# OpenClaw Architecture, Deployment Modes, and Ollama Integration

## Research Report for Timmy Time Dashboard Project

**Issue:** #721 — [Kimi Research] OpenClaw architecture, deployment modes, and Ollama integration  
**Date:** 2026-03-21  
**Author:** Kimi (Moonshot AI)  
**Status:** Complete

---

## Executive Summary

OpenClaw is an open-source AI agent framework that bridges messaging platforms (WhatsApp, Telegram, Slack, Discord, iMessage) to AI coding agents through a centralized gateway. Originally known as Clawdbot and Moltbot, it was rebranded to OpenClaw in early 2026. This report provides a comprehensive analysis of OpenClaw's architecture, deployment options, Ollama integration capabilities, and suitability for deployment on resource-constrained VPS environments like the Hermes DigitalOcean droplet (2GB RAM / 1 vCPU).

**Key Finding:** Running OpenClaw with local LLMs on a 2GB RAM VPS is **not recommended**. The absolute minimum for a text-only agent with external API models is 4GB RAM. For local model inference via Ollama, 8-16GB RAM is the practical minimum. A hybrid approach using OpenRouter as the primary provider with Ollama as fallback is the most viable configuration for small VPS deployments.

---

## 1. Architecture Overview

### 1.1 Core Components

OpenClaw follows a **hub-and-spoke (轴辐式)** architecture optimized for multi-agent task execution:

```
┌─────────────────────────────────────────────────────────────────────────┐
│                         OPENCLAW ARCHITECTURE                           │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌──────────────┐     ┌──────────────┐     ┌──────────────┐            │
│  │  WhatsApp    │     │   Telegram   │     │   Discord    │            │
│  │   Channel    │     │   Channel    │     │   Channel    │            │
│  └──────┬───────┘     └──────┬───────┘     └──────┬───────┘            │
│         │                    │                    │                     │
│         └────────────────────┼────────────────────┘                     │
│                              ▼                                         │
│                    ┌──────────────────┐                                │
│                    │     Gateway      │◄─────── WebSocket/API         │
│                    │   (Port 18789)   │         Control Plane         │
│                    └────────┬─────────┘                                │
│                             │                                          │
│              ┌──────────────┼──────────────┐                          │
│              ▼              ▼              ▼                          │
│       ┌──────────┐   ┌──────────┐   ┌──────────┐                     │
│       │ Agent A  │   │ Agent B  │   │  Pi Agent│                     │
│       │ (main)   │   │ (coder)  │   │(delegate)│                     │
│       └────┬─────┘   └────┬─────┘   └────┬─────┘                     │
│            │              │              │                           │
│            └──────────────┼──────────────┘                           │
│                           ▼                                          │
│              ┌────────────────────────┐                              │
│              │     LLM Router         │                              │
│              │  (Primary/Fallback)    │                              │
│              └───────────┬────────────┘                              │
│                          │                                           │
│        ┌─────────────────┼─────────────────┐                         │
│        ▼                 ▼                 ▼                         │
│   ┌─────────┐      ┌─────────┐      ┌─────────┐                     │
│   │ Ollama  │      │ OpenAI  │      │Anthropic│                     │
│   │(local)  │      │(cloud)  │      │(cloud)  │                     │
│   └─────────┘      └─────────┘      └─────────┘                     │
│        │                                                    ┌─────┐ │
│        └────────────────────────────────────────────────────►│ MCP │ │
│                                                              │Tools│ │
│                                                              └─────┘ │
│                                                                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐                  │
│  │   Memory     │  │   Skills     │  │  Workspace   │                  │
│  │   (SOUL.md)  │  │  (SKILL.md)  │  │  (sessions)  │                  │
│  └──────────────┘  └──────────────┘  └──────────────┘                  │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘
```

