chu2bard

chu2bard / ctxpack

Public

Context window compression and management utilities

compression context-window llm python summarization
15
0
89% credibility
Found Feb 11, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python library that automatically manages and compresses AI conversation histories to prevent them from exceeding memory limits using strategies like recent-message focus, importance trimming, or summarization.

How It Works

1
🔍 Discover Chat Helper

You find a handy tool that keeps AI conversations smart and memory-efficient even after long chats.

2
📦 Add to Your Project

You easily include this chat memory manager in your AI chat setup.

3
Pick Management Style
🪟
Keep Recent Chats

Focus on the newest messages and let old ones slide away.

✂️
Trim Unimportant Parts

Smartly remove less key messages while saving the essentials.

📝
Summarize Past Talks

Condense old discussions into a short, useful recap.

4
💬 Build Your Chat

Add instructions, questions, and responses to your conversation.

5
✨ Auto-Tidy Magic

When chats get too long, it automatically shortens them perfectly without losing the good stuff.

6
📊 Check Memory Status

See how full your chat history is and what's left.

🎉 Endless Smart Chats

Now enjoy long, helpful AI conversations that stay sharp and never overload.

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Star Growth

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AI-Generated Review

What is ctxpack?

Ctxpack is a Python library for compressing and managing context windows in LLM conversations, tackling the limits of models like GPT-4, Claude Sonnet 4.5, or even hypothetical GPT-5 with its context window comparison challenges. You set a token budget via tiktoken, add user/assistant/system messages, and fetch a trimmed list that fits—using strategies like sliding windows, pruning low-importance exchanges, or recursive summarization. It integrates with OpenAI clients, handling context window LLM overflows automatically for smoother ai chats.

Why is it gaining traction?

Unlike basic truncation tools, it offers smart compression that preserves recency, system prompts, and key details, making long-running context window llms feel native without manual hacks. Devs dig the dead-simple API: add messages, get compressed output, check status—no boilerplate for context engineering. For github actions or copilot workflows feeding LLMs, it shines in payload management, dodging "github context access might be invalid" errors via efficient token tracking.

Who should use this?

Backend devs building LLM agents or chat apps where conversations outgrow context windows in Claude or GPT models. AI prompt engineers iterating on github context workflows, needing quick compression for variable refs or payloads. Solo makers prototyping context window ai tools without deep token math.

Verdict

Worth a spin for small LLM projects—15 stars and 0.9% credibility score scream early alpha with thin docs and no tests, but the MIT-licensed core delivers immediate value for context compression. Fork and harden it if you're serious.

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