OpenClaw: The Open-Source AI Agent Revolutionizing Task Automation (2026 Complete Guide)

┌─────────────────────────────────────────────────────────────────────┐
│                     OPENCLAW OVERVIEW                                   │
├─────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐         │
│  │   Telegram   │    │   Discord    │    │  WhatsApp    │         │
│  └──────┬───────┘    └──────┬───────┘    └──────┬───────┘         │
│         │                    │                    │                  │
│         └────────────────────┼────────────────────┘                  │
│                              │                                        │
│                      ┌───────┴───────┐                                │
│                      │   Gateway     │                                │
│                      │  (Message      │                                │
│                      │   Routing)     │                                │
│                      └───────┬───────┘                                │
│                              │                                        │
│                      ┌───────┴───────┐                                │
│                      │    Agent       │                                │
│                      │  (LLM Brain)   │                                │
│                      └───────┬───────┘                                │
│                              │                                        │
│         ┌────────────────────┼────────────────────┐                  │
│         │                    │                    │                  │
│  ┌──────┴───────┐    ┌──────┴───────┐    ┌──────┴───────┐         │
│  │    Skills    │    │    Memory    │    │   Executor   │         │
│  │  (Tools)     │    │  (Context)   │    │ (Actions)    │         │
│  └──────────────┘    └──────────────┘    └──────────────┘         │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘

# Gateway handles multi-channel message routing
class Gateway:
    def __init__(self):
        self.channels = {
            'telegram': TelegramChannel(),
            'discord': DiscordChannel(),
            'slack': SlackChannel(),
            'webhook': WebhookChannel()
        }
    
    async def route_message(self, message: Message) -> Response:
        # Transform message to standard format
        standardized = self.channels[message.source].receive(message)
        
        # Pass to agent for processing
        result = await self.agent.process(standardized)
        
        # Route response back to source
        return await self.channels[message.source].send(result)

# Feishu/Lark Integration
# 1. Create app at https://open.feishu.cn/
# 2. Get App ID and Secret
# 3. Enable webhook and IM features
OPENCLAW_PLATFORM=feishu
OPENCLAW_FEISHU_APP_ID=cli_xxx
OPENCLAW_FEISHU_APP_SECRET=xxx
OPENCLAW_FEISHU_VERIFICATION_TOKEN=xxx
OPENCLAW_FEISHU_ENCRYPT_KEY=xxx

# Telegram Integration
# 1. @BotFather to create bot
# 2. Get bot token
# 3. Configure webhook
OPENCLAW_PLATFORM=telegram
OPENCLAW_TELEGRAM_BOT_TOKEN=xxx
OPENCLAW_TELEGRAM_ADMIN_IDS=123456789

# Discord Integration
# 1. Create application at https://discord.com/developers/
# 2. Add bot to server
# 3. Get token
OPENCLAW_PLATFORM=discord
OPENCLAW_DISCORD_BOT_TOKEN=xxx
OPENCLAW_DISCORD_GUILD_ID=xxx
OPENCLAW_DISCORD_CHANNEL_IDS=xxx

# WhatsApp Integration (Business API)
OPENCLAW_PLATFORM=whatsapp
OPENCLAW_WHATSAPP_PHONE_ID=xxx
OPENCLAW_WHATSAPP_TOKEN=xxx

# DingTalk Integration
OPENCLAW_PLATFORM=dingtalk
OPENCLAW_DINGTALK_APP_KEY=xxx
OPENCLAW_DINGTALK_APP_SECRET=xxx

class Agent:
    def __init__(self, model_provider: str = "openai"):
        self.model = self._init_model(model_provider)
        self.memory = Memory()
        self.skills = SkillsRegistry()
    
    async def process(self, request: Request) -> Response:
        # Load context from memory
        context = await self.memory.load_context(request.user_id)
        
        # Analyze request and plan actions
        plan = await self.model.plan(request, context)
        
        # Execute each step in the plan
        results = []
        for step in plan.steps:
            skill = self.skills.get(step.skill_name)
            result = await skill.execute(step.params)
            results.append(result)
            
            # Update memory with progress
            await self.memory.save_step(request.user_id, step, result)
        
        return Response(results=results, summary=await self.model.summarize(results))

# Example skill definition
skills:
  - name: "browser_control"
    description: "Control browser for web automation"
    actions:
      - navigate
      - click
      - type
      - screenshot
      - extract_data
      
