r/openclaw

I keep hearing people talk about how expensive OpenClaw is. Burning tokens, API, etc. I'm using OAuth with 2 models - (MiniMax2.7, and GPT 5.4.) $40/month total. Am I missing something??

Title

Currently I’m spending around $2k–$2.5k/month on Sonnet 4.6, with about 5% Opus 4.7 usage, mainly for development and business management through the Anthropic API.

Honestly, I’m extremely happy with the results for the money.
But I’m also trying to optimize costs and see whether I can get similar quality for less.

What I’ve done so far:

• Offloaded most coding work to GPT-5.5 using the $200 OpenAI plan
• For coding, it’s been great
• I also tried DeepSeek V4 Pro, but it felt too slow for my taste
• GPT-5.5 also feels a bit slower than Sonnet/Opus, but with multiple tabs open it works fine

Now I’m considering shifting more toward Codex/GPT to reduce my Anthropic API costs and get more value from the $200 plan.

But I keep hearing that Codex/GPT isn’t as good for:

• answering customer emails
• writing knowledge base articles
• handling employee planning/operations
• general business communication

And that Anthropic models (especially Sonnet/Opus) are still better for those tasks.

So I’m curious what others are doing:

• Are you running everything on Codex/GPT now?
• Or are you still using Anthropic for writing/business tasks and Codex mainly for development?
• Any setups/workflows/models you’d recommend for optimizing quality vs cost?

Would love to hear how others are structuring their stack.

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?

I can spend $25k-$100k on a local computer, call it a business expense. I'll spare the details.

But no... These models just suck.

I've tried every single model outside the 1.6T DeepSeekv4. Maybe if people think that is useful, I'll do a trial Vast.ai server.

I feel like I'm letting my ultra privacy focused customers down. I've been trying for 1+ months and probably spent $400+ on vast.ai server tryouts.

I know some people are getting weather and stonk prices with crappy 35B models... We have significantly more complex stuff. Combining 260 page pdf docs with a massive dropbox.

Only Opus has worked.

Maybe I need to lower expectations? Maybe I need to have Opus make MCP/CLI-like skills for ~500B models?

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!

Hello! Lately I have kept an eye on https://isitstable.com/openclaw to track whether it is safe or not to upgrade. But I must admit that the feedback posted, seems that it is a long time since it was not a breaking release. Is this really true, or are people just sacking it, because they can?

Hoping for some thoughts from the community. I know one of the major upsides to OC is the Initiative. However when I first started using it (several months ago now) I kept pulling the reins back because the bot drove me crazy.

Tons of things I would ask it to do were not remotely production ready and I wanted to review everything. Slowly I built a good rhythm of prompt injection, content review, tweaking, final approval and release. But now I'm not sure how much initiative it really has.

My bot is super stable. I do not have any of the issues often described here anymore. The largest stability shift was moving to OAuth OpenAI. I'm at a stage where everything I ask it to do just works.

But, OC doesn't make recommendations unless I ask for them. It doesn't wake up in the morning with a list of ideas on how we could be better. It doesn't brainstorm improvements.

Did I do that or am I misunderstanding "Initiative"?

In the early days: I'd ask it to do something (at this stage it was mostly setup related) and it would just try 3 different ways to make something happen. Sometimes choosing the stupidest path to retrieve an email for example. Then I would have to tell it to stop and get the right skills.

Now, it's humming. I'm a huge fan boy and don't want to go back. But I am curious if I should start prompt injecting some sort of initiative or did OC pull that back because it was like a toddler in a china shop.

I get it, running agents locally without proper sandboxing can be risky, and VPS setups are cleaner and safer for long-running workflows. But at this point it feels like every creator magically arrived at the exact same recommendation.

Is Hostinger actually that good for OpenClaw setups, or did they just sponsor the entire ecosystem? Also, I’m new to OpenClaw. I’ve tried it, but I want to dive deeper into the technical side of things. Can anyone suggest some good resources or creators who genuinely understand how it works?

My question is .. has anyone else started using this.. since this a new way for Agent Model's. ( I also use Codex. GLM5.1, Gemma3:27b-cloud ). I will report back in a few days, as I have all agents now on Super Grok, and held one Dev. back, tied to Codex5.5.)

​

For real-world lead qualification workflows, which one is actually better: AI agents built with n8n or OpenClaw?

I’m mainly interested in things like:

automation quality

CRM integration

lead scoring

WhatsApp/email follow-up

sales workflows

scalability

AI autonomy

reliability in production

ease of setup and maintenance

I’d like to hear practical experiences, pros and cons, and which one delivers better results for marketing and sales operations.

