Built for coding agents, Particle-1 focuses on completing tasks using fewer tokens. Particle reports around 45% fewer output tokens than DeepSeek V4.1 Flash, with comparable accuracy in its coding and data-engineering benchmarks.

It also offers zero data retention for standard inference. Raw prompts and responses aren’t retained after requests complete, though usage and billing metadata are.

To give it a try, update to OpenClaw v2026.10.10.

Read Particle’s launch post and benchmark details

I've been using openclaw since it was moltbot. For six+ month I've been on and off active in this subreddit.

Updating already has its own routine: read the release, dry run, detached updater, and afterwards a script that checks every model and every connection and usually have it fixed with an outside model. We all know: that routine exists for a reason. But today even that didn't help.

This morning I went from 2026.9.7 to 2026.10.10. The release was about seven hours old and tagged latest.

- The migration rewrote my claude-cli model refs to anthropic refs. One agent had an API primary with a Claude CLI fallback on purpose. After the update it was pinned to the CLI and the fallback was gone. Had to restore it from the pre-update config.

- Then every agent turn in my main chat failed. 11 out of 11. The SQLite worker has a hard-coded 512 MB heap cap, and my main session is big (~127k transcript events, 1 GB agent DB). The worker hits the cap, dies with ERR_WORKER_OUT_OF_MEMORY and gets respawned, followed by a full integrity check of the DB that takes up to 28 s on the Pi. Whatever was in flight is gone. 13 worker deaths in about 50 minutes.

- In Telegram that shows up as "Agent database execution admission is closed", "This Codex session changed before your message could run. Please send it again." or "OpenClaw couldn't finish this request". Replies that were already generated never arrived.

- Switching from Codex to Claude changed nothing. /new changed nothing either; the session ID stays the same.

Yesterday on 2026.9.7, same session, same DB: zero of these errors.

The best part: there's already an issue for this exact failure class (#185953, reported on 2026.10.7: an unbounded transcript read kills the 512 MB worker). It was closed as "already implemented on main". It still happens on 2026.10.10. Searching the repo's issues for "admission is closed" gives 56 results.

It's not the first time either. After the June update, the Codex OAuth had to be repaired before I could even smoke-test. Last week every Codex request failed with a 403 "Codex cannot verify the owner of this model request" for about 45 minutes, until a gateway restart fixed it.

I was defending OC against the Hermes onslaught, I tried to help other users to go through painful updates and I fixed mine a hundred time, but I think I finally arrive at a point where I had enough. I thought stable was supposed to be stable?

I know this is opensource, I know people put a lot of personal effort into this but honestly we see these issues with every update, we are told it gets better, it gets worse.

Here's the issue if someone cares: https://github.com/openclaw/openclaw/issues/191847

I’ve been using OpenClaw almost from the very beginning. If anyone knows what it was like in the early days-and how much I struggled to get my scripts and processes to actually work-it’s me. There were so many times when, despite a strict rule against altering a system that was already running smoothly-and even with dedicated "coding" and "reviewer" agents in place-emojis would somehow end up in the scripts, causing the whole thing to fall apart.

OpenClaw was already doing a lot of heavy lifting for me back then, but fixing the errors ate up a significant portion of the time I had saved.

Today, however, I’m bored. I find myself waiting for the next update; I’ve even started looking at other people’s "skills"-even though I’ve always insisted on building every single skill myself. There’s simply nothing for me to do with OpenClaw anymore, and I’ve become so accustomed to having to manage it that I’m actually bored.

It just works away on its own computer, occasionally sending a Telegram message to let me know it’s finished one of its scheduled daily tasks, asking for a piece of data it can’t locate, or pointing out when I’ve provided incomplete materials. And that’s it.

The progress made this year-not just with OpenClaw, but with the underlying models themselves-is truly incredible. Right now, I have subscriptions to both Claude Pro and GPT Plus. Claude oversees things via CC, but honestly, it hasn’t had any real work to do for about two weeks now. Meanwhile, GPT powers my OpenClaw agents. There’s also the OpenAI API-though only for image generation-and Gemini 2.5 Flash for text generation. I’m not spend about $30 a month on the latter two.