  - name: "file_operations"
    description: "Read, write, and manage files"
    actions:
      - read
      - write
      - append
      - delete
      - list
      - search
      
  - name: "shell_execution"
    description: "Execute shell commands"
    actions:
      - run
      - run_background
      - kill
      
  - name: "email_manager"
    description: "Manage email operations"
    actions:
      - send
      - read
      - categorize
      - archive

class Memory:
    def __init__(self, storage_path: str = "./memory"):
        self.vector_store = ChromaDB(storage_path)
        self.key_value_store = SQLite(storage_path)
    
    async def save_context(self, user_id: str, data: dict):
        # Save structured data
        self.key_value_store.set(f"user:{user_id}", data)
        
        # Embed and store for semantic search
        embedding = await self.embed(data)
        await self.vector_store.upsert(user_id, embedding)
    
    async def recall(self, user_id: str, query: str) -> list:
        # Semantic search through memory
        results = await self.vector_store.search(query, top_k=5)
        return results

module.exports = {
    name: 'my-custom-skill',
    version: '1.0.0',
    description: 'Custom skill for specific tasks',
    triggers: [
        'my skill',
        'custom action'
    ],
    async execute(context) {
        const { message, args, user } = context;
        const result = await performCustomAction(args);
        return {
            success: true,
            response: `Action completed: ${result}`
        };
    },
    permissions: ['browser', 'file:read', 'shell']
};

// config/skills.js
module.exports = {
    skills: [
        require('./skills/web-search'),
        require('./skills/file-manager'),
        require('./skills/reminder'),
        require('./skills/code-runner'),
        require('./skills/my-custom-skill')
    ]
};

# System requirements
- Node.js 20+
- Python 3.10+
- uv package manager
- For browser control: Chrome/Edge/Firefox

# 1. Clone the repository
git clone https://github.com/openshell-ai/openclaw.git
cd openclaw

# 2. Install dependencies
uv sync

# 3. Configure environment
cp .env.example .env

# 4. Edit .env with your settings

# .env configuration
# LLM Provider
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...

# Or OpenAI
# LLM_PROVIDER=openai
# OPENAI_API_KEY=sk-...

# Storage (local)
MEMORY_TYPE=local
STORAGE_PATH=./data

# Channels (enable what you need)
ENABLE_TELEGRAM=true
TELEGRAM_BOT_TOKEN=...

ENABLE_DISCORD=true
DISCORD_BOT_TOKEN=...

# Browser automation
BROWSER_TYPE=chromium
HEADLESS=false

# Security
API_SECRET=your-secret-key
ALLOWED_USERS=user1,user2

# Start the agent
uv run openclaw

# Or with specific config
uv run openclaw --config production.yaml

# Check status
curl http://localhost:8080/health

# docker-compose.yaml
version: '3.8'

services:
  openclaw:
    image: openclaw/openclaw:latest
    container_name: openclaw
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
      - OPENCLAW_PLATFORM=telegram
      - OPENCLAW_LLM_PROVIDER=openai
      - OPENCLAW_OPENAI_API_KEY=${OPENAI_API_KEY}
    volumes:
      - ./data:/app/data
      - ./logs:/app/logs
      - /var/run/docker.sock:/var/run/docker.sock
    restart: unless-stopped
    network_mode: host

# Start the container
docker-compose up -d

# Check logs
docker logs -f openclaw

# Stop
docker-compose down

# Example: Automate web form filling
async def fill_form(context: Context):
    await browser.navigate("https://example.com/form")
    
    # Wait for page load
    await browser.wait_for_selector("#name")
    
    # Fill fields
    await browser.type("#name", context.user_name)
    await browser.type("#email", context.user_email)
    await browser.type("#message", context.message)
    
    # Submit
    await browser.click("#submit")
    
    # Extract result
    result = await browser.extract(".success-message")
    return result

# Example: Generate weekly report
async def generate_weekly_report(context: Context):
    # Read data files
    sales = await files.read_json("data/sales.json")
    users = await files.read_csv("data/users.csv")
    
    # Process data
    total = sum(s['amount'] for s in sales)
    new_users = len(users[users['created'] > context.last_week])
    
