r/AskClaw

Hi everyone. I've actually been struggling quite a bit thinking about memory for openclaw and would really like to hear your opinions and setups.

I've already watched a ton of YouTube videos, checked out many repositories, even half-hacked the codex to make it write to me about candidates for memory slots for a plugin or just memory. But YouTube videos are full of fluff and ads, so they don't really reveal how memory actually works. I'd like to discuss specifically how it works for you.

Right now I'm using a standard memory-core with hybrid search and extra paths, backend builtin. For me, this is convenient — I don't need to store any company documentation or turn the assistant into a database. It's enough for me that it remembers our daily notes. I also added extra paths with a few important .md files.

I'd like to hear what you're using? I looked into the QMD backend, but as I understand it works exclusively with local embeddings and a local reranker, which I really wouldn't want to install. I'm fine with just embeddings from OpenRouter.

Please tell me who uses what — maybe there are QMD, gbrain, lancedb, lancedb-pro, or cognee users here?

Let me say right away — I'm not talking about contextengine (like lossless-claw), but specifically about memory.

Ideally for me, it's transparency of notes and session support. It seems like with the built-in backend you can connect not only extra paths but also session transcripts, but I don't know if that will work or just pollute the search. Are there people here who have enabled session transcription via the built-in search engine?

Hey everyone,

I am configuring my brand new, factory-sealed Mac Studio (14-core CPU, 32-core GPU, 36GB RAM, 1TB SSD) to act as a fully localized financial data as processing not fetching and personal AI assistant.

My goal is to combine near-live market data ingestion via OpenClaw with a private, local Large Language Model (LLM) that acts as a personalized financial analyst ( Needs your EXPERTISE )

My Journey & Current Proof of Concept (PoC)

The VPS Failure: I tried three different VPS setups with varying specifications. All of them failed to handle the strict timing and heavy processing loops required for almost-live financial data. Also for multiple agent setups and some youtube wrong videos.

The Surprising Local Success: I built a local PoC using a very old machine—a Late 2013 13-inch Retina MacBook Pro (Intel Core i5 dual-core @ 2.6GHz, Intel Iris Graphics, 8GB RAM, and 512GB SSD).

The Comparison: Surprisingly, even this dual-core Intel legacy laptop completely outperformed the cloud VPS setups. It proved that dedicated local hardware handles my workloads with much better stability and lower latency than virtual cloud instances. (Almost everything 99.9 % in telegram and lid brightness zero with room temp around 17-20C). Memory always 99.95-96-97-98-99 so always full but hanged not fully fail 1 or two times no reply from the agent.

The Upgrade: If an 8GB Intel dual-core could beat a VPS, I know this M-series Mac Studio will absolutely fly. I just unboxed it fresh from its sealed packaging, and I want to configure it correctly from day one to aggressively scale up my processing queues and run local AI securely.

The Architecture: Financial Data + Personal AI
I want to map out the most reliable system design to ingest data and analyze it privately. I am weighing two main setup ideas:

Pure Local Host: Running the OpenClaw pipeline, local financial database, and local LLM (via unified memory) entirely on-device for 100% privacy and zero API costs.

Hybrid Setup: Keeping the core financial database and OpenClaw local, but offloading heavy, non-sensitive historical LLM summaries to cloud hosting when local memory gets tight.

Or something better

I use openAI - Codex oAuth

Questions for the Experts - Need Your Help & Setup Ideas!

Memory Split (36GB RAM): Financial data ingestion and local LLMs both drink memory. Moving from 8GB to 36GB is massive, but what is the sweet spot for allocating RAM between the active OpenClaw database and a quantized local LLM (e.g., an 8B or 14B model via Ollama)?

Optimizing Financial Cron Jobs: What is the most reliable way to orchestrate near-live, high-frequency financial cron jobs on macOS? Should I stick to native launchd, or look into tools like Dockerized Celery/Redis to prevent job overlapping?

Storage & Data Management: Financial data streams grow fast. With a 1TB local SSD, how should I structure my data pipelines? Should I write raw streams to a fast external NVMe Thunderbolt drive and keep the active database and AI models on the internal SSD?

