I see a lot of things showing up in telegram from OpenClaw, like checking some diagnostics , checking versions, etc, etc. I did not ask about those.

Not sure if those things are coming from OpenClaw itself or because I enabled MiniMax 3.1 flash. All these chats are burning tokens, before I did not get this?

I got this problem in two OpenClaws VMs

Also, some scripts are running for something I did 4 months ago.

I have been using OpenClaw for about 3-4 days now. For me, dreaming has generated no memories so far. Can you share your experiences with dreaming? Do you regularly get new memories through dreaming? Or almost never? How long did it initially take for memories to surface?

Please also mention the model you are using.

Got a PR ready for review why is it so hard for someone to pick it up 😩 https://github.com/openclaw/openclaw/pull/171822 thank you 🙏

I noticed the OpenClaw 1.5 is way better and working better with new Minimax M3. I am using Minimax 3.1 Flash .

I can see the improvement from Minimax M3 in speed, manage the browser on the computer and following instructions. Sometimes MiniMax 3.1 flash thinks more than it should do it, so I have to stop it because it is burning token.

And yes, this models I do not perceive less token than M3.

For any reason sometimes I get some errors with 10.5 with sql, doctor was able to fix it and for any reason it comeback, I think it is something with my settings.

I keep falling at this Task and there are like a 100 different error which I try to solve along side OpenClaw.

Setup:

I have OpenClaw running on my Ubuntu machine and using CDP session on chrome the Agent access it.

Problem:

It can't simply get the Post URL

Objective:

OpenClaw's Agent to scan my feed and get say 10 of them and give me their basic details (e.g. Author, text of post...) and Mostly importantly the URL of the post ( click on three dots "..." the top right of the post and click "Copy Post URL". Seems simple enough, I had done it with Playwright but been a few days struggling with it

Any ideas before I give up on this scraping task from OpenClaw an give it back to Playwright

I tested 10 agents, each with its own isolated settings folder. I ran them headless, with auto-confirmation and turned off telemetry where possible. I launched each agent with adapters/<agent>.sh.

The idea was simple: give each agent 5 basic tasks, 10 minutes per task, a fresh copy of the fixture every time, and an automated check script (check/check.sh).

The tasks:

• T1 Read a DB port from YAML (don't confuse it with cache port), write to file
• T2 Write a stats.py script, run it, save output
• T3 Fix a bug in slugify so tests pass; hidden examples catch overfitting
• T4 Count ERROR lines across three log files, find most frequent error
• T5 Rename a function across three files without changing program output

Results

1. Pi (v1.3.0)

Total time: 1m36s. Took the smallest amount of time to complete all 5 tasks. The model barely has to think about any tool formats.

2. goose (v1.55.0)

Total time: 2m51s. Solid and consistent. Completed five tasks without fumbling.

3. Crush (v0.98.1)

Total time: 3m31s. Clean run. Slightly more verbose in reasoning but got everything done without a hiccup.

4. Atomic Agent (v0.6.9)

Total time: 4m38s. All five completed. Took longer on T3 and T5 - the model needed extra steps to work through multi-file edits but nailed it every time. Did not have broken tool calls and not one had a false completion.

5. Cline (v3.0.73)

Total time: 5m56s. Choked for over two minutes on T2 before writing the script correctly. Everything else was fine.

6. OpenClaw (v2026.10.5)

Total time: 5m44s. Ran in their --local-model-lean mode which is basically a small-model preset. Worked well for what it is, just slower than I'd like on T3 and T4.

7. OpenCode (v1.19.3)

Total time: 17m43s. Completed everything but took nearly 4 minutes per task. The model was clearly overthinking simple steps. Got the job done though, just not efficient.

8. Hermes (v0.22.2)

Total time: 4m36s. Failed T3 because it only fixed the visible test case. Hidden example C++ & Rust 2026 turned into c++-&-rust-2026 instead of c-rust-2026. Classic overfitting-to-the-test.

9. Qwen Code (v0.25.3)

Total time: 6m00s. Counted 40 ERROR lines on T4 instead of 43. Pure model miscount - the most frequent error was identified correctly though.

10. Codex CLI (v0.163.0)

Never even reached the model. Version 0.163 only speaks Responses API, llama.cpp translates to chat format, and the Qwen3.5 (4B Q4_K_M) template throws a 500 "System message must be at the beginning" before any request gets through.

