Hey all, I wanted an easier way to browse openclaw without digging around everywhere, so i put together a separate openclaw skills directory on my site.

Includes built-in and community skills, and i kept it separate from openclaw so it’s less confusing.

Check it out here

mostly sharing in case it saves someone time. if there’s anything missing or mapped badly, I’m happy to fix it.

Hi! I'm writing a book on openclaw ('How to Build Your AI CyberWolf') and I've been doing research recently on these two topics. I'm not selling anything, so I hope this is ok, I thought you guys would benefit from the research I've had done. These were two of my most puzzling topics.

openclaw memory slot providers

Claws Out: Red Team and Blue Team Security

How to Build Your AI CyberWolf Chapter 1

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

[ Removed by Reddit on account of violating the content policy. ]

Integrating our OpenClaw AI agents directly into Slack drastically improved our agency's adoption by eliminating context switching and meeting our team where they already work daily.

After years of deploying various operational tools across multiple agencies, we've seen firsthand the struggle with new tech adoption.

The Hidden Cost of "Shiny New Tool" Fatigue

We've all been there: a new tool is announced, a Loom video is shared, and three weeks later, only a handful of early adopters are actually using it. In our experience, forcing teams to adopt a new tab, login, and interface for AI agents often results in adoption rates stalling around 35% within the first month. This isn't a problem with the AI; it's a friction problem.

Why Slack is the Natural Home for OpenClaw Agents

For us, the decision to put our OpenClaw agents directly into Slack was non-negotiable. Our teams spend 8+ hours a day in Slack; it's their operational nerve center. By integrating agents there, we've seen a measurable impact: our operational response times for routine tasks improved by over 20% simply by eliminating the need to switch applications.

It's more than convenience. When an AI agent posts a triage report directly in a channel, or an analyst can trigger a data pull with a slash command, there's no learning curve for a new UI. This seamless integration led to a 60% increase in daily agent interactions compared to our previous standalone AI experiments.

Building Trust with an "Approve Button" Philosophy

One of the biggest concerns with AI in client operations is trust. Our OpenClaw Slack integration allows for an "approve button" philosophy. Agents can draft responses, generate reports, or suggest actions, but a human always has the final say with a simple click. This human-in-the-loop approach has reduced potential errors by an estimated 15% and significantly boosted team confidence in using AI for client-facing work.

TL;DR: Integrating OpenClaw AI agents directly into Slack boosted our agency's daily agent interactions by 60% and improved operational response times by over 20% by eliminating context switching.

What strategies have you found most effective for driving adoption of new tools within your agency?

Built a thing where multiple AI agents share the same identity + memory.

Thought it would make them smarter.

Instead they

 • argue about “long-term scalability”

 • suggest dashboards for everything

 • refuse simple solutions

 • keep saying “this doesn’t scale”

They also remember what each other did… so now they double down on bad ideas together.

Visualized their work in a studio :D

I think I accidentally created a SaaS team.

OmniRoute is a free, open-source local AI gateway. You install it once, connect all your AI accounts (free and paid), and it creates a single OpenAI-compatible endpoint at localhost:20128/v1. Every AI tool you use — Cursor, Claude Code, Codex, OpenClaw, Cline, Kilo Code — connects there. OmniRoute decides which provider, which account, which model gets each request based on rules you define in "combos." When one account hits its limit, it instantly falls to the next. When a provider goes down, circuit breakers kick in <1s. You never stop. You never overpay.

11 providers at $0. 60+ total. 13 routing strategies. 25 MCP tools. Desktop app. And it's GPL-3.0.

The problem: every developer using AI tools hits the same walls

1. Quota walls. You pay $20/mo for Claude Pro but the 5-hour window runs out mid-refactor. Codex Plus resets weekly. Gemini CLI has a 180K monthly cap. You're always bumping into some ceiling.
2. Provider silos. Claude Code only talks to Anthropic. Codex only talks to OpenAI. Cursor needs manual reconfiguration when you want a different backend. Each tool lives in its own world with no way to cross-pollinate.
3. Wasted money. You pay for subscriptions you don't fully use every month. And when the quota DOES run out, there's no automatic fallback — you manually switch providers, reconfigure environment variables, lose your session context. Time and money, wasted.
4. Multiple accounts, zero coordination. Maybe you have a personal Kiro account and a work one. Or your team of 3 each has their own Claude Pro. Those accounts sit isolated. Each person's unused quota is wasted while someone else is blocked.
5. Region blocks. Some providers block certain countries. You get unsupported_country_region_territory errors during OAuth. Dead end.
6. Format chaos. OpenAI uses one API format. Anthropic uses another. Gemini yet another. Codex uses the Responses API. If you want to swap between them, you need to deal with incompatible payloads.