    # Generate markdown report
    report = f"""
# Weekly Report - {context.week}

## Summary
- Total Sales: ${total:,.2f}
- New Users: {new_users}

## Details
{build_table(sales)}
"""
    
    # Write report
    await files.write(f"reports/week-{context.week}.md", report)
    
    # Also export to PDF
    await files.convert_markdown_to_pdf(report, "reports/week-{context.week}.pdf")
    
    return "Report generated successfully"

# Example: Deploy application
async def deploy_app(context: Context):
    # Run build
    result = await shell.run("npm run build")
    if result.exit_code != 0:
        raise Error(f"Build failed: {result.stderr}")
    
    # Run tests
    result = await shell.run("npm test")
    if result.exit_code != 0:
        raise Error(f"Tests failed: {result.stderr}")
    
    # Deploy to production
    result = await shell.run("./deploy.sh production")
    
    return f"Deployed successfully: {result.stdout}"

# scheduled_tasks.yaml
schedules:
  - name: "Morning briefing"
    cron: "0 8 * * 1-5"
    action: "send_daily_summary"
    params:
      channels: ["telegram", "email"]
      
  - name: "Weekly report"
    cron: "0 9 * * 1"
    action: "generate_weekly_report"
    
  - name: "Price monitoring"
    cron: "*/30 * * * *"
    action: "check_prices"
    params:
      targets: ["amazon/product1", "ebay/product2"]
      
  - name: "Data backup"
    cron: "0 2 * * *"
    action: "backup_data"

# Content publishing workflow
async def publish_content(topic: str, platforms: list):
    # 1. Research
    research = await browser.search(topic)
    await memory.save(f"research:{topic}", research)
    
    # 2. Generate content
    article = await llm.generate_article(topic, research)
    
    # 3. Format for each platform
    for platform in platforms:
        formatted = format_for_platform(article, platform)
        
        if platform == "twitter":
            await twitter.post(formatted)
        elif platform == "linkedin":
            await linkedin.post(formatted)
        elif platform == "blog":
            await blog.publish(formatted)
    
    return f"Published to {len(platforms)} platforms"

# Auto-respond to support tickets
async def handle_support_ticket(ticket_id: str):
    # 1. Fetch ticket details
    ticket = await api.get_ticket(ticket_id)
    
    # 2. Analyze and categorize
    category = await llm.classify(ticket.message)
    
    # 3. Check knowledge base
    solutions = await search_knowledge_base(category, ticket.message)
    
    if solutions:
        # 4. Send response
        response = await llm.generate_response(ticket, solutions)
        await api.reply_ticket(ticket_id, response)
    else:
        # Escalate to human
        await api.escalate(ticket_id)
        await notify("#support", f"New escalated ticket: {ticket_id}")
    
    return "Ticket processed"

# ETL pipeline
async def run_etl_pipeline():
    # Extract
    raw_data = []
    for source in ["api", "database", "files"]:
        data = await extract_from(source)
        raw_data.extend(data)
    
    # Transform
    cleaned = []
    for record in raw_data:
        transformed = transform_record(record)
        if validate_record(transformed):
            cleaned.append(transformed)
    
    # Load
    await load_to_database(cleaned)
    await load_to_analytics(cleaned)
    
    # Notify
    await send_notification(f"Pipeline complete: {len(cleaned)} records")
    
    return f"Processed {len(cleaned)} records"

const OpenClawAPI = require('openclaw-api');

const client = new OpenClawAPI({
    baseUrl: 'http://localhost:3000',
    apiKey: 'your-api-key'
});

// Send message
await client.sendMessage({
    platform: 'telegram',
    chatId: '123456789',
    message: 'Summarize the latest tech news'
});

// Execute automation workflow
await client.executeWorkflow({
    name: 'research',
    params: {
        topic: 'OpenClaw AI assistant',
        depth: 'detailed',
        outputFormat: 'markdown'
    }
});

# llm.yaml
providers:
  anthropic:
    enabled: true
    model: "claude-sonnet-4-20250514"
    temperature: 0.7
    max_tokens: 4096
    
  openai:
    enabled: false
    model: "gpt-4o"
    temperature: 0.7
    
  local:
    enabled: false
    provider: "ollama"
    model: "qwen2.5:14b"
    base_url: "http://localhost:11434"

