Stop Managing AI Agents Manually! HiClaw Runs Your Entire Agent Team in One Chat Room

What if your AI agents could hire, manage, and fire each other—while you simply watched from the sidelines?

Here's the brutal truth every developer building with AI agents eventually faces: orchestration hell. You've got one agent for code generation, another for browser automation, a third for API testing. Each needs separate credentials. Each runs in its own black box. Each demands your attention when something breaks. You're not building an AI team—you're running an AI daycare, constantly juggling API keys, debugging opaque failures, and praying your GitHub PAT doesn't leak in a log file.

The enterprise world is even worse. Security teams panic about credential sprawl. Compliance officers demand audit trails that don't exist. Your "multi-agent system" is actually five separate Docker containers you glued together with shell scripts and desperation.

Enter HiClaw—the open-source collaborative multi-agent runtime that's about to make every other agent orchestration approach look like a toy. Built by the higress-group and unleashed on GitHub, HiClaw doesn't just run agents. It creates an operating system for agent teams, complete with instant messaging coordination, human-in-the-loop control, and military-grade credential isolation. One command. One chat room. Total visibility.

Ready to see how the future of agent collaboration actually works?

What is HiClaw? The Multi-Agent OS You Didn't Know You Needed

HiClaw is an open-source Collaborative Multi-Agent OS designed for transparent, human-in-the-loop task coordination via Matrix rooms. Born from the Higress ecosystem (the same team behind the popular Higress AI Gateway), HiClaw takes a fundamentally different approach to agent orchestration: instead of implementing agent logic itself, it orchestrates and manages multiple Agent containers through a centralized Manager-Workers architecture.

The project emerged from a simple observation: existing agent frameworks like OpenClaw are brilliant at individual agent reasoning, but terrible at team dynamics. They leave you to manually configure each agent, distribute credentials like risky secrets, and cobble together monitoring through log files. HiClaw solves this by treating agent collaboration as a distributed systems problem with communication, security, and observability as first-class citizens.

Since its open-source announcement on March 4, 2026, HiClaw has evolved rapidly through multiple releases:

• v1.0.4: QwenPaw Worker support with 80% memory reduction
• v1.0.6: Enterprise-grade MCP Server management with zero credential exposure
• v1.0.9: Kubernetes-style declarative resource management, Worker Template Marketplace, Nacos Skills Registry
• v1.1.0: Kubernetes-native control plane, Hermes autonomous coding agent runtime, 1.7 GB image shrink, and the game-changing hiclaw CLI

The latest architecture supports three distinct agent runtimes coexisting in the same IM room: OpenClaw (Node.js, general-purpose), QwenPaw (Python, lightweight browser automation), and Hermes (autonomous coding with terminal sandbox). This isn't just flexibility—it's runtime polyglotism for AI agents.

Key Features: Why HiClaw Is Architecturally Superior

HiClaw's feature set reads like a wishlist from every developer who's ever screamed at a broken agent pipeline. Let's dissect what makes this system genuinely innovative:

🧬 Manager-Workers Architecture: Agents Managing Agents

The core innovation is recursive delegation. A Manager Agent (running OpenClaw or QwenPaw) centrally orchestrates multiple Worker Agents, eliminating the need for humans to micromanage individual "Claws." The Manager creates Workers through natural language commands, assigns tasks, monitors progress, and terminates completed agents. You become the CEO; the Manager becomes your CTO.

🤝 Multi-Runtime Collaboration: The Right Agent for the Right Job

HiClaw uniquely allows heterogeneous agents to share the same communication space. Deterministic agents (OpenClaw/QwenPaw) serve as task Leaders for orchestration and planning, while Hermes Workers handle autonomous code execution with self-improving skills. Each runtime specializes; Matrix rooms synchronize them. This isn't theoretical—it's production-ready polyglot agent collaboration.

📦 MinIO Shared File System: Slash Your Token Bills

Multi-agent collaboration traditionally wastes tokens through repetitive context passing. HiClaw introduces a shared file system for inter-Agent information exchange, dramatically reducing token consumption. Workers read shared artifacts instead of re-describing context in every message. Your API costs just got rationalized.

