PromptThrift FAQs

Question 1

What are 'Pinned Facts' and how do they help?

Accepted Answer

Pinned Facts (or the 'Never-Compress List') are a unique PromptThrift feature that guarantees critical context is never lost during compression. You can 'pin' essential information – like user preferences or key project details – ensuring these facts are always included in every compressed summary, regardless of conversation length.

Question 2

How does PromptThrift ensure privacy and security?

Accepted Answer

PromptThrift prioritizes privacy and security by processing all data locally on your machine. Its Gemma 4 compression runs entirely via Ollama on your hardware, and it includes a post-compression security sanitizer that strips potential prompt injection patterns from summaries, with no persistent storage or telemetry.

Question 3

What is PromptThrift and how does it reduce LLM costs?

Accepted Answer

PromptThrift is an intelligent tool designed to cut your Large Language Model (LLM) API costs by 70-90%. It achieves this by intelligently compressing your conversation history and routing requests to the cheapest suitable LLMs across 14 different models, ensuring you only pay for what's essential.

Question 4

What is 'Gemma 4 local compression' and why is it important?

Accepted Answer

Gemma 4 local compression leverages a locally run Gemma 4 LLM (via Ollama) to create highly intelligent, nuanced summaries of your conversation history. This provides excellent compression quality (70-90% token reduction) for free, eliminating API costs associated with compression while running securely on your own hardware.

Question 5

Can PromptThrift work with my existing LLM applications?

Accepted Answer

Yes, PromptThrift integrates seamlessly as an MCP (Model Context Protocol) server. This makes it compatible with applications like Claude Desktop, Cursor, Windsurf, and any other tool that supports the Model Context Protocol, allowing easy adoption into your existing development workflows.

PromptThrift

PromptThrift

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主要功能

使用案例