### 1.2 Component Deep Dive

| Component | Purpose | Configuration File |
|-----------|---------|-------------------|
| **Gateway** | Central control plane, WebSocket/API server, session management | `gateway` section in `openclaw.json` |
| **Pi Agent** | Core agent runner, "指挥中心" - schedules LLM calls, tool execution, error handling | `agents` section in `openclaw.json` |
| **Channels** | Messaging platform integrations (Telegram, WhatsApp, Slack, Discord, iMessage) | `channels` section in `openclaw.json` |
| **SOUL.md** | Agent persona definition - personality, communication style, behavioral guidelines | `~/.openclaw/workspace/SOUL.md` |
| **AGENTS.md** | Multi-agent configuration, routing rules, agent specialization definitions | `~/.openclaw/workspace/AGENTS.md` |
| **Workspace** | File system for agent state, session data, temporary files | `~/.openclaw/workspace/` |
| **Skills** | Bundled tools, prompts, configurations that teach agents specific tasks | `~/.openclaw/workspace/skills/` |
| **Sessions** | Conversation history, context persistence between interactions | `~/.openclaw/agents/<agent>/sessions/` |
| **MCP Tools** | Model Context Protocol integration for external tool access | Via `mcporter` or native MCP |

### 1.3 Agent Runner Execution Flow

According to OpenClaw documentation, a complete agent run follows these stages:

1. **Queuing** - Session-level queue (serializes same-session requests) → Global queue (controls total concurrency)
2. **Preparation** - Parse workspace, provider/model, thinking level parameters
3. **Plugin Loading** - Load relevant skills based on task context
4. **Memory Retrieval** - Fetch relevant context from SOUL.md and conversation history
5. **LLM Inference** - Send prompt to configured provider with tool definitions
6. **Tool Execution** - Execute any tool calls returned by the LLM
7. **Response Generation** - Format and return final response to the channel
8. **Memory Storage** - Persist conversation and results to session storage

---

## 2. Deployment Modes

### 2.1 Comparison Matrix

| Deployment Mode | Best For | Setup Complexity | Resource Overhead | Stability |
|----------------|----------|------------------|-------------------|-----------|
| **npm global** | Development, quick testing | Low | Minimal (~200MB) | Moderate |
| **Docker** | Production, isolation, reproducibility | Medium | Higher (~2.5GB base image) | High |
| **Docker Compose** | Multi-service stacks, complex setups | Medium-High | Higher | High |
| **Bare metal/systemd** | Maximum performance, dedicated hardware | High | Minimal | Moderate |

### 2.2 NPM Global Installation (Recommended for Quick Start)

```bash
# One-line installer
curl -fsSL https://openclaw.ai/install.sh | bash

# Or manual npm install
npm install -g openclaw

# Initialize configuration
openclaw onboard

# Start gateway
openclaw gateway
```

**Pros:**
- Fastest setup (~30 seconds)
- Direct access to host resources
- Easy updates via `npm update -g openclaw`

**Cons:**
- Node.js 22+ dependency required
- No process isolation
- Manual dependency management

### 2.3 Docker Deployment (Recommended for Production)

```bash
# Pull and run
docker pull openclaw/openclaw:latest
docker run -d \
  --name openclaw \
  -p 127.0.0.1:18789:18789 \
  -v ~/.openclaw:/root/.openclaw \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  openclaw/openclaw:latest

# Or with Docker Compose
docker compose -f compose.yml --env-file .env up -d --build
```

**Docker Compose Configuration (production-ready):**

```yaml
version: '3.8'
services:
  openclaw:
    image: openclaw/openclaw:latest
    container_name: openclaw
    restart: unless-stopped
    ports:
      - "127.0.0.1:18789:18789"  # Never expose to 0.0.0.0
    volumes:
      - ./openclaw-data:/root/.openclaw
      - ./workspace:/root/.openclaw/workspace
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
      - OLLAMA_API_KEY=ollama-local
    networks:
      - openclaw-net
    # Resource limits for small VPS
    deploy:
      resources:
        limits:
          cpus: '1.5'
          memory: 3G
        reservations:
          cpus: '0.5'
          memory: 1G

networks:
  openclaw-net:
    driver: bridge
```

### 2.4 Bare Metal / Systemd Installation

For running as a system service on Linux:

```bash
# Create systemd service
sudo tee /etc/systemd/system/openclaw.service > /dev/null <<EOF
[Unit]
Description=OpenClaw Gateway
After=network.target

[Service]
Type=simple
User=openclaw
Group=openclaw
WorkingDirectory=/home/openclaw
Environment="PATH=/usr/local/bin:/usr/bin:/bin"
Environment="NODE_ENV=production"
Environment="ANTHROPIC_API_KEY=sk-ant-..."
ExecStart=/usr/local/bin/openclaw gateway
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl daemon-reload
sudo systemctl enable openclaw
sudo systemctl start openclaw
```