🔐 Higress AI Gateway: Credential Fort Knox

Real credentials (API keys, GitHub PATs) never touch Worker containers. The Higress AI Gateway centralizes traffic management and credential storage, issuing consumer tokens to Workers that are useless if compromised. This mitigates the security vulnerabilities inherent in frameworks like Lobster where agents directly hold sensitive keys.

☎️ Zero-Configuration IM: Matrix Without the Pain

Built-in Tuwunel Matrix server and Element Web client eliminate integration overhead. No DingTalk approvals. No Lark bot applications. No enterprise workflow bottlenecks. Users experience model services through familiar chat interfaces, with full mobile access via any Matrix client (Element, FluffyChat).

Use Cases: Where HiClaw Absolutely Dominates

1. Enterprise AI Teams with Compliance Requirements

Financial services and healthcare organizations need auditable AI operations. HiClaw's Matrix-based communication creates permanent, searchable records of all agent interactions. Every decision, every tool call, every credential access is logged in a room that compliance officers can review. Human-in-the-loop is default, not bolted-on.

2. Autonomous Software Development Squads

Imagine a Hermes Worker that writes code, a QwenPaw Worker that tests it in a browser, and an OpenClaw Manager that coordinates sprints—all in one Matrix room. The Manager assigns features, Hermes implements with terminal access, QwenPaw validates UI flows, and you intervene only when the conversation warrants it. True CI/CD for agent teams.

3. Multi-Model Strategy Without Complexity

Organizations hedging across OpenAI, Anthropic, Qwen, and local models traditionally manage separate agent infrastructures. HiClaw's gateway abstraction lets different Workers use different providers transparently. The Manager optimizes cost and capability by routing tasks to the most efficient model.

4. Secure Outsourced Agent Operations

Third-party agents are security nightmares. HiClaw's consumer token system means external Workers operate with zero access to real credentials. Even if an agent container is compromised, attackers gain nothing of value. The gateway maintains complete control over actual API access.

Step-by-Step Installation & Setup Guide

HiClaw's installation philosophy is aggressively simple: one command, running system. Here's how to deploy across environments.

Local Development (Docker)

Prerequisites: Docker Desktop (Windows/macOS) or Docker Engine (Linux). Minimum 2 CPU cores + 4 GB RAM; 4 cores + 8 GB recommended for multiple Workers.

macOS / Linux:

# One-liner installation—AI gateway, Matrix server, file storage, web client, and Manager Agent
bash <(curl -sSL https://higress.ai/hiclaw/install.sh)

Windows (PowerShell 7+):

# Bypass execution policy for this process only, download and execute installer
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 interactive installer configures:

1. LLM Provider: Choose from OpenAI-compatible APIs (OpenAI, DeepSeek, local models)
2. API Key: Enter your credentials (stored securely in gateway, never in Workers)
3. Network Mode: Local-only for development, or external access for team sharing
4. Automated Setup: Downloads images, starts containers, verifies health

Access your deployment:

# Open Element Web in your browser
open http://127.0.0.1:18088  # macOS
# Or navigate manually on Windows/Linux

The Manager Agent greets you immediately and guides Worker creation. Mobile access: Connect any Matrix client to your server address—zero additional configuration.

Production Kubernetes (Helm)

For shared deployments, HiClaw provides a comprehensive Helm chart with zero external dependencies.

Prerequisites:

• Kubernetes 1.24+ (kind, minikube, k3s, or managed clusters)
• Helm 3.7+
• Default StorageClass for PVCs

Standard OpenAI Deployment:

# Add the official HiClaw Helm repository
helm repo add higress.io https://higress.io/helm-charts
helm repo update

# Install with minimal required configuration
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

Custom Provider (OpenAI-Compatible):

# For DeepSeek, local vLLM, or other compatible endpoints
helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace \
  --render-subchart-notes \
  --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

Qwen (通义千问) Deployment:

# Native Qwen support with optimized defaults
helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace \
  --render-subchart-notes \
  --set credentials.llmApiKey=<your-qwen-api-key> \
  --set credentials.llmProvider=qwen \
  --set credentials.defaultModel=qwen3.5-plus \
  --set credentials.adminPassword=<your-admin-password> \
  --set gateway.publicURL=http://localhost:18080

Multi-Region Registry Override (for faster pulls outside China):