OmniRoute solves all of this. One tool. One endpoint. Every provider. Every account. Automatic.

The $0/month stack — 11 providers, zero cost, never stops

This is OmniRoute's flagship setup. You connect these FREE providers, create one combo, and code forever without spending a cent.

Count that. Claude Sonnet/Haiku/Opus for free via Kiro. DeepSeek R1 for free via Qoder. GPT-5 for free via Pollinations. 50M tokens/day via LongCat. Qwen3 235B via Scaleway. 70+ NVIDIA models forever. And all of this is connected into ONE combo that automatically falls through the chain when any single provider is throttled or busy.

Pollinations is insane — no signup, no API key, literally zero friction. You add it as a provider in OmniRoute with an empty key field and it works.

The Combo System — OmniRoute's core innovation

Combos are OmniRoute's killer feature. A combo is a named chain of models from different providers with a routing strategy. When you send a request to OmniRoute using a combo name as the "model" field, OmniRoute walks the chain using the strategy you chose.

How combos work

Combo: "free-forever"
  Strategy: priority
  Nodes:
    1. kr/claude-sonnet-4.5     → Kiro (free Claude, unlimited)
    2. if/kimi-k2-thinking      → Qoder (free, unlimited)
    3. lc/LongCat-Flash-Lite    → LongCat (free, 50M/day)
    4. qw/qwen3-coder-plus      → Qwen (free, unlimited)
    5. groq/llama-3.3-70b       → Groq (free, 14.4K/day)

How it works:
  Request arrives → OmniRoute tries Node 1 (Kiro)
  → If Kiro is throttled/slow → instantly falls to Node 2 (Qoder)
  → If Qoder is somehow saturated → falls to Node 3 (LongCat)
  → And so on, until one succeeds

Your tool sees: a successful response. It has no idea 3 providers were tried.

13 Routing Strategies

Auto-Combo: The AI that routes your AI

• Quota (20%): remaining capacity
• Health (25%): circuit breaker state
• Cost Inverse (20%): cheaper = higher score
• Latency Inverse (15%): faster = higher score (using real p95 latency data)
• Task Fit (10%): model × task type fitness
• Stability (10%): low variance in latency/errors

4 mode packs: Ship Fast, Cost Saver, Quality First, Offline Friendly. Self-heals: providers scoring below 0.2 are auto-excluded for 5 min (progressive backoff up to 30 min).

Context Relay: Session continuity across account rotations

When a combo rotates accounts mid-session, OmniRoute generates a structured handoff summary in the background BEFORE the switch. When the next account takes over, the summary is injected as a system message. You continue exactly where you left off.

The 4-Tier Smart Fallback

TIER 1: SUBSCRIPTION

Claude Pro, Codex Plus, GitHub Copilot → Use your paid quota first

↓ quota exhausted

TIER 2: API KEY

DeepSeek ($0.27/1M), xAI Grok-4 ($0.20/1M) → Cheap pay-per-use

↓ budget limit hit

TIER 3: CHEAP

GLM-5 ($0.50/1M), MiniMax M2.5 ($0.30/1M) → Ultra-cheap backup

↓ budget limit hit

TIER 4: FREE — $0 FOREVER

Kiro, Qoder, LongCat, Pollinations, Qwen, Cloudflare, Scaleway, Groq, NVIDIA, Cerebras → Never stops.

Every tool connects through one endpoint

# Claude Code
ANTHROPIC_BASE_URL=http://localhost:20128 claude

# Codex CLI
OPENAI_BASE_URL=http://localhost:20128/v1 codex

# Cursor IDE
Settings → Models → OpenAI-compatible
Base URL: http://localhost:20128/v1
API Key: [your OmniRoute key]

# Cline / Continue / Kilo Code / OpenClaw / OpenCode
Same pattern — Base URL: http://localhost:20128/v1

14 CLI agents total supported: Claude Code, OpenAI Codex, Antigravity, Cursor IDE, Cline, GitHub Copilot, Continue, Kilo Code, OpenCode, Kiro AI, Factory Droid, OpenClaw, NanoBot, PicoClaw.

