I’ve been using OpenClaw heavily on my local machine for a while now. At first, I treated it like any other AI tool. But after wiring it into Telegram, Obsidian, scheduled tasks, local models, and my content workflow, I began to see it as a system that could keep working between conversations. It can receive messages, call tools, run scheduled tasks, dispatch to different models, build long-term memory, write results back to Obsidian, and delegate complex tasks to other agents. If you’re only using it as a chatbot, you’re using only part of its capabilities.
Historical and version note (reviewed October 2026): This post retains the personal experience, hardware, and file counts recorded on March 8, 2026. The original did not identify the exact OpenClaw version in use. Technical updates below reference OpenClaw v2026.10.3, without treating later behavior as a verified result from that earlier setup. Illustrations are conceptual, not product screenshots or execution logs.
I work on Android system performance, write, run communities, maintain a knowledge base, follow projects, and code. What stood out to me was that AI could keep moving this work forward between conversations.
1. When Did I Actually Start Using OpenClaw?
The initialization records in my local environment point to the evening of February 16, 2026, when I started running OpenClaw on my own machine. I kept adding it to my routine: messaging, scheduled tasks, Obsidian, daily and weekly reports, GitHub monitoring, paper reading, content collection, writing, and reviews. It became the place where I coordinated those tasks, kept memory, and handed coding work to Claude Code.
2. What Does OpenClaw Actually Do for an Android System Engineer?
For Android engineering, performance analysis, and stability work, I find it most useful for recurring tasks that span several tools and days. I use it in four areas.
Category 1: Information Flow Automation
Daily tech briefings, daily news digests, RSS scraping, high-value content filtering, GitHub issue/PR monitoring, daily paper reading – none of these are things I couldn’t do before. The problem was that they’re too fragmented, and I’d inevitably drop them. OpenClaw turns these from “powered by willpower” into “powered by system defaults.”
Category 2: Continuous Knowledge Base Maintenance
I consume a lot of content: technical articles, WeChat public accounts, long-form posts on X, RSS feeds, project materials, my own thoughts. The biggest problem used to be: I’d save things but never organize them, organize them but never find them again, find them but struggle to reuse them. After connecting OpenClaw, it continuously does incremental organization, structural repair, high-value archiving, content review, and memory maintenance. For me, the useful part is being able to find a piece of knowledge and use it again later.
Category 3: Daily Reports, Retrospectives, and Weekly Reviews
This is the part most people overlook, but it’s also the one with the most compounding returns. I now have OpenClaw running daily morning reminders, end-of-day reports, evening retrospectives, weekly distillations, project reviews, three-tier memory maintenance, and meditation/evolution logs. The thing that hooked me most about OpenClaw is this: It takes all those things I know are important but can never sustain as habits, and turns them into things the system does for me automatically.
Category 4: Engineering Collaboration
It can also participate in engineering work – tracking GitHub repo changes, patrolling issues and PRs, organizing code context, and calling other coding agents to do work. In my workflow, OpenClaw handles orchestration while Claude Code handles intensive terminal coding. Claude Code is more like a top-tier programmer sitting in your terminal. OpenClaw is more like an AI operations system.
3. Why Do I Always Keep Claude Code Nearby?
Because my early OpenClaw setup did break: upgrades caused errors, model configurations went wrong, scheduled tasks behaved unexpectedly, certain tool chains stopped working, certain file paths failed to write, and old sessions did not always sync with new configurations. If you only have OpenClaw itself, many of these problems are extremely frustrating. But if you have Claude Code on the side, things get much simpler.
My division of labor is clear: OpenClaw handles orchestration, memory, scheduling, archiving, messaging, and workflows. Claude Code handles bug fixes, log inspection, error diagnosis, complex code changes, and post-upgrade firefighting.
4. What’s My Local Setup?
My current machine is a Mac Studio M1 Ultra with 64GB of RAM and a 48-core GPU. This configuration is very comfortable for local agent workflows because it lets me run OpenClaw as a persistent service alongside Ollama local models, Obsidian, browser automation, and various scripts and knowledge base tasks. I run a cloud + local hybrid.
