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Alternatives

Products that do what ContextCut PRO does

Private AI that injects your knowledge into every LLM query

  1. 1

    Reusable AI Memory for Smarter Prompts Anywhere

    Nov 2025

  2. 2

    Memory for your AI Tools

    2025

  3. 3

    Free MCP for security AI: live BGP, DNS, threat graph

    May 2026 · whisper.security

  4. 4
    Spydr139

    Github for LLM context. One memory, infinite possibilities.

    2025

  5. 5

    Personal URLs for sharing context with AI

    Nov 2025

  6. 6

    ThoughtDAG indexes local agent conversations across tools, finds the turns relevant to your work, and turns them into editable context graphs.

    23d ago · chenxiachan.github.io

  7. 7CO

    I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…

    2025 · github.com

  8. 8IB
  9. 9

    Web tool to slice codebase clutter & save 50%+ on LLM tokens

    Jun 2026 · polar.sh

  10. 10

    Cut your AI token costs by 40-60% with one API call

    Feb 2026 · agentready.cloud

  11. 11

    The context manager and skills library for marketing teams

    Apr 2026 · promptr.ai

  12. 12

    Transform Your Email Experience With Contextual Intelligence

    2025

  13. 13

    Turn your work into AI agent memory, served over MCP

    May 2026 · contextberg.com

  14. 14

    Turn your work activity into structured AI context.

    Feb 2026 · claw.toggle.pro

  15. 15

    RAG-ready web scraping that cuts your LLM token costs

    Apr 2026 · geekflare.com

  16. 16OS

    Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…

    Jan 2026

  17. 17OS

    Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…

    2025

  18. 18

    Smart task-driven LLM context optimizer

    May 2026 · github.com

  19. 19

    Your Agents. Your Business. Connected.

    Aug 2026 · salestrics.com

  20. 20CS

    AI agents accumulate stale tool results — file reads, web fetches, bash outputs — in their context window. Every one sits there for the entire conversation, consuming tokens and degrading quality. The standard fix is auto-compaction: wait until full, then drop content indiscriminately. Context Surgeon gives the agent three operations — evict, replace, and restore — so it can manage its own context. It works as a transparent local proxy that intercepts API requests, assigns IDs to content blocks, and applies eviction directives before forwarding. The agent calls the tools via bash. The proxy…

    Apr 2026 · github.com

  21. 21LC

    Hi HN, I'm building Librarian (https://uselibrarian.dev/), an open-source (MIT) context management tool that stops AI agents from burning tokens by blindly re-reading their entire conversation history on every turn. The Problem: If you're building agentic loops in frameworks like LangGraph or OpenClaw, you hit two walls fast: Financial Cost: Token usage scales quadratically over long conversations. Passing the whole history every time gets incredibly expensive. Context Rot: As the context window fills up, the LLM suffers from the "Lost in the Middle" effect. Response latency…

    Feb 2026 · uselibrarian.dev

  22. 22

    Cut your LLM Token Costs by 65%

    Jul 2026 · supercompress.dev

  23. 23AP

    Hey HN! We've run our privacy-focused open-source inference company for a while now, and we're launching a flat monthly subscription similar to Anthropic's. It should work with Cline, Roo, KiloCode, Aider, etc — any OpenAI-compatible API client should do. The rate limits at every tier are higher than the Claude rate limits, so even if you prefer using Claude it can be a helpful backup for when you're rate limited, for a pretty low price. Let me know if you have any feedback!

    2025 · synthetic.new

  24. 24

    Build agent that uses 80% less token and delivers better results. - Tura-AI/tura

    26d ago · github.com

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