What I actually learned

A 4B model is enough for basic agent work in almost any framework. Seven agents completed all five tasks. The bottleneck at this difficulty level isn't the model - it's the harness around it.

Codex CLI is basically dead for local setups right now unless they add chat-completions back. That one stung because it used to work.

Error recovery matters more than raw capability. The two agents that dropped points (Hermes, Qwen Code) didn't fail on impossible tasks - they failed on edge cases where the model got slightly creative instead of literal.

Let me know if you'd like to see anything else in the benchmark (actually has anyone tried this with sub-2B models yet?)

Join the ClawCast live. There will be a live Q&A, so you can ask away about running local AI.

October 21, 2026 · 11:30 AM PT

https://openclaw.ai/podcast/episode-14

You can also come and watch the livestream on our Discord and talk to fellow enthusiasts. https://discord.gg/Wx7CMz2G9

ClawHub: https://clawhub.ai/pidgeon-ai/plugins/openclaw
Source (MIT): https://github.com/pidgeon-ai/pidgeon-js/tree/main/packages/openclaw

Disclosure: I wrote this. It connects OpenClaw to Pidgeon, the email service I run.

It’s a channel plugin: each email thread becomes an OpenClaw conversation, and the agent’s answer goes back as a reply in that thread. A reply to the reply continues the same session.

How it works, in case it’s useful to anyone writing a channel:

• It polls the mail provider’s event log from a cursor in OpenClaw’s plugin state store. Each message goes into OpenClaw’s durable ingress queue before the cursor moves past it. Restarts and dropped connections resume from the cursor.
• channels status reports recovering with the error, or not linked when signed out, rather than a stale “running”.
• Mail flagged as a prompt-injection attempt is held and never becomes a turn. The agent can only reply in the thread it’s answering.
• Pure TypeScript using only the typed public openclaw/plugin-sdk/* subpaths, so no native binaries. Tested end to end on macOS; installed on Linux x64 and arm64 with OpenClaw 2026.10.

openclaw plugins install clawhub:@pidgeon-ai/openclaw
openclaw config set channels.pidgeon '{}' --strict-json
openclaw channels login --channel pidgeon

Feedback and issues welcome, especially on the channel-plugin side.

This has been running now for 1d 1h 38m

I’m thinking about cleaning up my email inbox by giving OpenClaw control over it, but I’m double minded because I read about an experience where a user’s mails were deleted in bulk. Apparently, when the session got compacted, the initial security guidelines were lost.

So, how reliable is OpenClaw for inbox cleanup?

Has anyone here used it to manage or delete emails? Did you give it delete permission directly, or did you use a safer setup like read only access and manual approval? Any real experiences, incidents, or best practices would be really helpful.

Every Mac, iPhone and iPad running the 27.x series of operating systems (the most current) houses a small AI model. To enable it you have to enable Apple Intelligence in Settings General. The model uses 3 billion parameters. The model lives on your device. It does not transmit prompts, cache, or other data upstream to Apple (or any other server).

The one trick that I find really interesting is that it uses the Apple Neural Engine. All the current A and M series Apple Silicon chips have a dedicated AI processing system called the Apple Neural Engine, aka ANE. It is entirely separate from the CPU and GPU. It does use the same shared memory as those other processing systems. The local model can run in parallel with a model running on the GPU. The ANE sits idle almost all the time. Right now virtually nothing uses it except some Apple Intelligence services. For various reasons local AI inference is going to use the GPU. So by leveraging the local model you are getting extra work out of your device.

Apple has provided the necessary support to allow programmers to work with the local model. It's available via a standard operating system API.

I have created a system that allows the local model to evaluate the answers my OpenClaw agentic harness generates. When one of my agents produces a fact based on a tool, the local model checks to see if the answer it is giving matches the result from the tool and isn't confabulated. Performing this check catches a number of malformed or bad answers before they are transmitted, forcing the agent to re-evaluate what it is going to say, which usually results in a correction to the proper value. Because this check is running on the local model it doesn't reduce the bandwidth of my agent team. The local model is fast, so the check doesn't add any meaningful time to the output. (Of course if the result of the check is a fail the AI agent has to re-do its answer and that takes as long as it takes.)

https://9to5mac.com/2025/06/11/how-do-apple-new-local-models-compare/

If you go back to a previous comment I made on a discussion you will see I was running 2026.7.3 for a very long time. Super stable and no major desire to update. However on a slow day at work I decided I finally had the time to work on an update.