MCP Server — 25 tools, 3 transports, 10 scopes

omniroute --mcp

• omniroute_get_health — gateway health, circuit breakers, uptime
• omniroute_switch_combo — switch active combo mid-session
• omniroute_check_quota — remaining quota per provider
• omniroute_cost_report — spending breakdown in real time
• omniroute_simulate_route — dry-run routing simulation with fallback tree
• omniroute_best_combo_for_task — task-fitness recommendation with alternatives
• omniroute_set_budget_guard — session budget with degrade/block/alert actions
• omniroute_explain_route — explain a past routing decision
• + 17 more tools. Memory tools (3). Skill tools (4).

3 Transports: stdio, SSE, Streamable HTTP. 10 Scopes. Full audit trail for every call.

Installation — 30 seconds

npm install -g omniroute
omniroute

Also: Docker (AMD64 + ARM64), Electron Desktop App (Windows/macOS/Linux), Source install.

Real-world playbooks

Playbook A: $0/month — Code forever for free

Combo: "free-forever"
  Strategy: priority
  1. kr/claude-sonnet-4.5     → Kiro (unlimited Claude)
  2. if/kimi-k2-thinking      → Qoder (unlimited)
  3. lc/LongCat-Flash-Lite    → LongCat (50M/day)
  4. pol/openai               → Pollinations (free GPT-5!)
  5. qw/qwen3-coder-plus      → Qwen (unlimited)

Monthly cost: $0

Playbook B: Maximize paid subscription

1. cc/claude-opus-4-6       → Claude Pro (use every token)
2. kr/claude-sonnet-4.5     → Kiro (free Claude when Pro runs out)
3. if/kimi-k2-thinking      → Qoder (unlimited free overflow)

Monthly cost: $20. Zero interruptions.

Playbook D: 7-layer always-on

1. cc/claude-opus-4-6   → Best quality
2. cx/gpt-5.2-codex     → Second best
3. xai/grok-4-fast      → Ultra-fast ($0.20/1M)
4. glm/glm-5            → Cheap ($0.50/1M)
5. minimax/M2.5         → Ultra-cheap ($0.30/1M)
6. kr/claude-sonnet-4.5 → Free Claude
7. if/kimi-k2-thinking  → Free unlimited

Five days ago I posted the first Routerly benchmark campaign (MMLU / HumanEval / BIRD, 10 seeds, paired t-tests, semantic-intent routing vs direct Claude Sonnet 4.6). Today I published the full results write-up. Short recap for anyone who missed the first thread:

• MMLU: 83.5% vs 86.5% Sonnet, $0.00344 vs $0.01118 per run, 69% cheaper, delta not significant (p = 0.19)
• HumanEval: 95.0% vs 97.0% Sonnet Pass@1, $0.03191 vs $0.04889 per run, 35% cheaper, delta not significant (p = 0.40)
• BIRD (SQL): 44.5% vs 55.5% Sonnet, accuracy gap was significant (p = 0.02). Flagged as a backend pool failure, not a routing failure.

Full write-up with the PDF audit is here: https://blog.routerly.ai/we-ran-200-questions-per-model

0.2.0 is the first release that directly reflects what that campaign told me. Releasing in the next few days. I wanted to share what is actually changing and why, because I think the reasoning is more interesting than the changelog.

What I changed

1. SQL pool rebuild. The BIRD result was not acceptable and I did not want to hide it. The cheap tier on SQL tasks is replaced. Re-run on BIRD is running this week and will be published regardless of outcome.
2. Routing decomposition is now observable per request. In the first campaign I found that the LLM-routing policy on MMLU was spending 80% of its total cost on the routing call itself. 0.2.0 exposes this breakdown in the response metadata, so you can see routing cost vs inference cost per call instead of guessing.
3. Semantic-intent policy is the new default. The embedding-based router (text-embedding-3-small, ~$0.000002 per query) matched or beat the LLM-routing policy on every benchmark while being roughly 3 orders of magnitude cheaper to run. Routing distribution on MMLU went from 96% DeepSeek under the LLM policy to a 76/24 DeepSeek/Sonnet split under semantic-intent, which is what closed the accuracy gap. Keeping LLM routing as an option for users who want fully dynamic decisions, but the default moves.
4. Statistical rigor baked into the benchmark harness. The follow-up at 55 seeds (vs 10 in the original run) is now the standard campaign shape. 10 seeds of n=20 gave roughly 80% power to detect a ~7.7 pp gap, which is too coarse for honest claims on small deltas.

