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Work · alternatives · 2026

24 alternatives to LISA Core

LLM memory using semantic compression for AI conversations

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    LISA Core — semantic compression for AI conversations

    Apr 2026 · lisa-web-backend-production.up.railway.app · its alternatives →

  2. 2

    Reusable AI Memory for Smarter Prompts Anywhere

    Nov 2025 · chromewebstore.google.com · its alternatives →

  3. 3
    Eden▲531

    One-click create comments on any webpages with AI

    2024 · chromewebstore.google.com · its alternatives →

  4. 4
    TalkWeb▲188

    AI powered chrome extension to chat with any website

    2023 · chromewebstore.google.com · its alternatives →

  5. 5
    Spoke.ai▲403

    Summarize Slack with powerful & secure AI

    2023 · spoke.ai · its alternatives →

  6. 6
    Bloc▲360

    Turn your knowledge base to AI chat in 2 minutes and share

    2023 · its alternatives →

  7. 7

    Build chatbots with memory using just an API

    Jan 2026 · amarsia.com · its alternatives →

  8. 8

    Captures and stores your chat from various AI platforms

    Mar 2026 · ai-memory-beta.vercel.app · its alternatives →

  9. 9

    We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…

    Mar 2026 · github.com · its alternatives →

  10. 10

    Anchor Engine is ground truth for personal and business AI. A lightweight, local-first memory layer that lets LLMs retrieve answers from your actual data—not hallucinations. Every response is traceable, every policy enforced. Runs in <3GB RAM. No cloud, no drift, no guessing. Your AI's anchor to reality. We built Anchor Engine because LLMs have no persistent memory. Every conversation is a fresh start—yesterday's discussion, last week's project notes, even context from another tab—all gone. Context windows help, but they're ephemeral and expensive. The STAR algorithm (Semantic Traversal And…

    Mar 2026 · github.com · its alternatives →

  11. 11

    Hey HN! We're Deshraj and Taranjeet. We've been building working on a startup called Mem0, building an open-source memory layer for AI apps and agents (https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=41447317). We also kept running into our own daily frustrations with AI assistants forgetting everything between conversations. Over a weekend, we decided to hack together a Chrome extension to solve this for ourselves. The problem was simple: we were constantly re-explaining our context across platforms when switching between ChatGPT, Claude, and Perplexity. Start a coding discussion in…

    2024 · github.com · its alternatives →

  12. 12

    Persistent, structured memory for AI Agents

    Jan 2026 · mnexium.com · its alternatives →

  13. 13

    AI powered extension to chat with almost anything on the web

    2023 · browserai.app · its alternatives →

  14. 14BS

    We built BSE (Bramble Semantic Engine) – a semantic compressor that transforms natural inputs into low-dimensional structured representations. It's designed as a preprocessing engine for LLMs, capable of reducing long inputs into compact, logic-preserving forms across: 1. Language Extracts SVO (Subject, Verb, Object) structure Captures modifiers: adjectives&#x2F;adverbs Restores pronouns from short-term memory Detects questions Computes: Compression Rate (%) Semantic Loss (%) Compares sentence compression outputs via SDC: Subject-Subject, Verb-Verb, Object-Subject similarity Sentence…

    2025 · its alternatives →

  15. 15CA

    Current AI chat assistants face a fundamental challenge: context management in long conversations. While current LLM apps use multiple separate conversations to bypass context limits, a truly human-like AI assistant should maintain a single, coherent conversation thread, making efficient context management critical. Although modern LLMs have longer contexts, they still suffer from the long-context problem (e.g. context rot problem) - reasoning ability decreases as context grows longer. Memory-based systems have been invented to alleviate the context rot problem, however, memory-based…

    Nov 2025 · its alternatives →

  16. 16

    Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs. - It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url - The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables You can see it in action in this demo where it memorizes tic-tac-toe games: https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=PWbyeZwPjuY Why we built this: before Butter, we were Pig.dev (YC W25), where we…

    Oct 2025 · butter.dev · its alternatives →

  17. 17

    OpenMemory Chrome Extension: Long-term memory for ChatGPT, Claude, Perplexity, Grok etc - mem0ai/mem0-chrome-extension

    2024 · github.com · its alternatives →

  18. 18

    It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.

    2024 · github.com · its alternatives →

  19. 19

    The context manager and skills library for marketing teams

    Apr 2026 · promptr.ai · its alternatives →

  20. 20BA

    Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…

    2025 · docs.butter.dev · its alternatives →

  21. 21ZL

    Zep is a long-term memory store designed for conversational AI applications built using modern LLMs. It handles the storage, summarization, embedding, indexing, and enrichment of chat histories, and offers developers a simple, low-latency API to this data. Chat history storage is an infrastructure challenge all developers and enterprises face as they look to move from prototypes to deploying conversational AI applications that provide rich and intimate experiences to users. Key features include long-term memory persistence, auto-summarization, vector search, auto-token counting, and Python…

    2023 · its alternatives →

  22. 22

    Сhat with any website using your favorite AI models

    2025 · its alternatives →

  23. 23

    Simple AI chrome extension enabling chatting with web pages

    2024 · talk2page.vercel.app · its alternatives →

  24. 24

    A great way to enhance chatbots is to allow them to look up information for context, typically using a vectordb. If you have writings you would like to share with others, you can offer a server that allows others to do semantic lookup, and that way anyone can have a chatbot which can pull from your writing. The goal of this project is to have a protocol that makes that easy. Strictly speaking, the protocol is for semantic retrieval and doesn't require using LLMs although LLMs are the motivating application. For far more details and how to get a demo up and running, see the readme.

    2024 · github.com · its alternatives →

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