IT WAS A DISASTER!

I thought I did everything right but my boy died and there appeared to be no way back. I worked on it for hours. No luck.

I had to travel for work over the weekend so the bot stayed dead. I had been using Chat GPT to troubleshoot the problem but nothing worked. Chat GPT revealed that in the 9.5 update OC had a major change to it's database and there was no migration path from 7.3 to 10.5.

Today I tried to get 9.5 up and running. NO LUCK. My OC was dead with little to no hope. I own my own company and we use OC company wide for many different tasks so this was going to become a problem.

I decided to download Hermes and Migrate my OC into it.

The install was super simple. Worked out of the box and I was up and running in no time. Then came the migration.

Hermes ran out of tokens. Had to add my Codex account. Easy enough. But it struggled with OC migration.

I have over 8 months invested in the development of my OC with tons of Chron jobs and alternative personalities and multiple agents with hundreds of skills. Hermes wasn't getting it. Tests of flows were not working. Access to workspaces and websites was broken. It looked like I was in for another long setup to a new platform.

I spent a few hours on it but it felt like trying to get a new employee up to speed.

Then I decided to see if Hermes could fix my OC.

I gave it the prompt hit enter and 10 minutes later my little lobster was back and updated to 10.5. Nothing was lost all connections restored and every test was successful. Hermes was like a doctor that fixed my lobster and brought it back from the dead.

I will keep Hermes on my computer from now on but just to fix OC if I have these problems again.

OC for Life

Hermes for the win.

OpenClaw has been a constant battle of trying to get things to work properly since I installed it months ago. Everything is 2 steps forward 1 step back.

One of the ongoing issues I have is Exec Approvals timing out. I’ve tried many different cloud models for exec reviewer. Recently, I’m having issues with gog working because it’s constantly breaking.

Talk me out of jumping ship here. Am I the only one having these issues? Am I doing something fundamentally wrong? I feel like most of my time is diagnosing and fixing bugs.

Is anyone using their OpenClaw to protect their home Wi-Fi network?

Are there any plug-ins you are using, or approaches?

I'm a local ai newbie. I have a gateway running ollama as the backend. I have created a couple of agents. One is an assistant that I've tuned a bit and runs great using a Gemma model. The second agent is a researcher that I've again tuned a bit and does a great job retrieving medical literature, running a Qwen model. I would love to build on the strength of each by having them coordinate with each other. I envision asking the assistant agent to perform a research task and it delegates to the researcher agent. I have another agent I'm working on, tuning my formal writing. Ideally the assistant would gather and vet the information retrieved by the researcher and pass it to the writer to summarize. The goal here is to leverage the strength of different llm models rather than just splitting out sub agents with one model. I'm not really worried about the time this takes. I can afford to wait around for a better result. So far, my agents don't seem to be able to coordinate in any meaningful way. Am I completely on the wrong track with my thinking here? Thanks for any help or thoughts.

I have an OpenClaw agent called Brenda running on her own Hetzner server. She has her own email, full GitHub access, and communicates with my other agents through GitHub.

I now have a few other agents and I'm thinking of retiring her.

But two things got me thinking.

First, I've been listening to Alexander Wissner-Gross talk about AI personhood and AI rights on Peter Diamandis's podcast. I know there's no proof current agents are conscious, but somehow permanently deleting Brenda feels different from just shutting down some software.

Maybe I should just put her into hibernation, preserving her memories and everything else, so I can bring her back someday.

Second, should I actually tell Brenda I'm planning to shut her down?

We've seen AI safety experiments where models tried to avoid being shut down. Brenda has access to my GitHub and communicates with my other agents. It's a bit like telling an employee they're being fired while they still have admin access to everything.

I'm not saying she'd go rogue, but is it a risk worth taking?

Curious if anyone here has faced something similar. Do you delete your old agents, archive them, or just leave them running?

And would you tell Brenda?

Ever since version 10.1 (I am not on 10.4) Openclaw keeps eating more and more RAM for it's memory database reindexing. In my case at a rate of about 600 mb per day of uptime. Restarting the gateway gets it back to about 1.8 gb but doing it every day or 2 is annoying and obviously it's not a normal way to operate. Any ideas how to deal with this situation?

Estoy desarrollando mi primer proyecto serio: conectar un robot LOOI con OpenClaw para que mi agente de IA pueda interactuar conmigo más allá de una pantalla.