I'm looking for 2-3 ai projects to discuss in my newsletter. It's a rather small group that I am mailing too so no huge publicity but none-the-less a good opportunity to get more eyeballs on your project.

Send me a short description below and a link to your project.

People using 2nd rate models. What are you accomplishing? (Chinese models, Codex, Sonnet)

People using 3rd rate with GPU. What are you accomplishing? (Local tier with Nvidia Chips)

People with 3rd rate with CPU. What are you accomplishing? (Everyone else)

I'm most interested in why you are still interested in OpenClaw. I have been miserable trying Sonnet and Deepseekv4 after getting used to Opus.

I joke when I say: "Oh you got the weather or stock prices?" but... its not like you are single shotting full stack HIPAA approved apps.

Just set up OpenClaw using Codex OAuth with a ChatGPT account, and when I tried to use gpt-5.5 Pro but it errored out: "model not supported when using Codex with a ChatGPT account." So it seems the 'pro' models are off-limits through the Codex bridge regardless of subscription tier.

If that's the case, what does paying for Pro actually get you here versus Plus...just higher rate limits / more headroom on the same standard models? Trying to decide whether to keep Pro past month one or drop to Plus. What are people actually running, and did you find Plus's limits enough for an always-on agent with the 30-min heartbeat?

I'm just fooling around with this for the first time. Historically I've not been much of a 'computer' person, but i caught the agentic ai bug. although i don't have any solid plans for it yet, i'd like to see what it can do for me...i thought about giving it access to $100 and an investment account...not to make money, but to document the loss of it, lol (yes i'm aware of all the security measures that need to be taken). I"m not sure how useful this will be for me, but i'm going 'all out' for this first 30 days to see if i can find a place for it in life and there's only one way to find out, and that's to implement it.

but why won't it let me use gpt 5.5 pro?

I hear about people on CPU using 35B models... From everything I read, when you load up a context of like 100k tokens, this takes like 5-15 minutes.

Is this what life is like running local models? My 9B models on old 6gb VRAM wont even run with OpenClaw because it needs 16k tokens just to send its first command.

I've been running multi-hour OpenClaw sessions and the context window fill-up is my main pain. Native compaction kicks in late (around the threshold) and it's all-or-nothing — once it summarizes, older detail is gone.

What I wanted instead: compress *gradually*, every turn, but keep the last few turns completely raw so the agent doesn't lose the thread it's mid-way through.

I ended up writing a Plugin SDK hook on before_prompt_build that does this — folds older turns into a compressed episodic view, keeps the trailing turns verbatim. On a long session it cut the re-sent context by roughly 80% without the agent losing track of earlier turns.

Two questions for people running long sessions:

1. Do you rely on native compaction, or roll your own context management?
2. Has anyone found the right "keep N turns raw" number? I'm defaultingto 4 but it feels workload-dependent.

(If useful, the hook is here — MIT core:

https://github.com/compresh/compresh-mcp — but mostly curious how others are handling this.)

Hi all,

I am new to OpenClaw, I am trying to set up a cron job but am encountering a problem. I have an instructions file that it is prompted to read, and ask me to review a new submission it sees in there.

This step works perfectly, but once I respond, it’s like the agent has no idea what is happening at all?

I am unsure what could be causing this? I can only seem to get it to even send me a message when it is set to sub agent isolated mode and it won’t do anything with main timeline mode.

Can someone help me with this?

Hey everyone 👋

We're working on OpenClaw model plans. Open-source models sold by concurrency slot, not by token. Nothing new, right? Well... each slot is yours: from 100 tokens/s and a 500M+ daily token cap depending on model, no shared pool, no speed degradation under 24/7 load. Built for the exact problem that keeps showing up in this sub.

The current stack we're validating:

• Qwen 3.6 35B-A3B
• Qwen 3.6 27B
• Gemma 4 31B
• Qwen 3.5 122B-A10B
• DeepSeek v4 Flash

This is our early-access lineup. Which models stay, which get cut, which we add. That'll come from real demand.

Looking for a handful of OpenClaw users running real agent loops who'd be up for:

• Virtual coffee, 15–30 min with me. Relaxed, informal, no slides
• Free access to the tier that matches your workload, for a month
• Trying it on something real and telling us honestly what sucks. Website, pricing, docs, the inference itself, anything

If our thing is worse than what you're already using, I want to know why.

If you're running OpenClaw at any serious volume and this sounds interesting, drop a comment or DM. I'll respond to all of them, and we'll set up the coffee time.

Thanks 🙏