The Model Strategy Is Simple
High-quality tasks go to the cloud (long-form writing, weekly reports, complex summaries, critical decisions). High-frequency grunt work runs locally (structured tasks, patrols, batch processing, daily workers). My local model tiers look like this: 2B for status checks, 4B for lightweight patrols, 9B as the high-frequency workhorse, and 27B/35B for heavier local tasks. On my machine, 9B is the best value daily workhorse.
These parameter-count tiers describe my historical workload choices, not a reproducible benchmark. The original does not specify model identities, quantization, context length, or concurrency. Similar parameter counts do not guarantee similar tool use, writing, or reasoning. Test real task completion, latency, and memory use rather than only whether a model loads.
5. The Most Important Part of OpenClaw Isn’t the Models – It’s the Core Files
When people first encounter OpenClaw, they focus on models, tools, and the command line. But I’ve come to believe that its real power lies in its file-based rules. The files I care most about are:
SOUL.md: Defines its personality, style, and working postureUSER.md: Defines who it’s helping and what the goals areAGENTS.md: Defines startup procedures, memory rules, safety boundaries, and group chat behaviorMEMORY.md + memory/YYYY-MM-DD.md: One stores long-term memory, the other stores what happened each dayTOOLS.md: Records environment-specific information unique to this machine
These files became an important part of keeping my setup consistent: personality, memory, and working rules that I could read and edit.
v2026.10.3 update: The versioned workspace guide places local tool conventions in AGENTS.md under ## Tools, rather than listing the older TOOLS.md convention as a required file. The list above preserves my earlier organization. These files guide behavior; they do not enable tools or enforce permissions. Memory must actually be written and retrievable, and a file’s existence does not mean it is fully loaded on every turn.
6. Why Does the Skill System Matter?
Many people think of Skills as “plugins,” but I think a more accurate framing is capability packages. A Skill usually isn’t just an extra button. It bundles together what scenarios it fits, how it calls tools, whether it depends on scripts, what the boundaries and caveats are, and when it should or shouldn’t be used. Capabilities like Obsidian integration, coding-agent, RSS, and skill auditing – once properly installed, OpenClaw stops just chatting and starts actually working. But my advice is also clear: Don’t install Skills just to show off. Start with core capabilities, run them for a couple of days to confirm they’re stable, then expand. For Skills involving elevated permissions or external connections, audit first, then decide whether to enable them.
In the v2026.10.3 skills guide, a skill centers on SKILL.md instructions with metadata and optional scripts/resources. Installing it does not by itself grant tools, credentials, or host permissions. Plugins, tool authorization, and skills are separate layers; required binaries, services, and credentials still need configuration. A skill allowlist does not replace constraints on exec.
7. Telegram: Single Agent with Multiple Group Chats Works, but Split by Role Later
Telegram gave me more ways to organize the work than a single private chat. The same system can serve several conversations, with different tasks assigned to them.
Option A: Single Agent, Multiple Group Chats
The upside is simplicity: fast setup, low initial cost, and an easy way to validate the workflow. But session isolation differs from shared configuration. In the v2026.10.3 session guide, groups default to separate sessions (session.groupScope: "per-group"); chat histories do not inherently merge. Explicit routing to main changes that behavior.
Groups handled by one agent may still share persona rules, workspace, accessible memory, and tool configuration. Those shared resources need attention when writing, reporting, and technical Q&A require different rules or data scopes.
Option B: Split Into Multiple Agents by Role
This is the approach I’ve come to prefer. For example, main handles the primary private chat, daily handles scheduled tasks, daily reports, patrols, and knowledge base work, writer handles writing and content refinement, and specialized agents handle specific groups, business lines, or projects. Separate agents can organize workspaces, sessions, and roles, but routing bindings, workspace paths, credential inheritance, and tool permissions must match that intent. Different names alone do not guarantee isolation. My recommendation: Start with a single agent and multiple group chats early on, but split by role as things mature.