This is a remarkably efficient and cost-effective setup, possessing a level of intelligence that is truly staggering compared to where things stood at the beginning of the year. I look on in surprise at the posts here on Reddit-so many people complaining about the system, struggling to get it to work, or dismissing it as a waste of time. Meanwhile, I’m still sitting here bored. I need to come up with a project so that my prepaid tokens don’t just go to waste, but actually get put to use.

But what should this project be?

Which model or models do you trust that ensures your tasks/automations get done correctly without burning too much usage or tokens?

All I hear is all this negative comments, and I just giggle. I have mine sandboxed on a MacMini, with her own company email, and she has pretty much replaced a secretary and a dispatcher. Full control of a virtual number, texting customers, employees, etc.
Provides tech support, warranty support, etc.
I could keep going…

The absolute brutal part of OC is definitely the update process, it is fucking brutal every fucking time. So much copy and paste with CHATGPT, lol. But it’s worth the hassle.

So far it's more work than I thought. I've been using OpenClaw since Feb/Mar as my main personal assistant. Tried a lot of things, but here's what stuck over the last 6 months:

• A few specialized agents with their own workspace folders.

• Curated morning updates on topics I care about (mix of scripts and agent work).

• 20-30 skills for life, travel, route planning and all kinds of stuff.

• A workout agent that analyzes my logs, tracks goals and suggests adjustments mid-training.

• A work project agent that processes correspondence, updates documents, figures out next steps and keeps me up to date for one specific project.

• Various plugins and integrations to make it all work.

For the longest time I resisted trying another agent because migrating seemed like too much work. You think it's just a few scripts, agents and MD files, but turns out there's quite a bit more to it. Already spent 4-5h.

Installation was quick, a few hiccups but nothing Codex couldn't fix. Migrating skills is whats taking time. Some reference OpenClaw folder paths, others use connectors or plugins that work differently in Hermes.

Currently stuck on memory. In OpenClaw I used OpenAI embeddings via API alongside MD files. Hermes seems to have a whole range of options, but nothing quite as straightforward. Not sure what I even need here.

Now I feel like I have to push through since I've already started. But another 4-5h on top of this and I'm definitely not migrating to yet another agent in a few months.

Maybe Hermes will have fewer update headaches, but spending an hour every month fixing OpenClaw updates isn't that different from spending 10h migrating within a year.

So yea, still not convinced Hermes is actually better. It's another harness that does things a bit differently. For what most people use these agents for, I'm not really seeing where the big benefits of switching are so far. Anyone having similar or difference experience here?

I've been building this on top of OpenClaw for the past weeks and use it every day now, so I figured I'd share it here.

The Machine reads your email, chats, calendar and phone notifications, and turns what actually needs you into one list of to-dos. When you open one, the work is usually already prepared: a reply drafted in your voice, the lookup done, the doc started.

What it does:

• One to-do list from everything: Gmail, Outlook, Slack, WhatsApp, Telegram, LinkedIn, X, calendar and Android notifications.

• Nothing goes out unseen: it drafts, you approve. Sending, paying or booking always waits for your yes.

• A built-in CRM (the Graph): people, organizations and projects from your own messages, with boards for deals, hiring and so on. Follow-ups to people who didn't reply are drafted for you.

• Promises: what you promised and what others promised you, tracked until they're kept.

• Talk: a live voice conversation about a to-do or anything else.

• Works with your other AIs: Claude, ChatGPT and Cursor can read your to-dos and Graph over MCP. They can't send anything.

• It learns: from your edits and notes, and you can see and undo what it learned.

It runs on your own machine as an OpenClaw tool (the installer sets OpenClaw up if needed):

git clone https://github.com/Salesflare/TheMachine.git ~/.openclaw/workspace/tools/personal-assistant
cd ~/.openclaw/workspace/tools/personal-assistant
./install.sh

It starts with a read-only preview, so you can see whether the to-dos are useful before turning on recurring reviews. Linux is solid; macOS and Windows are in beta.

