AI · alternatives · 2026

24 alternatives to Txtos for LLMs – 60 SEC setup, long memory, boundary guard, MIT

i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope. what it is plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny…

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

1
Yet another memory system for LLMs▲165
Built this for my LLM workflows - needed searchable, persistent memory that wouldn't blow up storage costs. I also wanted to use it locally for my research. It's a content-addressed storage system with block-level deduplication (saves 30-40% on typical codebases). I have integrated the CLI tool into most of my workflows in Zed, Claude Code, and Cursor, and I provide the prompt I'm currently using in the repo. The project is in C++ and the build system is rough around the edges but is tested on macOS and Ubuntu 24.04.
2025 · github.com · its alternatives →
2
Twigg▲157
Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
2025 · twigg.ai · its alternatives →
3
A local-first memory store for LLM agents (SQLite)▲48
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc. - CaviraOSS/LongMemory
Dec 2025 · github.com · its alternatives →
4
DeltaMemory▲104
Most AI agents forget everything between sessions. We built DeltaMemory to fix that. It's a cognitive memory layer that gives agents persistent recall, automatic fact extraction, and temporal reasoning — via a single SDK call. Key numbers: - 89% accuracy on LoCoMo long-term conversation benchmark - 50ms p50 retrieval latency - 97% cost reduction vs raw token re-processing Open source SDKs, works with any LLM stack. Currently in early access. Happy to answer questions about the architecture, benchmark methodology, or how we handle knowledge graphs.
Feb 2026 · deltamemory.com · its alternatives →
5
Core – open source memory graph for LLMs – shareable, user owned▲112
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 · its alternatives →
6
TXT OS – Open-Source AI Reasoning, One Plain-Text File at a Time▲10
Hi HN, I'm excited to share TXT OS — an open-source AI reasoning engine that runs entirely inside a single `.txt` file. - No installs, no signup, no hidden code — just copy-paste the file into any LLM chat window (GPT, Claude, Gemini, etc.). - +22.4% semantic accuracy, +42.1% reasoning success, and 3.6× more stability (benchmarked on GSM8K and Truthful-QA). - Features Semantic Tree Memory, Hallucination Shield, and fully exportable logic. - MIT Licensed, zero tracking, zero ads. Why did I build this? I wanted to prove that advanced reasoning and memory could be made open, portable, and…
2025 · github.com · its alternatives →
7
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com · its alternatives →
8
Continual Learning with .md▲34
I have a proposal that addresses long-term memory problems for LLMs when new data arrives continuously (cheaply!). The program involves no code, but two Markdown files. For retrieval, there is a semantic filesystem that makes it easy for LLMs to search using shell commands. It is currently a scrappy v1, but it works better than anything I have tried. Curious for any feedback!
Apr 2026 · github.com · its alternatives →
9
AI Memory Layer for ChatGPT and Claude.▲26
Shared persistent memory across all your LLMs.
2025 · teamlayer.xyz · its alternatives →
10ZL
Zep – Long-Term Memory Store for LLM Apps▲7
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 →
11
Object database for LLMs that persists across chats (MCP server)▲9
I’d like to use LLMs for remembering all kinds of things: fitness, to-do lists, contacts, bug reports, research links, whatever. But there is no way to do that now. For example, if I find a great coding tutorial in chat, or tell it how much I ran yesterday, it forgets that when I close the chat. Even if I keep the chat history, I still need to scour through lots of messages to find the data I want. Ideally, Claude would remember all this, and I’d be able to find it later with ease. This is what my team built. It is a collaborative database you add to any LLM that supports MCP. (Claude Code,…
2025 · dry.ai · its alternatives →
12
Memorist▲7
An auditable, local-first memory engine for Open WebUI
Jul 2026 · github.com · its alternatives →
13
llms-txt-gen▲12
Generate your site's llms.txt in one command
Jun 2026 · github.com · its alternatives →
14
I made a state machine framework for guided conversations with LLMs▲19
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 →
15
Chat LLMs▲17
Engage in free conversations with advanced LLMs.
2025 · chats-llm.com · its alternatives →
16
`tc` like `wc` but for LLM tokens▲8
Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com · its alternatives →
17GL
GPT–LLM native macOS app with time travel, versioning, search▲12
Hey everyone! I made a Mac app for exploring large language models. It’s fast, small, has a tiny memory footprint. It’s immutable by design with both immediate time travel and automatic versioning as foundational elements. The app is written in Swift and a bit of Rust for the tokenizer. I used SwiftUI for structure and animations and Cocoa for advanced behavior. All storage is SQLite and local-only. You can go through the database as needed and backup it as well. The app has support for variants, which is the `n` parameter in the OpenAI chat completion API—equivalent to the drafts feature in…
2023 · thellm.app · its alternatives →
18
Mamba-Chat – A Chat LLM Based on State Space Models▲9
Hey everyone! Many of you might have come across the Mamba paper a few days ago, which introduced an LLM based on a state space model architecture. The Mamba architecture is quite useful as its complexity scales subquadratically with input length and is therefore way more efficient than transformer models: https://github.com/state-spaces/mamba I got really excited about the paper, so I decided to fine-tune the model on a chat dataset. It turns that this actually worked quite well! The model is quite suitable for casual chatting, which honestly surprised me given that it…
2023 · github.com · its alternatives →
19
Another LLM Chat App to avoid having 10 different subscriptions and MCP▲18
This is like ChatGPT but simpler, with less features (for now). It offers unlimited access to o3, Claude Sonnet 4 and other models for the pro plan. It offers access to o4-mini and the like for the free version. Requests are unlimited up to rate limits that we hope apply to less than 2% of users (they're transparent and can be seen on the account page). I was sick of having these like t3.chat or abacus.ai that give you very few messages for the $8-$10 / month subscription, so wanted to create something closer to Cursor's new pricing but transparent. Image generation will come soon, I…
2025 · donsir.com · its alternatives →
20
An all-in-one blog for learning Large Language Models (LLMs)▲5
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app · its alternatives →
21
Castra – Strip orchestration rights from your LLMs▲8
I got tired of AI agents forgetting what they were doing the moment their context window filled. The current industry solution is to write massively bloated agent harnesses full of defensive spaghetti just to stop models from drifting. The problem is treating chat history as project state. A conversation is not a ledger. Castra is a compiled Go binary that strips orchestration rights from the LLM. State lives in an encrypted, local SQLite database (castra.db). The LLM is just a stateless executor — it reads the DB, executes a highly constrained task, and the result is written back subject to…
Apr 2026 · github.com · its alternatives →
22
llmstxtdirectory.org▲4
Make your website visible to AI in less than 30 seconds
Jan 2026 · llmstxtdirectory.org · its alternatives →
23
Torqon▲4
Persistence Context for LLMs with inbuilt Token Reduction
Jul 2026 · torqon.dev · its alternatives →
24LS
Llm.sql – Run a 640MB LLM on SQLite, with 210MB peak RSS and 7.4 tok/s▲8
Hi HN, I built llm.sql, an LLM inference framework that reimagines the LLM execution pipeline as a series of structured SQL queries atop SQLite. The motivation: Edge LLMs are getting better, but hardware remains a bottleneck, especially RAM (size and bandwidth). When available memory is less than the model size and KV cache, the OS incurs page faults and swaps pages using LRU-like strategies, resulting in throughput degradation that's hard to notice and even harder to debug. In fact, the memory access pattern during LLM inference is deterministic - we know exactly which weights are needed…
Apr 2026 · its alternatives →

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