# North America deployment example
helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace \
  --render-subchart-notes \
  --set global.imageRegistry=higress-registry.us-west-1.cr.aliyuncs.com/higress \
  --set credentials.llmApiKey=<your-api-key> \
  --set credentials.adminPassword=<your-admin-password> \
  --set gateway.publicURL=http://localhost:18080

Access and Upgrade:

# Port-forward for local access
kubectl port-forward -n hiclaw-system svc/higress-gateway 18080:80

# Upgrade preserving all data
helm repo update
helm upgrade hiclaw higress.io/hiclaw -n hiclaw-system --reuse-values

# Complete removal
helm uninstall hiclaw -n hiclaw-system
kubectl delete namespace hiclaw-system

REAL Code Examples: HiClaw in Action

Let's examine actual patterns from the HiClaw repository, with detailed explanations of how each component functions.

Example 1: Runtime-Switching a Live Worker

HiClaw's CLI enables hot-swapping agent runtimes without recreation. This is transformative for optimizing task execution:

# Dynamically change a Worker's runtime to Hermes for autonomous coding tasks
# The Worker retains its identity, room membership, and task context
hiclaw update worker --runtime hermes

Why this matters: Traditional agent systems require destroying and recreating agents to change capabilities. HiClaw's runtime abstraction means a Worker named "alice" can shift from deterministic OpenClaw mode (for structured tasks) to autonomous Hermes mode (for exploratory coding) mid-conversation. The Matrix room history persists; only the execution engine changes. This enables adaptive task routing where the Manager optimizes which runtime handles incoming work based on complexity and requirements.

Example 2: Alternative Runtime Helm Deployment

Deploy with heterogeneous Manager and Worker configurations for specialized workloads:

# Manager uses QwenPaw for lightweight orchestration
# Workers default to Hermes for maximum coding autonomy
helm install hiclaw higress.io/hiclaw \
  -n hiclaw-system --create-namespace --devel \
  --set manager.runtime=copaw \
  --set worker.defaultRuntime=hermes \
  --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

Architecture insight: The --devel flag enables bleeding-edge runtime combinations. Image selection is automatic based on runtime values: hiclaw-manager vs hiclaw-manager-copaw for Manager, and hiclaw-worker / hiclaw-copaw-worker / hiclaw-hermes-worker for Workers. This declarative approach means you specify intent ("I want QwenPaw orchestration with Hermes execution"), and HiClaw resolves the correct container images, environment variables, and capability grants.

Example 3: Debug Log Export for AI-Assisted Troubleshooting

HiClaw's debugging workflow is meta-AI optimized—it expects you'll use AI tools to diagnose itself:

# Export comprehensive debug logs with automatic PII redaction
# Range: 1h, 24h, 7d, or custom
python scripts/export-debug-log.py --range 1h

# Produces debug-log/ directory with:
# - Matrix message logs (all room communications)
# - Agent session logs (internal reasoning traces)
# - Cross-referenced timeline for event correlation

Revolutionary workflow: After export, load the codebase into Cursor, Claude Code, or similar AI coding tools and prompt:

Read the JSONL files in debug-log/. Analyze the Matrix message logs 
and agent session logs together. Cross-reference with the HiClaw 
codebase to identify the root cause of [describe your bug].

This self-healing system design means HiClaw improves through its own operational data. The AI assistant can then submit fixes directly via GitHub CLI with the OpenClaw GitHub skill:

# Prerequisites for AI-submitted fixes
gh auth login  # Browser authentication
# Add OpenClaw GitHub skill to your AI tool
# Then simply ask: "File an issue with your analysis" or "Open a PR with the fix"

Example 4: Makefile Development Commands

HiClaw's build system supports rapid iteration and testing:

# Build all container images from source
make build

# Full integration test suite—validates Manager-Worker coordination,
# Matrix connectivity, gateway routing, and MinIO file sharing
make test

# Quick smoke test for CI/CD pipelines
make test-quick

# Replay a specific task for debugging or demonstration
make replay TASK="Create a Worker named alice for frontend development"

# Clean removal of all local resources
make uninstall

# Discover all available commands
make help

Development velocity: The make replay command is particularly powerful for regression testing—capture a failing task description, fix the underlying code, then replay to verify resolution without manual interaction.

Advanced Usage & Best Practices

Runtime Selection Strategy

OpenClaw as Manager + Hermes Workers: Optimal for software development teams. The deterministic Manager decomposes features into tasks; Hermes Workers autonomously implement with terminal access and self-improving skills.