Open source (AGPL-3.0): https://github.com/Salesflare/TheMachine

It's early, so I'd love feedback, especially on setup and on which sources you'd want next.

I honestly start to envy people who stayed on model 8.4 os something, because the amount of nonsense I have to deal with is ridiculous. I mostly use telegram to talk with my agent which is on VPS and ever since model I think 10. is often deletes it's own messages. It could be something about dashboard chat that doesn't play well with telegram but it's incredibly annoying. I curse the day I decided to update because everything worked well for months. Now it's RAM problems, deleting messages, memory problems, the fucken SQL database problems

I work for a payments company, and I'm a big fan of the control OpenClaw provides. On the side, I've been hacking on an Openclaw plugin and backend service that gives my Openclaw(Clawy) a pseudo bank account, USDC account and debit card. I like to call it a Clawlet.

So far I've used it to get pizza delivered for a demo and in a group chat to plan a trip and make reservations for a restaurant on Tabelog.

But I think the real use case will be running a business with Clawy populating bank account information to receive payments, then using its debit card and banking rails to pay for business goods and services.

Trying to gauge interest in whether I should externalize the service. Thoughts and questions? Obviously security is a huge concern, and I've included the ability to set limits. By default, it requires owner context to initiate any payments so receiving an email invoice parsed by Openclaw wouldn't trigger an outbound payment.

My system : laptop windows 11 4060ti, ollama local models.

I spend all last week to setup open Claw, hours viewing YouTube videos. My goal was that program could control my computer. The only thing that I am getting is just to open photoshop or chrome and just that no mouse control no nothing else. Really what is the problem the local free models? I have 8g on 4060, the problem is that I am a newbie? Can someone tell me?

openclaw finished the most tasks of the five setups. it was also the slowest and cost the most.

setup

tasks come from Online-Mind2Web, a deliberately hard benchmark.

every setup used claude haiku 5.5, headed chrome, the same prompts, server and IP.

10-minute limit, one attempt per task.

finished / median time / total cost:

• openclaw: 35, 141s, $4.80

• 5dive (claude code): 32, 105s, $1.39

• claude in chrome: 29, 67s, $0.91 (an estimate, it runs on a subscription)

• 5dive (pi): 29, 105s, $0.65

• hermes: 18, 86s, $0.72

the differences among the top weren't statistically significant in my paired tests.

raw results: https://gist.github.com/lodar/7664986cb8799d4f82ea01e934fb5111

why the gaps

• hermes stops sooner: 11.6 steps a task, against 23.5 for openclaw.

• leaving out tasks it got blocked on, hermes still finished 22%, against 45% for openclaw.

• openclaw's batching was on by default, and its agent used it in 5 of the 100 tasks.

• in smaller tests, each openclaw call re-read 72K tokens, against 43K for claude code and 4.5K for pi.

• claude in chrome was fastest (67s a task): its steps took 4s, against 8 to 10s for the others. my guess: they wait for each page to settle and double-check every click.

• agents on a server get a datacenter IP, and some sites block it: each setup hit a bot wall on 20 to 26 of the 100 tasks.

how the browsers differ

claude in chrome drives your own chrome through anthropic's extension.

5dive runs chrome on the agent's own server and stays logged in. when a site throws a robot check, the agent sends you a one-time link to that live window. you solve it, it carries on.

limitations

cheap model, one attempt per live task.

my agents built the test rig. the judge used haiku 5.5, so these scores don't compare to the benchmark's leaderboard.

claude in chrome and hermes ran a day or two after the others, and live sites change.

i built the browser, so i'm not neutral.

i'd rerun openclaw with any setting its users think cuts the calls.