The figure illustrates a workflow cycle, not Telegram routing configuration. Splitting by role is my workload preference, not a requirement for every deployment. The multi-agent guide states that a workspace is a default cwd, not a hard sandbox. The security model does not treat a shared Gateway as an isolation boundary between mutually untrusted users.
8. Security Is Not an Appendix – It’s a Prerequisite
I strongly recommend being upfront about this from the start. Once OpenClaw begins accessing the local file system, browser, command execution, external messaging, and a mix of local and cloud models, it’s no longer a “harmless chat box.” It becomes a system with real agency. At that point, security boundaries must be established first.
The principles I’ve come to follow are:
- Layer your long-term memory: Something like
MEMORY.md, which contains more personal and stable information, should ideally only be used in the main session – don’t let it be read freely in group chat contexts - Be cautious with outbound actions: Emails, public posts, social media publishing – these should require confirmation by default. Don’t let it automatically publish half-finished work
- Don’t overstep in group chats: In a group, it’s a participant, not your spokesperson, and certainly not you
- Don’t install Skills carelessly: Especially those involving network access, script execution, or local path reading – audit first, decide later
- Don’t blindly give small local models broad permissions: Especially when accessing web pages, executing commands, or reading/writing paths, be clear about workspace and tool boundaries
If you plan to connect even more external capabilities, browser automation, or overseas models down the road, you’ll typically also need a stable network access setup. But the more capabilities you add, the more important it is to design the boundaries first.
These rules also need enforcement through tool policies, sandboxing, host permissions, and allowed destinations. Writing “do not read private memory” or “confirm before sending” in Markdown does not establish an enforced boundary. The v2026.10.3 sandbox guide says sandboxing is off by default in regular configuration. A local Gateway also does not mean all data stays local: cloud models, messaging platforms, and external tools have their own transmission boundaries.
9. The Really Critical Question: What Has It Actually Produced?
If this post only described what OpenClaw “can do,” readers might still see it as a conceptual system. What’s actually convincing is what this system has already deposited into Obsidian. As of the time I’m writing this, my Obsidian vault contains these tangible outputs:
- Under
OpenClaw定时任务/there are 214 Markdown files, from the reporting, review, archiving, and patrol workflows described here - Under
Personal-Knowlodge/source/there are 1,760 Markdown files; the knowledge base was continuing to grow - Under
X 文章/there are 115 Markdown files – high-value external content has been consolidated into a long-term resource library - Under
论文/there are 4 standardized paper directories, each organized as “original PDF + translation + deep reading” - Under
小说工坊/夜航之上（分章）/there are 58 chapter-related files – it has entered a long-term creative workflow, not just technical tasks Ebook/already contains actual EPUB deliverables – not just notes, but distributable formats
To give a few concrete examples: OpenClaw定时任务/每日论文精读（Android+AI）/2026-03-08-每日论文精读（Android+AI）.md, Personal-Knowlodge/source/2026-03-08_wechat_Android_JNI原理分析.md, 论文/AI-2026-03-08-Agentic-Reasoning-Framework/01-paper.pdf + 02-翻译.md + 03-精读.md, 小说工坊/夜航之上（分章）/第018章-光标闪烁.md. This is what I value most about OpenClaw: the results stay in my library, where I can find them and use them in later work.
These counts remain the original writing-time record; the illustration depicts how outputs can be organized. Files on disk do not prove every scheduled run succeeded, every artifact is complete, or every message was delivered. Check execution records, artifacts, and delivery results separately.
10. What Pitfalls Did I Hit?
This section is critical, because it determines whether you’ll give up halfway.
Pitfall 1: It’s Genuinely Unstable Early On
You need to accept a reality: OpenClaw is powerful, but it is not “zero maintenance.” My pitfalls have included upgrade errors, misconfigured model allowlists, model switches that don’t take effect in old sessions, scheduled tasks that run but fail to write to disk, external path permission issues, and browser policy problems.