QwenPaw Manager + OpenClaw Workers: Best for cost-sensitive operations. Lightweight Python orchestration minimizes overhead; Node.js Workers handle complex tool chains.

Homogeneous Hermes: Maximum autonomy for research environments where agents should self-organize. Monitor closely—this is the "give them goals, not tasks" configuration.

Kubernetes-Native Declarative Management

HiClaw v1.1.0+ supports Kubernetes CRDs for Worker, Team, and Human resources. Define your agent organization in YAML:

# Example pattern (see docs/k8s-native-agent-orch.md for full schema)
apiVersion: hiclaw.io/v1
kind: Team
metadata:
  name: frontend-squad
spec:
  manager:
    runtime: openclaw
  workers:
    - name: react-specialist
      runtime: hermes
      skills: ["react", "typescript", "testing"]
    - name: ui-validator
      runtime: qwenpaw
      skills: ["browser-automation", "accessibility"]

Apply with kubectl apply -f team.yaml—the HiClaw controller reconciles actual state to desired state automatically.

Security Hardening

• Network Policies: Restrict Worker egress to gateway-only in production Kubernetes
• Credential Rotation: The gateway supports hot-reloading API keys without Worker restart
• Audit Logging: Enable Tuwunel's message retention policies for compliance archiving

Comparison with Alternatives

The verdict: OpenClaw Native excels for single-agent reasoning. CrewAI simplifies multi-agent Python patterns. But HiClaw is the only system architected for production-grade agent team operations with security, observability, and scalability as foundational principles.

FAQ: Your Burning Questions Answered

Is HiClaw free for commercial use?

Yes. HiClaw is Apache 2.0 licensed. Deploy internally, modify, redistribute—no restrictions. The Higress ecosystem is backed by Alibaba's open-source investment.

Can I use my existing OpenAI API key?

Absolutely. HiClaw supports any OpenAI-compatible API, including official OpenAI, Azure OpenAI, DeepSeek, local vLLM endpoints, and more. Set llmBaseUrl for non-standard providers.

How does HiClaw compare to just running multiple AutoGPT instances?

AutoGPT instances are isolated black boxes with no native coordination, shared state, or security boundaries. HiClaw provides structured collaboration, credential isolation, and human oversight that multi-AutoGPT setups cannot replicate without massive custom engineering.

What's the minimum team size to benefit from HiClaw?

One human + one Manager + one Worker is sufficient. Many developers use HiClaw solo, letting the Manager handle task decomposition while they focus on high-level direction. The system scales to dozens of Workers as teams grow.

Can Workers access the internet?

Hermes Workers have controlled terminal sandbox access for code execution, but all LLM and API traffic routes through the Higress gateway. Network policies can restrict Worker egress further in Kubernetes deployments.

How do I migrate from OpenClaw Native to HiClaw?

HiClaw orchestrates OpenClaw Workers natively. Your existing skills and prompts transfer directly. The Manager simply creates OpenClaw Workers instead of running standalone. Migration is typically conversational: "Create a Worker with my existing OpenClaw config."

Is Matrix required, or can I use Slack/Teams?

Matrix is foundational for HiClaw's security and audit model. However, Tuwunel supports federation—you can bridge to other protocols if needed. Native Slack/Teams integration would sacrifice the zero-configuration, self-hosted security guarantees.

Conclusion: The Agent Team OS Is Here

HiClaw represents a paradigm shift in how we think about AI agent systems. It doesn't ask you to choose between capability and control, between autonomy and observability, between speed and security. It delivers all of them through architectural decisions that treat agent collaboration as a distributed systems challenge worthy of rigorous engineering.

The one-command setup removes adoption friction. The Kubernetes-native control plane enables enterprise scale. The multi-runtime collaboration lets you compose specialized agents like functions in a program. And the human-in-the-loop design ensures you remain the ultimate decision-maker, not a bystander to opaque automation.

If you're still manually configuring individual agents, passing API keys like hot potatoes, and debugging through scattered log files, you're working too hard. The future of agent orchestration is conversational, observable, and secure—and it's available right now at github.com/higress-group/hiclaw.

Deploy HiClaw today. Let your agents manage themselves.