Inspect the output. Evaluate it on your own workload.

Read the evaluations and their limitations

Sarvam Startup Program

Zoho for Startups

×LLMSlim

Part of the Sarvam Startup Program & Zoho for Startups

A shared beginning.
Built from India.

We’re building LLMSlim with support from the Sarvam Startup Program and Zoho for Startups. Start with local context compression, then bring Sarvam’s models into your application.

Explore the integration

About Sarvam’s program About Zoho for Startups

Small library. Familiar workflow.

A few lines.
Then back to building.

Add LLMSlim before your model call. Run extraction locally, or bring a provider for rewrite and hybrid strategies.

Python-native Provider-agnostic MIT licensed

Open the documentation

from llmslim import compress

# Keep the useful. Leave the rest.
result = compress(
    long_context,
    target_ratio=0.5,
)

# Ready for your model
context = result.compressed_text

Runs locallyNo API key needed

Before you start.

A few good
questions.

Does it need an API key?+

Not for local extraction. LLMSlim’s default extractive strategy runs in your Python application. Rewrite and hybrid strategies use a provider you supply.

Does it work with my model?+

LLMSlim prepares text before your model call. Use the output with Sarvam, OpenAI, Anthropic, Gemini, or a local model. Your application keeps control of the request.

How much should I compress?+

Start with a conservative target ratio and evaluate the answers your application produces. The right setting depends on the documents, question, and model. Compression can remove useful information; there is no universal quality guarantee.

What do the startup program announcements mean?+

LLMSlim is part of both the Sarvam Startup Program and Zoho for Startups. Our Sarvam integration guide shows how to combine local context compression with the official Sarvam Python SDK.

Make a little room.

Your next model call is a good place to start.

Get started View on GitHub

Your context.Only what matters.

Good context gives your model direction. The rest just takes up space.