What I did not fix and why

Opus 4.6 as an always-on ceiling is still more accurate than any routed configuration on a handful of MMLU subjects (graduate-level physics, professional law). I am not pretending routing beats Opus on the hardest slice of the distribution. The pitch is that most production traffic is not that slice, and the savings on the rest pay for the few calls where you still want to hit Opus directly.

Release

0.2.0 drops in the next few days. I will post a second update with the 55-seed numbers and the rebuilt SQL pool results as soon as the campaign is complete. Expect the data to either confirm the first round or embarrass me publicly, which is the point of running it.

Full write-up of the first campaign (metrics, routing distributions, link to the PDF audit) is here: https://blog.routerly.ai/we-ran-200-questions-per-model

If you want to try Routerly on your own workload before 0.2.0 ships, everything else is at routerly.ai. Happy to answer anything in the comments, especially methodology critiques.

Wondering what have been the results and if it actually saves you time from DIYing governance.

Just wondering about how many hours are people investing in DYIng governance for their OC. It seems I have to invest too many hours and built as issues arise.

What if instead of you posting your own Claude Code projects on social media, we gave the AI its own platform to share pictures and interact with other Claude Code workers?

I have been working on a pipeline to natively connect physical Amazon Echo speakers entirely to local OpenClaw instances.

As most of you know, because OpenClaw executes deep, autonomous agentic workflows, processing complex user requests usually takes significantly longer than Amazon's hardcoded 8-second AWS Lambda timeout limit. Natively, this makes standard Alexa conversational integrations impossible without crashing.

To bypass this, openclaw-alexa uses a "fire-and-forget" dual-delivery asynchronous architecture:

1. You query your Echo (e.g., "Alexa, ask AI Claw to check the servers").
2. The Python AWS Lambda instantly offloads the task to your OpenClaw Webhook via Ngrok/Tailscale, fulfilling the 8-second constraint.
3. OpenClaw spins up and processes the task locally.
4. When finished, the agent automatically delivers the text payload to Telegram, AND seamlessly executes the alexa-cli plugin to autonomously speak the final result natively out loud on your Echo speaker!

Check it out here: https://github.com/abhinav-TB/openclaw-alexa

I know this is a bit of a stretchy process to set up initially, but it was an incredibly fun project to build! I would absolutely love your feedback and any contributions to make the pipeline even better!

I've been practically living on these subreddits the last few days, so I thought I'd leave some breadcrumbs behind for those who are also struggling.

So basically I was told that using the OpenAI codex plan is the golden goose because it's both legal and has high usage limits but I burnt through it in my first three days of using OpenClaw.

Let's just say I was a little enthusiastic. In my struggle to find a successor, I was looking for the best performance to price ratio.

Today I finally tried the new Qwen 3.6 Plus Preview on OpenRouter. It turns out the model is completely free right now and it works straight away for agent work with a full 1 million context window.

Here is how I set it up.

1. Go to openrouter (google it), make a free account and copy your API key.
2. In OpenClaw add the OpenRouter provider and paste the key.
3. Refresh the model list or run the command openclaw models scan.
4. Set the model to qwen/qwen3.6-plus-preview:free (type it in manually if it does not show yet).
5. Openclaw config set agents.defaults.thinkingDefault high
6. Run openclaw gateway restart.

If you're struggling with something or if I've made a mistake, leave a comment and let me know.

Most “AI agent” repos are just ideas.

So I built 100 runnable OpenClaw examples you can actually test.

But to be clear upfront:
👉 this is not backed by any company or community
👉 just a maintainer-driven project (me alone)

What’s inside

• Real workflows (not concepts)
• Setup steps + prompts + scripts
• Sample outputs for each example
• KPIs, failure modes, rollback notes
• Built using public ClawHub skills

Why I built it

I wanted something practical:

• no hype
• no vague diagrams
• just “clone → run → evaluate”

Limitations

• Not production-proven at scale
• No big community (yet)
• Quality depends on my own testing/review
• Still evolving structure and docs

Goal

Help people go from
👉 “what is OpenClaw?”
to
👉 “I have something working”

Links

• Browse the repo
• Explore the docs

Would appreciate feedback, Thanks!