├── MEMORY.md # Long-term knowledge (auto-loaded each session)
├── HEARTBEAT.md # Autonomous task queue
├── SOUL.md # Personality and behavioral guidance
Say what you will, but AI really does feel like living in the future. As far as the project is concerned, pretty neat, but I'm not really sure about calling it "local-first" as it's still reliant on an `ANTHROPIC_API_KEY`.I do think that local-first will end up being the future long-term though. I built something similar last year (unreleased) also in Rust, but it was also running the model locally (you can see how slow/fast it is here[1], keeping in mind I have a 3080Ti and was running Mistral-Instruct).
I need to re-visit this project and release it, but building in the context of the OS is pretty mindblowing, so kudos to you. I think that the paradigm of how we interact with our devices will fundamentally shift in the next 5-10 years.
Your docs and this post is all written by an LLM, which doesn't reflect much effort.
You're using the same memory format (SOUL.md, MEMORY.md, HEARTBEAT.md), similar architecture... but OpenClaw already ships with multi-channel messaging (Telegram, Discord, WhatsApp), voice calls, cron scheduling, browser automation, sub-agents, and a skills ecosystem.
Not trying to be harsh — the AI agent space just feels crowded with "me too" projects lately. What's the unique angle beyond "it's in Rust"?
I also think it'd be a great starting point for building a private pub/sub network of autonomous agents (e.g. a company that doesn't want to exfil its password files via OpenClaw)
The name, however, is a problem. LocalGPT is misleading in 2 ways. 1. It is not Local, it relies on external LLM providers. 2. It is not a Generative Pretrained Transformer.
I'd highly recommend changing the name to something that more accurately portrays the intent and the method.
Does this mean the inference is remote and only context is local?
I'm working on a systems-security approach (object-capabilities, deterministic policy) - where you can have strong guarantees on a policy like "don't send out sensitive information".
Would love to chat with anyone who wants to use agents but who (rightly) refuses to compromise on security.
"cargo install localgpt" under Linux Mint.
Git clone and change Cargo.toml by adding
"""rust
# Desktop GUI
eframe = { version = "0.30", default-features = false,
features = [ "default_fonts", "glow", "persistence", "x11", ] }
"""
That is add "x11"
Then cargo build --release succeeds.
I am not a Rust programmer.
I feel Elixir and the BEAM would be a perfect language to write this in. Gateways hanging, context window failures exhaustion can be elegantly modeled and remedied with supervision trees. For tracking thoughts, I can dump a process' mailbox and see what it's working on.
Uses Mlx for local llm on apple silicon. Performance has been pretty good for a basic spec M4 mini.
Nor install the little apps that I don't know what they're doing and reading my chat history and mac system folders.
What I did was create a shortcut on my iphone to write imessages to an iCloud file, which syncs to my mac mini (quick) - and the script loop on the mini to process my messages. It works.
Wonder if others have ideas so I can iMessage the bot, im in iMessage and don't really want to use another app.
I assume I could just adjust the toml to point to deep seek API locally hosted right?
curious: when you say compatible with OpenClaw's markdown format, does that mean I could point LocalGPT at an existing OpenClaw workspace and it would just work? or is it more 'inspired by' the format?
the local embeddings for semantic search is smart. I've been using similar for code generation and the thing I kept running into was the embedding model choking on code snippets mixed with prose. did you hit that or does FTS5 + local embeddings just handle it?
also - genuinely asking, not criticizing - when the heartbeat runner executes autonomous tasks, how do you keep the model from doing risky stuff? hitting prod APIs, modifying files outside workspace, etc. do you sandbox or rely on the model being careful?
Ask and ye shall receive. In a reply to another comment you claim it's because you couldn't be bothered writing documentation. It seems you couldn't be bothered writing the article on the project "blog" either[0].
My question then - Why bother at all?
[0]: https://www.pangram.com/history/dd0def3c-bcf9-4836-bfde-a9e9...
Can it run on these two OS? How to install it in a simple way?
Can you explain how that works? The `MEMORY.md` is able to persists session history. But it seems that it's necessary for the user to add to that file manually.
An automated way to achieve this would be awesome.
We're past euphoria bubble stage, it's now delulu stage. Show them "AI", and they will like any shit.
Big props for the creators ! :) Nice to see some others not just relying on condensing a single context and strive for more
ort-sys@2.0.0-rc.11: [ort-sys] [WARN] can't do xcframework linking for target 'x86_64-apple-darwin'
Build failed, bummer.How much should we budget for the LLM? Would "standard" plan suffice?
Or is cost not important because "bro it's still cheaper than hiring Silicon Valley engineer!"
Its fast and amazing for generating embedding and lookups