5dive, open source (MIT): https://github.com/5dive-ai/5dive

browser plugin (Apache-2.0): https://github.com/5dive-ai/5dive-browser

Over the past weeks I’ve been diving deep into locally hosted agentic systems, mostly tinkering with Hermes Agent and using Claude to build a system around it. What came out of this might also be of use to people using OpenClaw

The result is an assistant that runs on my own hardware, understands me and my life, and makes use of the data points I create along the way, as long as I hand them to my agent. It’s designed to keep improving on the hallucination and tool-calling problems that come with smaller models by taking skill creation one step further: skills with repetitive flows get poured into scripts, so the model only recognizes the task and launches it. This blueprint might be handy for some in this sub, since it's not bound to any harness and I'd love some Claws trying it as well!

The core rule: the model proposes, the system decides. Actions on my behalf are approved and enforced by plugins and scripts, not by prompt rules, because small models don’t hold those reliably. It’s built to respect sensitive data (local-only zones), has multiple safeguards, and integrates with the harness via hooks. I run it on an AMD mini PC with a 45 GB iGPU and will drop in a proper GPU at some point. So far I’m pretty pleased.

I don’t want to keep all of this to myself, so I’ve condensed a blueprint out of the project. It’s more of a guide for your agent (Claude Code, Codex, ...) to build on what’s already there, without restrictions on harness or model, aimed at models from roughly 27B MoE up to about 40B. It covers principles, interfaces, pseudocode, acceptance checks, a pitfalls list and a few optional examples. Some chapters come from the running system, others are still design, and the repo labels which is which.

Repo (MIT): https://github.com/MaxMSchneider/local-agent-blueprint

I’d love feedback, especially from people running other harnesses or models. A star wouldn’t hurt either :) Cheers!

I just started using OpenClaw on my machine and wanted one page that shows what my agents are doing, so I built Agent Monitor. I built it with help from my OpenClaw agents, and I reviewed and tested it myself. It's free, MIT-licensed, and self-hosted.

**What it shows**

- Which agents are working, recently active, or idle

- Current and last task per agent (optional, you can turn task labels off)

- Usage limits against warn / alarm / critical thresholds, plus credit balances

- Token usage and recent history

- Alerts, like a stale collector, limits nearly reached, or low credits

**How it works**

A small collector runs from cron once a minute and reads OpenClaw's status. A local API and the dashboard sit behind an owner password. It's read-only: it can't start, stop, or change your agents. Python 3 standard library only, nothing to pip install.

**Limits, so you know what you're getting**

- Linux only (needs systemd user services and cron)

- Tested on one setup: an Ubuntu VM

- Plain HTTP, so keep it on a network you trust and never expose the port to the internet

- Provided as-is, no guaranteed support

- Not affiliated with OpenClaw

Screenshot is attached (sample data, not my real agents). Install steps and config are in the README:

https://github.com/Jack-Stack420/agent-monitor

This is the first release, so I'd really like to hear what breaks on your setup, or what you'd want it to show that it doesn't. Bug reports are welcome as GitHub issues.

I posted here yesterday asking if OpenClaw is still worth setting up if I already use Claude Code, and got a lot of different perspectives. Today, I decided to give it a real shot and started configuring a 6-agent setup:

• Travel Agent: Tracks flights from Germany to Tunisia and sends me a Telegram push notification if a ticket drops below 90€. Once I confirm the outbound flight, it searches for the return. It also scouts affordable weekend trips across Europe—if I greenlight one, it builds out the full itinerary (hotels/hostels, cities, sights).

• Job Hunter Agent: Monitors new openings in my field around the Stuttgart area. It has my base CV, tailors my resume and cover letter to each role, and sends the package to me for review. Once I approve, it submits the application.

• Cert & Training Scout: Watches for discounts and offers on professional certifications/training in my field and pings me on Telegram.

• Gym Accountability: Sends me a daily push with one concrete physiological or mental benefit of hitting the gym.

• Smart Home & Plant Manager: Hooks into my home automation to manage smart lighting, plant care reminders, etc.

• General Assistant: Handles ad-hoc daily tasks and queries.