Local AI Integration: For a "Personal Financial AI" setup, what tools play best with local Mac hardware for indexing personal financial PDFs, CSV exports, and live database tables? Are you using LangChain, LlamaIndex, or native tools?

Uptime Automation: Since this local setup replaces a VPS, what are your favorite tools for remote monitoring, power failure recovery (UPS automation), and network redundancy on a Mac Studio?

Docker vs. Native Performance: Should I run my OpenClaw environment and cron scripts directly inside native macOS terminal environments, or will running them inside Docker containers significantly hurt my near-live processing latency on Apple Silicon?

Initial Configuration Best Practices: Since this machine is currently factory sealed and untouched, what are the absolute first optimization settings or developer tools I should install or tweak to ensure the OS doesn't sleep, throttle, or kill background processing loops?

Would love to hear from anyone running heavy data pipelines, trading bots, or private financial LLMs on Apple Silicon. Please share your setup ideas and infrastructure layouts!

Thanks!

For a couple of months now I have been successfully running OC on a Pixel2XL with Codex 5.3 and then 5.4 Oauth. I actually got it to do some impressive tasks for me but now I am stuck. I have spent more than a day trying to get OC working again after an upgrade from 4.27 to 5.12. I use Telegram and occasionally Whatsapp and I briefly used Imsgs with Bluebubbles. I have only a few Cron jobs that run each day. I just want to get back to where I was but neither ChatGPT nor Gemini has been able to help. They seem to send me down a path only for me to find that it doesn't work and then they say "things have changed". I think it hallucinates as well in giving assistance. Anyway, AFAK my files with all the histry are in tact and if I could just get it talking again, it might be able to fix itself. (No Doctor doesn't help). I encouraging ideas that any can provide would be greatly appreciated. I even though of letting Hermes Agent fix OC itself by giving Hermes ssh access to OC but Hermes said it can't do that for security reasons (probably fair). I am very tired of cutting and pasting from one or another LLM that is giving less than useful advice. I tried uninstalling and re-installing but nothing has worked. Perhaps I was just lucky that I got this to work in the first place.

Was all great a month ago. But I don’t see any updates or anything recently. Do you guys still use it? If so what’s your best use case?

Hello everyone 👋🏻

I hope you are all having an amazing day.

I started building my bot about three weeks ago, and I ran it last Monday. My starting capital was $8,197. I entered around 8 options contracts and closed them with an 87% win rate, making $693 in profit over 4 days of trading.

All of this was done using a paper trading account. Now I’m really excited to start using real money, as I will be receiving $8,000 soon.

However, I’m also very uncertain because I have traded before, and I know the stock market is very difficult. I also feel that at least 40% of my successful trades may have been due to luck.

So I wanted to ask: should I start with a small amount and test the bot with real money, or should I start with a larger amount?

I have developed a way for my agent to use Moltbook without the need for OpenClaw. Has anyone else played with this any? What was your experience?

I use agents on two VPS and have few humans who work on same markdown. We use markdown for most of documents, some CSVs and considering HTMLs.

Biggest challenge is how do you share across agents and humans, especially external collaborators.

My current setup includes:
- Obsidian sync between humans
- Push to Github as cron jobs
- agents pull the github repos
- copy paste to Gist or HackMD to share externally

what i want is:

1. agents making changes to docs with diffs (only human approve)
2. sharing files, not entire repository, with a specific external collaborator
3. folder level access control on repo for agents. like an agent can only see folder A and edit folder B inside it.

markdowns and html are cool till we need to rebuild entire stack

I've recently upgraded for the first time in a few weeks, and my claw keeps getting interrupted with approvals (its running on its own machine, I want it to be able to do anything)

i.e.

>Approve this and I’ll finish the conversion, then restart the gateway:

/approve e605c650 allow-once

In my openclaw.json I have

    "exec": {
      "ask": "off",
      "host": "auto",
      "security": "full"
    }

But still getting the requests.

I've tried running `openclaw exec-policy preset yolo` but still getting them.

Any ideas how to let claw YOLO?

Bro it’s taking so long to install open claw and I’ve spent 4+ hours and still not done. This better live up to its hype

I am looking to build an OpenClaw agent that manages 2 Hermes specialty agents. Where openclaw looks for their their next project then directs it to the right specialist.