HiClaw: An Industrial Solution for Multi-Agent Collaboration, Say Goodbye to Manual Supervision

HiClaw: When AI Agents Get Their Own "Office", I Finally Stop Being the Foreman

As a Java veteran tortured by the Spring ecosystem for years, I used to sigh at every new Agent project—another thing requiring me to write code to serve AI? But HiClaw is different. It doesn't make AI write code for you; it builds an "office" for AI agents to collaborate, where you only need to drop in occasionally for a coffee and some guidance.

What Problem Does This Project Actually Solve?

Simply put, HiClaw is a Multi-Agent Collaboration Operating System. Imagine having 5 AI assistants: one handles frontend, one backend, one writes tests, one does code review, and another handles deployment. With existing solutions, you'd need to chat with each separately, transfer files, and coordinate progress—as exhausting as manual labor.

HiClaw's approach is brilliant: create a Matrix chatroom, bring all Agents and humans in, let the Manager coordinate centrally while Workers do their tasks, with all conversations transparently visible. It's like merging scattered WeChat work groups into a single enterprise WeChat with permission management, complete with file sharing and audit logs.

Technical Architecture: Manager-Workers Pattern

The core architecture follows the classic Manager-Workers pattern, implemented with Go + Kubernetes for industrial deployment:

┌───────────────────────────────────────────────┐
│            hiclaw-controller                  │
│  Higress │ Tuwunel │ MinIO │ Element Web      │
└──────────────────┬────────────────────────────┘
                   │ Matrix + HTTP Files
┌──────────────────┴──────────┐
│     hiclaw-manager-agent     │
└──────────────────┬──────────┘
                   │
┌──────────────────┼────────────────────────────┐
│                  │                            │
▼                  ▼                            ▼
Worker Alice    Worker Bob              Worker Charlie
(OpenClaw)      (QwenPaw)               (Hermes)

Key components:

• hiclaw-controller: K8s control plane managing Worker/Team/Human resources via CRDs
• Higress AI Gateway: Centralized LLM API credential management; Workers never get real API Keys
• Tuwunel (Matrix): Self-hosted IM server; all Agents and humans communicate via Matrix
• MinIO: Shared filesystem preventing token passing between Agents

What impresses me most is the security model. Worker Agents only hold consumer-grade tokens; real API Keys and GitHub PATs stay at the gateway layer. Even if a Worker is compromised, attackers can't access your cloud provider credentials—a rare find in today's Agent security wild west.

Installation: One Command Done

Installation is friendlier than many K8s projects. One command for macOS/Linux:

bash <(curl -sSL https://higress.ai/hiclaw/install.sh)

Windows users get a PowerShell version:

Set-ExecutionPolicy Bypass -Scope Process -Force; $wc=New-Object Net.WebClient; $wc.Encoding=[Text.Encoding]::UTF8; iex $wc.DownloadString('https://higress.ai/hiclaw/install.ps1')

The process guides you through LLM provider selection, API Key input, and network mode configuration. It runs on 2-core 4GB RAM; for multiple Workers, 4-core 8GB is recommended—much lighter than running Jenkins.

Kubernetes Deployment: Production-Grade Setup

Deploy in production using Helm with flexible configuration:

helm repo add higress.io https://higress.io/helm-charts
helm repo update

helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace \
  --render-subchart-notes \
  --set credentials.llmApiKey=<your-api-key> \
  --set credentials.adminPassword=<your-admin-password> \
  --set gateway.publicURL=http://localhost:18080

For non-OpenAI providers (e.g., DeepSeek, Qwen), configure compatible APIs:

helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace \
  --set credentials.llmApiKey=<your-api-key> \
  --set credentials.llmBaseUrl=https://your-provider.example.com/v1 \
  --set credentials.defaultModel=your-model-name \
  --set credentials.adminPassword=<your-admin-password> \
  --set gateway.publicURL=http://localhost:18080

Configuration items like llmProvider, defaultModel, and manager.runtime can be overridden via Helm values. Multi-region image registries are supported—Alibaba Cloud Hangzhou for China, US West or Southeast Asia for overseas. Thoughtful design.

Multi-Runtime Collaboration: The Real Highlight

HiClaw supports three Worker runtimes collaborating in the same Matrix room:

Smart design—letting Agents excel at their strengths. Use OpenClaw for task decomposition, Hermes for code implementation, QwenPaw for frontend automation testing. Switch runtime with one command:

hiclaw update worker --runtime hermes

User Experience: Human-in-the-Loop is Core

Workflow example:

You: Create a frontend Worker named alice

Manager: Done. Worker alice ready.
         Room: Worker: Alice
         Tell alice what to do.

You: @alice Implement a login page with React

Alice: Processing... [a few minutes later]
       Done. PR submitted: https://github.com/xxx/pull/1

The key is all conversations are transparently visible in Matrix rooms. No hidden Agent-to-Agent calls; you can interject and modify requirements anytime. Critical for enterprise scenarios—audit compliance is no joke.

Pitfall Warnings

1. Resource Consumption: Memory usage grows significantly with multiple Workers. 8GB is recommended; production suggests 16GB minimum
2. Matrix Learning Curve: Matrix protocol has a learning curve for newcomers; Element Web interface needs adaptation
3. China Network: Despite multi-region registries, some LLM APIs may need proxies

Debug log export is practical:

## Export debug logs (PII auto-redaction)
python scripts/export-debug-log.py --range 1h

Then analyze logs with Cursor or Claude Code—much more efficient than traditional issue reporting.

Comparison with Native OpenClaw

HiClaw doesn't replace OpenClaw; it adds an industrial shell.

Personal Opinion: Worth Learning?

As an 8-year Java developer, my take: Architecture design outweighs code implementation.

1. Security Model Worth Borrowing: Consumer tokens + gateway-hosted real credentials applicable to any API Key management scenario
2. Matrix Protocol is a Treasure: Decentralized, federated, open-source—far more flexible than integrating enterprise WeChat/DingTalk
3. K8s-Native Design: Managing Agent resources via CRDs with declarative config—the right cloud-native approach

If I were to use it, I'd deploy a small team setup first, letting frontend/backend Agents handle repetitive tasks. Consider CI/CD integration after stabilization.

Only concern: the project is new (open-sourced March 2026), community ecosystem immature. But 4,299 stars and today's trending status show rising attention—worth watching.

Summary: HiClaw isn't another AI toy; it seriously addresses enterprise Agent collaboration pain points. Secure, transparent, scalable—all reflected in the README. If evaluating Agent collaboration platforms, this should be on your shortlist.