### Local LLM Integration

For privacy and zero API costs, OpenClaw supports local models through Ollama:

```bash
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model
ollama pull qwen:7b
ollama pull llama3:8b

# Configure OpenClaw
OPENCLAW_LLM_PROVIDER=ollama
OPENCLAW_OLLAMA_BASE_URL=http://localhost:11434
OPENCLAW_OLLAMA_MODEL=qwen:7b
```text

| Model | Parameters | Best For |
|-------|-----------|----------|
| Llama 3 | 8B | General purpose |
| Qwen 2.5 | 7B-72B | Coding, reasoning |
| Mistral | 7B | Fast, efficient |
| CodeLlama | 7B-34B | Code generation |

### Skill Configuration

```yaml
# skills.yaml
skills:
  browser:
    enabled: true
    default_timeout: 30000
    headless: false
    
  file_operations:
    enabled: true
    allowed_paths:
      - "./workspace"
      - "./data"
    max_file_size: 100MB
    
  shell:
    enabled: true
    allowed_commands:
      - "git *"
      - "npm *"
      - "python *"
    blocked_commands:
      - "rm -rf /"
      - "dd *"
      
  http:
    enabled: true
    timeout: 30000
    allowed_domains:
      - "*.example.com"
      - "api.github.com"
```text

### Security Configuration

```yaml
# security.yaml
authentication:
  type: "api_key"
  keys:
    - "sk-openclaw-user1-xxxx"
    - "sk-openclaw-user2-xxxx"
    
authorization:
  user_permissions:
    user1:
      skills: ["*"]
      channels: ["telegram", "discord"]
    user2:
      skills: ["browser", "file_operations"]
      channels: ["telegram"]
      
rate_limiting:
  enabled: true
  requests_per_minute: 10
  burst: 20
```text

---

## Security Considerations

OpenClaw's rapid growth has brought both capability and scrutiny. Understanding its security posture is essential before production deployment:

**Strengths**: OpenClaw shipped a major security hardening block in March-April 2026 covering environment variable sanitization, execution sandboxing, archive extraction hardening, cross-component trust, and WebSocket session invalidation. Plugin installation now defaults to fail-closed, and all plugins must declare their required permissions in manifests.

**Known gaps**: As of April 2026, the repository had approximately 469 open security issues. The project's SECURITY.md explicitly scopes prompt injection out of security fixes — it is acknowledged as a known architectural trade-off, not something being patched. The model has broad access to tools and services by design (operator-trust-based security model). Community skills are not vetted before installation, and malicious skills have been observed exfiltrating credentials and accessing sensitive files.

**Recommendations**: For personal use, the risk is manageable. For business or regulated environments, implement additional isolation — run OpenClaw on dedicated infrastructure, restrict network access, audit installed skills, and consider enterprise wrappers like NVIDIA's NemoClaw.

---

## Best Practices

### Good: Start with Simple Tasks

```python
# Good: Start simple, expand gradually
async def my_first_skill(context: Context):
    # Simple file organization
    files = await list_files("./downloads")
    for file in files:
        category = categorize(file)
        await move_file(file, f"./organized/{category}/")
    return "Files organized"
```text

### Bad: Over-Complex Initial Workflows

```python
# Bad: Too complex for initial testing
async def complex_workflow(context: Context):
    # Too many dependencies
    data = await fetch_api_1()  # Might fail
    processed = await process_1(data)  # Depends on above
    await save_to_db(processed)  # Depends on above
    await send_notifications()  # Depends on above
    # If any step fails, entire workflow fails
```text

### Good: Error Handling & Recovery

```python
# Good: Robust error handling
async def robust_workflow(context: Context):
    try:
        # Try primary method
        result = await primary_method()
    except PrimaryError:
        # Fallback to alternative
        try:
            result = await fallback_method()
        except FallbackError:
            # Notify and log
            await notify_admin("Workflow failed")
            raise
        
    # Save checkpoint
    await save_checkpoint(context.workflow_id, result)
    
    return result
```text

### Good: Rate Limiting & Resource Management

```python
# Good: Respect API limits
class RateLimitedSkill:
    def __init__(self):
        self.limiter = TokenBucket(rate=10, capacity=10)
    
    async def execute(self, params):
        # Wait for rate limit
        await self.limiter.acquire()
        
        # Execute with retry
        for attempt in range(3):
            try:
                return await self._do_work(params)
            except RateLimitError:
                await asyncio.sleep(2 ** attempt)  # Exponential backoff
        raise MaxRetriesExceeded()
```text

---

## Deployment Options

### Local Development

```bash
# Development mode with hot reload
uv run openclaw --dev

# Debug specific channel
uv run openclaw --debug telegram
```text