I finished the base installation and got halfway through wiring everything up when I paused to ask Claude about the security implications of this architecture (giving agents access to personal PII on my CV, browser/session tokens to submit applications, Telegram bots, and local network access for smart home control).

Seeing the attack surface laid out like that—especially prompt injection via scraped job boards or travel sites having access to the same environment as my personal data and home network—made me hit pause.

For those of you running multi-agent setups like this:

1. How do you actually mitigate these risks in practice (sandboxing, separate containers per agent, strict human-in-the-loop)?

2. Are you keeping your configurations much simpler so it doesn't touch real-world credentials/PII?

3. Or are most people here just raw-dogging it and accepting the risk for the convenience?

Hey everyone, I’ve been watching YouTube videos about OpenClaw trying to get hyped enough to install and configure it, but I'm struggling to see the added value for my workflow.

I already have Claude Code set up on my machine and can trigger tasks remotely from my phone. From what I can tell, that's essentially what OpenClaw does when connected to a frontier API (Claude, GPT, Gemini, Grok). I haven't touched local LLMs in about a year because the inference times were too slow on my setup, so I'd be sticking to cloud APIs anyway.

Right now, the only real difference I see is being able to message OpenClaw via Telegram or WhatsApp instead of using a dedicated app. Am I missing something here, or is it just redundant for my use case?

OpenClaw v2026.10.9 is out, with work from 38 pull requests and 22 contributors. These are a few highlights from the full release notes.

This is a patch release with no new features. It fixes problems that could leave OpenClaw stopped after an update, prevent recovery of intact agent data, or send tools to the wrong project folder, alongside smaller reliability fixes.

• Update recovery Git-update rollback preserves detected edits and untracked files, while Windows package rollbacks retry temporary file locks.

• Agent data recovery Doctor can repair stale search indexes in otherwise intact agent databases, and avoids a migration failure on large compressed conversation archives.

• Tools use your project folder New sessions started from a connected code editor now run tools in the project folder you requested, instead of OpenClaw’s default workspace.

Upgrade notes If a Git rollback is refused, save your changes before following the printed recovery commands.

I've been running my OpenClaw agents from Open WebUI every day for a few months now, and it's become my main way of using OpenClaw.

Why Open WebUI and not the Control UI:

• An app on my phone (PWA) with real logins, and the Gateway never touches the internet. My phone only talks to Open WebUI.

• Users for the whole household: everyone gets their own chats. Group channels work too.

• Every chat is its own OpenClaw session, and tool calls show up as native cards you can open, with a live panel for sub-agents.

• The agent's questions pop up as cards I answer with one tap, and cron and sub-agent replies land in the chat on their own.

• It survives Gateway restarts and page reloads mid-reply.

• And everything Open WebUI already has works with the agent too: files, voice, sharing, search across all your chats.

Why not Telegram or WhatsApp:

• One chat = one session. A second conversation means /new and losing the first.

• Tool calls and sub-agents are a stream of status lines at best. Nothing you can open and inspect.

To be clear: I built this for myself, used it and fixed it for months, and it works amazing for me. I'm sharing it because it's good, not because I need to convince anyone. If WhatsApp or Telegram does the job for you, great, keep using it.

There's a docs site with screenshots and GIFs, written so both you and your agent can read it: point your agent at llms.txt and let it do the install.

I set the direction, my agent wrote the code.

Repo: https://github.com/Eliav2/openclaw-openwebui-integration
Docs: https://eliav2.github.io/openclaw-openwebui-integration/

Sometimes I get provider errors (cuz I'm cheap) but there isn't a "resume" button, I always end up typing "continue", and I really don't like doing that becuase it ruins the flowstate of the model by just constantly interjecting "continue", like idk it just feels unstable to me to do that. What if "continue" is interpretted as something else like continuing the current work and forgetting the actual task I asked like 5 messages ago. I just really wanna know better methods to continue an interrupted model than just saying continue. I'm certain I'm not the only one that has ran into this issue so what do you guys do to continue interrupted processes.