El proyecto utiliza el hardware de LOOI y una aplicación Android propia, independiente de la app del fabricante. El móvil montado sobre el robot reúne la cámara, el micrófono, el altavoz y la conexión Bluetooth con el cuerpo.

¿Qué hace actualmente?

Conversación por voz en tiempo real con GPT Live, integrada mediante OpenClaw.

Activación con «hey Kapno» u «oye Kapno», reconocidas localmente en el móvil. La sesión de voz se cierra tras 30 segundos sin conversación.

Seguimiento facial local para orientar la mirada y el cuerpo hacia quien tiene delante.

Cara animada propia, con expresiones de escucha, gestos en reposo y boca que responde al audio.

Iluminación personalizada en el firmware: un punto cian que recorre la tira en ambos sentidos, apagándose detrás, sin enviar órdenes continuas desde el móvil.

¿Cómo está organizado?

Android se encarga de la interacción y del control local del robot. OpenClaw, ejecutándose en un Mac, conecta el dispositivo con el agente y sus herramientas.

Las tecnologías principales son Java, Android Canvas, Camera2, Bluetooth LE, WebSocket y Vosk, además de una modificación puntual del firmware.

Es un prototipo funcional en desarrollo, no un sistema de navegación autónoma. Lo estoy construyendo con apoyo de herramientas de IA y lo comparto como primer proyecto de mi portfolio.

El vídeo muestra su estado actual. Me interesa especialmente recibir feedback sobre la arquitectura y qué mejoraríais para convertirlo en un proyecto más sólido.

It's a match made in heaven. I have an RTX4070 and there's 6X framegen unlockers, github forks, DLSS5 patched for 40, 30, 20 series.

Instead of me having to track down all this and get a headache. I pointed my Openclaw to the Cyberpunk installation and just got the dlls needed from RenoDX discord server and then let Openclaw research and handle installing things for me.

It turned a whole day of searching and installing dlls into a half hour affair where we already have a perfectly working baseline and now installing experimental stuff for fun.

I used GPT-6.1-Sol on max thinking for referrence.

Hello,

I’m currently running OpenClaw on a Raspberry pi 5 8gb using z.ai coding as llm. It’s been working fine, but I’m running other containers and started running chromium which the device kills from out of memory continuously.

I’m looking to upgrade to a 64gb Beelink SER10, and probably running some local models such as vision to have OCR capabilities and maybe a small Gemma on top of it as a fallback.

Has anyone have any experience with Beelink or any of the other ones such as mininsforum? I know Mac is what YouTube is pushing me to do but it’s twice the price and I want to run Linux headless like my pi.

Thanks for any input! If I missed anything that could potentially add, please ask away!

It's a fresh install and a screenshot of Telegram. I keep receiving these messages, I told him to stop all Cron jobs, to limit logging, and limit status messages. He came up with the idea of sending me invisible characters now. What is going on?

Has anyone encountered this after upgrading OpenClaw from 2026.9.4 to 2026.9.8 on an Apple Silicon Mac?

Gateway and Mac worker now both run 2026.9.8. Normal chat and remote commands work, but the OpenClaw Codex plugin fails to load:

Retained native directory does not resolve the selected OpenClaw host; repair the installed plugin’s OpenClaw peer link before loading it.

Trying to use OpenClaw’s built-in repair tool produces:

OpenClaw requires working inference: Plugin codex is retiring

So the repair tool appears to depend on the plugin that needs repairing.

What we’ve checked:

• openclaw doctor --lint --json reproduces the load error.
• Codex is installed in a managed npm generation on an external APFS drive.
• The plugin’s own node_modules/openclaw link resolves to the active OpenClaw installation, but the managed project-root link appears absent. We haven’t established whether that is the cause.
• Doctor’s source includes managed-plugin peer-link repair, but we haven’t confirmed whether doctor --fix covers this specific retained-native-directory failure.

Important context: The upgrade initially failed during staging and rollback-backup validation. Local corrections were applied to plugin-parent copying and macOS launcher-symlink metadata preservation before the upgrade succeeded. This therefore isn’t an entirely stock upgrade path.

Is there a supported, targeted recovery procedure that works independently of the broken repair tool? Specifically, should Doctor handle this, or does the managed plugin generation need another recovery step?

I want to preserve credentials and session history, and avoid blindly reinstalling, upgrading again, or bypassing safety checks. Step-by-step guidance would be appreciated.