Pitfall 2: Don’t Mix Node Environments
I eventually unified my runtime environment under Homebrew Node and stopped mixing in nvm. That simplified my path management; it does not mean nvm is inherently unsupported.
Version update: The v2026.10.3 Node guide lists Node 24.16+ on 24.x or 26.1+, recommends Node 26, and requires compatible SQLite behavior. Its package.json declares >=24.16.0 <25 || >=26.1.0. During upgrades, verify the executable path/version used by both the CLI and persistent Gateway, not only node -v in an interactive terminal.
Pitfall 3: Don’t Get Sloppy with Obsidian External Paths
This one gave me a lot of trouble. Absolute paths and exec + python/pathlib helped me handle an external directory at the time. They are not universal OpenClaw requirements or a way around access controls.
- Relative paths normally resolve against the configured workspace; an explicit absolute path makes an external Vault target easier to verify.
- Write access depends on tool policy, sandbox mounts, and host permissions.
write/editis not inherently unreliable. exec + Pythonis also subject to its effective permissions. Use an authorized method appropriate to the task rather than broadening host access to solve a path issue.- Append or update specific content according to the file’s purpose. Concurrent appends can still duplicate or conflict; verify the destination, content, and completeness afterward.
Pitfall 4: More Automation Isn’t Always Better
It’s easy to get carried away early on, wanting to automate everything. But eventually you’ll find that too many tasks lead to duplication, conflicts, noise, rising token costs, and outputs nobody actually reads. I would start with the few recurring tasks whose results I actually use.
11. Who Is OpenClaw Best Suited For?
I think there are three types of people it fits best. The first are people with continuous input and output needs – engineers, indie developers, technical bloggers, community managers, research-oriented creators. The second are people who enjoy tinkering with workflows – if you have a natural interest in automation, systematization, and structure, OpenClaw will feel more natural the more you use it. The third are people who want to actually integrate AI into their workflow – if you don’t just want to ask a few questions but genuinely want continuous monitoring, automated organization, proactive reminders, scheduled output, long-term memory, and cross-platform content coordination, OpenClaw will serve you well.
12. Who Might Not Be a Good Fit?
I should be honest here too. People who just want a ChatGPT replacement, people who don’t want to maintain environments or read logs, and people without a sustained workflow – these three groups won’t necessarily fail at it, but they probably won’t experience its real value.
13. Hardware Recommendations
If you ask me what machine to get, I’d break it into three tiers:
- Entry tier: 16GB RAM, primarily running cloud models, with only light local assistance
- Practical tier: 32GB RAM, can run some local models, handles light to medium workloads
- Comfortable tier: 64GB and above, Mac Studio or high-end workstation class, cloud + local hybrid running persistently
If you truly want to run OpenClaw as a “long-term persistent, cloud + local hybrid, multi-task parallel” system, the 64GB tier is where the experience becomes noticeably more stable.
These tiers describe my experience running local models alongside other apps, not OpenClaw’s minimum RAM or a guarantee that a model fits. The v2026.10.3 local-model guide ties requirements to weights, runtime/quantization, context, and concurrent work. Cloud-model use does not itself require a 64GB machine. Measure the intended workload before choosing hardware.
14. My Final Take on OpenClaw
What keeps me using OpenClaw is the work it carries forward: collecting information, organizing it, filing it, following up, and preparing material for the next task. Getting there took maintenance. It crashed, produced errors, and needed fixes. Once it was running well, though, I no longer had to initiate every step myself. That is why the setup was worth the effort for me.
Closing Thoughts
My experience may be useful if you have recurring work like this and are willing to maintain the setup.
Related Posts
About Me && Blog
Below is my personal intro and related links. I look forward to exchanging ideas with fellow professionals. “When three walk together, one can always be my teacher!”
- Blogger Intro
- Blog Content Navigation: A guide for my blog content.
- Curated Excellent Blog Articles - Android Performance Optimization Must-Knows
- Android Performance Optimization Knowledge Planet — historical community introduction; closed in 2026.
One walks faster alone, but a group walks further together.