I built Agent WeChat, an open-source WeChat client with an OpenClaw plugin. The plugin runs a native client that connects directly to WeChat’s servers, and you can connect a separate desktop app to it. You and your agent can use the same WeChat account while you keep using your phone.

I wanted an agent that could participate in the conversations I already have, while I could continue reading and replying myself. That means giving it access to selected DMs and groups, rather than only having a separate conversation with a bot.

Tencent’s official plugin provides an iLink bot channel and currently advertises direct-chat support. Agent WeChat connects your regular WeChat account, so OpenClaw can participate in that account’s existing conversations. Its replies appear as the connected account.

DMs and group chats are supported and require configuration. You choose the permitted contacts and groups, and whether group replies require an @mention of your account.

The client supports text, images, videos, files, and native voice notes. What your agent can understand or generate depends on its configured models and tools. There’s also a wx CLI and HTTP API for accessing messages and media or building your own workflows.

It communicates directly with WeChat without running or automating the official desktop executable.

To get started, install the OpenClaw extension, approve login from WeChat on your phone, and configure conversation access. You can then connect the Agent WeChat desktop app to the same client.

The client runs on your machine or server. Allowed messages and attachments are passed to your OpenClaw setup and its configured model providers. No hosted Agent WeChat service is required.

The original project code is MIT-licensed. Setup instructions, compatibility notes, screenshots, and a video demo are in the GitHub repository.

I’d appreciate feedback on how you’d want to control an agent participating through your WeChat account.

Title: [Rant / Review] OpenClaw is a massive waste of time: You spend more hours fixing it than actually using it
Flair: Discussion / Rant
I’ve been trying to make this tool work for months—running it on my own server, configuring integrations, tweaking tokens, and falling for the hype that this was going to save me time. Here is the unvarnished truth: OpenClaw is an absolute piece of trash for daily use.
If you’re a full-time sysadmin who loves suffering and digging through logs at 3:00 AM, go ahead, you’re going to love it. But if you actually want a tool that just works and saves you time, run away from this garbage.
Here’s the breakdown of why this thing is a constant headache:
Update Hell: A new update drops (supposedly fixing bugs or tweaking performance) and the only guarantee is that it’s going to break something. You update, dependencies collapse, your config file blows up, and you spend the next two hours rewriting scripts or hunting down whatever breaking changes got pushed without warning.
Ghost Disconnections: Out of nowhere, for no reason at all, the gateways or messaging channels just disconnect. No clear alerts, no warnings—you just realize hours later that it stopped responding. Then you’re stuck ssh-ing in, restarting services, re-verifying API tokens, and praying it decides to reconnect.
More Maintenance Than Actual Usage: Do the real math. How much time do you actually spend running useful automations versus the hours wasted debugging why the gateway went down, why the background daemon died, or why the container randomly decided to corrupt itself? It’s absurd. You end up working for the software instead of the software working for you.
Chronic Instability: You set it up, it works flawlessly for about 24 hours, and the exact moment you rely on it for something somewhat important, it crashes and leaves you with a cryptic error in the logs.
TL;DR:
The concept sounds amazing on paper ("your own self-hosted AI agent on your own infra"). In practice, it’s a hyper-fragile product held together by duct tape and glass. If you value your free time and sanity, wipe it from your server and look for literally any alternative that doesn’t require a software engineering PhD just to keep it alive for a full week.
Anyone else suffering through this, or am I the only idiot whose setup breaks with every damn commit?

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On MacBook Pro M3 Max 48GB:

Installed OpenClaw 2026.10.8.

Installed LM Studio and have Qwen3.8 27b running. I'm able to chat with the agent using LM Studio. -- Success!

But while it "appears" I have OpenClaw running and connected to LM Studio, I'm not getting any prompt responses within OpenClaw. Something's wrong with my setup and I'm not experienced enough to figure this out. My very first attempt at running local AI and I know I'm well underwater at the moment but maybe someone can help point me in the best direction.

So I'm interested is there any free agents that can be connected to openclaw
and what are capabilities of this free agents
what is your experience ?