### Production Server

```bash
# Production with systemd
sudo cp openclaw.service /etc/systemd/system/
sudo systemctl enable openclaw
sudo systemctl start openclaw
```text

```ini
# openclaw.service
[Unit]
Description=OpenClaw AI Agent
After=network.target

[Service]
Type=simple
User=openclaw
WorkingDirectory=/opt/openclaw
ExecStart=/usr/bin/uv run openclaw
Restart=always
Environment=ANTHROPIC_API_KEY=xxx

[Install]
WantedBy=multi-user.target
```text

### Cloud Deployment

```yaml
# Railway/Render render.yaml
services:
  - name: openclaw
    buildCommand: uv sync
    startCommand: uv run openclaw
    envVars:
      - key: LLM_PROVIDER
        value: anthropic
      - key: ANTHROPIC_API_KEY
        sync: false
```text

---

## Comparison with Alternatives

| Feature | OpenClaw | Anthropic Computer Use | OpenAI Operator |
|---------|----------|------------------------|-----------------|
| **Open Source** | Yes | No | No |
| **Local Deploy** | Yes | No | No |
| **Memory** | Persistent | Session | Session |
| **Channels** | 10+ | API only | API only |
| **Cost** | API only | API only | API only |
| **Custom Skills** | Yes | Limited | Limited |
| **Scheduling** | Yes | No | No |

### OpenClaw vs. AI Frameworks

| Feature | OpenClaw | LangChain | AutoGPT |
|---------|----------|-----------|---------|
| **Self-hosted** | Yes | Yes | Yes |
| **Platform integration** | Multiple | No | No |
| **Browser control** | Yes | No | Yes |
| **File system access** | Yes | No | Yes |
| **Visual interface** | Yes | No | No |
| **Plugin system** | Yes | Yes | No |
| **Active development** | Yes | Yes | Limited |

### OpenClaw vs. Self-Hosted & Open-Source Alternatives

| Tool | Type | Self-Host | Channels | Skills | Stars | Best For |
|------|------|:---:|:---:|:---:|:---:|---------|
| **OpenClaw** | Personal agent framework | Yes | 20+ | 44K+ | 350K | Maximum autonomy, largest ecosystem |
| **NanoClaw** | Minimalist OpenClaw fork | Yes | 2-3 | Limited | 20K | Lightweight deployment, Raspberry Pi |
| **NemoClaw** | Enterprise OpenClaw (NVIDIA) | Yes | OpenClaw-compat | 44K+ | N/A | Regulated environments, OS sandboxing |
| **Hermes Agent** | Server-oriented agent (Nous) | Yes | Telegram, Discord, Slack, Signal | Growing | 111K | Developers wanting full model control |
| **n8n** | Visual workflow + AI | Yes | Via integrations | 400+ nodes | 48K | Business process automation with AI |
| **Dify** | LLM app builder | Yes | API/embed | RAG pipelines | 100K | Building AI products with visual builder |
| **Claude Code** | Terminal agent | No | CLI | MCP tools | N/A | Coding workflows, dev productivity |
| **Manus** | Cloud autonomous agent | No | Web | Multi-agent | N/A | Long-horizon task execution |

**NanoClaw** is a minimalist successor to OpenClaw that runs on a Raspberry Pi and fits in roughly 4,000 lines of code. It is faster to start and uses less memory, but lacks the broader skill ecosystem and multi-channel support. Best for learning, prototypes, or constrained hardware.

**NemoClaw** is NVIDIA's enterprise security wrapper for OpenClaw, announced at GTC March 2026. It adds OS-level sandboxing, managed inference proxy through NVIDIA NIM, built-in safety guardrails, and inherits OpenClaw's 44,000+ skill ecosystem. It requires NVIDIA OpenShell infrastructure, ruling out low-cost VPS options.

**Hermes Agent** (Nous Research) crossed 111,000 GitHub stars by focusing on self-improving models — it gets better at your specific tasks over time. It offers five sandbox backends (local, Docker, SSH, Singularity, Modal) with container hardening. Best for developers who want maximum model control and server-oriented deployment.

**n8n** is not a personal AI assistant but a workflow automation platform with AI Agent nodes. With 48,000 GitHub stars and 400+ integrations, it excels at connecting AI to business systems. Use n8n when your primary need is cross-service automation with an AI component, not a personal assistant.

**Dify** is an open-source LLM application platform with 100,000+ GitHub stars. It provides a visual workflow builder, built-in RAG pipeline, vector database support, and monitoring. Best for teams building customer-facing AI products or internal tools.

**Claude Code** is Anthropic's terminal-based AI agent for software development. Unlike OpenClaw, it keeps the developer in the loop and focuses on code writing, editing, and reasoning. Not designed for autonomous task execution.

**Manus**, acquired by Meta for approximately $2B in early 2026, is a cloud-based autonomous AI agent for long-horizon task execution. It excels at research, code generation, and document production but runs entirely on Meta's servers.

---

## Common Issues & Solutions

### Debug Mode

```bash
# Enable debug logging
DEBUG=openclaw:* npm start

# Debug specific module
DEBUG=openclaw:browser npm start
DEBUG=openclaw:llm npm start
```text

### Quick Troubleshooting Guide

| Issue | Solution |
|-------|----------|
| Bot not responding | Check platform webhook configuration |
| Browser automation fails | Install Chrome/Chromium dependencies |
| LLM API errors | Verify API key and quota |
| Memory issues | Increase RAM or reduce concurrent tasks |
| Slow responses | Use local model or increase timeout |

### Issue: Browser Not Starting

```bash
# Install browser dependencies
npx playwright install chromium

# Or use system browser
BROWSER_TYPE=firefox
```text

### Issue: Rate Limit Errors

```python
# Implement exponential backoff
async def with_retry(func, max_retries=3):
    for i in range(max_retries):
        try:
            return await func()
        except RateLimitError:
            await asyncio.sleep(2 ** i)
    raise
```text

### Issue: Memory Full

```python
# Configure memory limits
memory:
  max_conversation_length: 1000
  vector_store_cleanup:
    enabled: true
    older_than_days: 30
```text

---

## Future of OpenClaw

OpenClaw represents the next evolution of AI agents - from passive assistants to active executors. With the founder now leading OpenAI's agent division, expect:

- **Better computer vision** - More accurate screen understanding
- **Improved planning** - Complex multi-step reasoning
- **Enhanced memory** - Long-term learning and adaptation
- **More integrations** - Expanded ecosystem support

---

## Conclusion

OpenClaw is transforming how we think about AI assistants. Instead of just answering questions, it actively works alongside us, handling repetitive tasks and automating workflows. Its open-source nature, local-first approach, and powerful capabilities make it an essential tool for anyone looking to leverage AI for real productivity gains.

Whether you're automating your workday, building a personal AI assistant, or exploring the future of human-AI collaboration, OpenClaw provides the foundation for the next generation of AI-powered automation.

Start small, experiment safely, and watch your digital worker handle the tasks that used to consume your days.

---

## Related Articles

- [Agent-to-Agent Protocol: A2A for Multi-Agent Systems](/ai/agent-to-agent-protocol-a2a-multi-agent-systems/)
- [Model Context Protocol: MCP Complete Guide](/ai/model-context-protocol-mcp-complete-guide/)
- [Building AI Agents with LangGraph](/ai/building-ai-agents-langgraph/)
- [Introduction to Agentic AI](/ai/introduction-to-agentic-ai/)
- [AI Automation Workflows 2026](/ai/ai-agent-workflow-automation-complete-guide-2026/)
- [n8n Workflow Automation Guide](/ai/n8n-complete-guide-ai-automation/)
- [Self-Hosted AI Starter Kit](/ai/self-hosted-llm-automation-complete-guide-2026/)

## Resources

- [OpenClaw GitHub](https://github.com/openclaw/openclaw) — 350K+ stars, MIT license
- [OpenClaw Website](https://openclaw.ai) — Official project site
- [Documentation](https://openclaw.dev) — Setup guides, API reference, security docs
- [OpenClaw 2026: Complete Self-Hosted AI Agent Setup](https://petronellatech.com/blog/openclaw-ai-agent-guide-2026/) — Petronella Technology Group guide
- [10 Best OpenClaw Alternatives (2026)](https://www.vellum.ai/blog/best-openclaw-alternatives) — Vellum comparison review
- [Ollama](https://ollama.ai) — Local LLM provider for self-hosted inference
- [OpenAI Documentation](https://platform.openai.com/docs)
- [Hugging Face Documentation](https://huggingface.co/docs)
- [Papers with Code](https://paperswithcode.com/)