### 2.5 Recommended Deployment for 2GB RAM VPS

**⚠️ Critical Finding:** OpenClaw's official minimum is 4GB RAM. On a 2GB VPS:

1. **Do NOT run local LLMs** - Use external API providers exclusively
2. **Use npm installation** - Docker overhead is too heavy
3. **Disable browser automation** - Chromium requires 2-4GB alone
4. **Enable swap** - Critical for preventing OOM kills
5. **Use OpenRouter** - Cheap/free tier models reduce costs

**Setup script for 2GB VPS:**

```bash
#!/bin/bash
# openclaw-minimal-vps.sh
# Setup for 2GB RAM VPS - EXTERNAL API ONLY

# Create 4GB swap
sudo fallocate -l 4G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

# Install Node.js 22
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo bash -
sudo apt-get install -y nodejs

# Install OpenClaw
npm install -g openclaw

# Configure for minimal resource usage
mkdir -p ~/.openclaw
cat > ~/.openclaw/openclaw.json <<'EOF'
{
  "gateway": {
    "bind": "127.0.0.1",
    "port": 18789,
    "mode": "local"
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "openrouter/google/gemma-3-4b-it:free",
        "fallbacks": [
          "openrouter/meta/llama-3.1-8b-instruct:free"
        ]
      },
      "maxIterations": 15,
      "timeout": 120
    }
  },
  "channels": {
    "telegram": {
      "enabled": true,
      "dmPolicy": "pairing"
    }
  }
}
EOF

# Set OpenRouter API key
export OPENROUTER_API_KEY="sk-or-v1-..."

# Start gateway
openclaw gateway &
```

---

## 3. Ollama Integration

### 3.1 Architecture

OpenClaw integrates with Ollama through its native `/api/chat` endpoint, supporting both streaming responses and tool calling simultaneously:

```
┌──────────────┐      HTTP/JSON      ┌──────────────┐      GGUF/CPU/GPU     ┌──────────┐
│   OpenClaw   │◄───────────────────►│    Ollama    │◄────────────────────►│  Local   │
│   Gateway    │   /api/chat         │   Server     │   Model inference    │   LLM    │
│              │   Port 11434        │  Port 11434  │                      │          │
└──────────────┘                     └──────────────┘                      └──────────┘
```

### 3.2 Configuration

**Basic Ollama Setup:**

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

# Start server
ollama serve

# Pull a tool-capable model
ollama pull qwen2.5-coder:7b
ollama pull llama3.1:8b

# Configure OpenClaw
export OLLAMA_API_KEY="ollama-local"  # Any non-empty string works
```

**OpenClaw Configuration for Ollama:**

```json
{
  "models": {
    "providers": {
      "ollama": {
        "baseUrl": "http://localhost:11434",
        "apiKey": "ollama-local",
        "api": "ollama",
        "models": [
          {
            "id": "qwen2.5-coder:7b",
            "name": "Qwen 2.5 Coder 7B",
            "contextWindow": 32768,
            "maxTokens": 8192,
            "cost": { "input": 0, "output": 0 }
          },
          {
            "id": "llama3.1:8b",
            "name": "Llama 3.1 8B",
            "contextWindow": 128000,
            "maxTokens": 8192,
            "cost": { "input": 0, "output": 0 }
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "ollama/qwen2.5-coder:7b",
        "fallbacks": ["ollama/llama3.1:8b"]
      }
    }
  }
}
```

### 3.3 Context Window Requirements

**⚠️ Critical Requirement:** OpenClaw requires a minimum **64K token context window** for reliable multi-step task execution.

| Model | Parameters | Context Window | Tool Support | OpenClaw Compatible |
|-------|-----------|----------------|--------------|---------------------|
| **llama3.1** | 8B | 128K | ✅ Yes | ✅ Yes |
| **qwen2.5-coder** | 7B | 32K | ✅ Yes | ⚠️ Below minimum |
| **qwen2.5-coder** | 32B | 128K | ✅ Yes | ✅ Yes |
| **gpt-oss** | 20B | 128K | ✅ Yes | ✅ Yes |
| **glm-4.7-flash** | - | 128K | ✅ Yes | ✅ Yes |
| **deepseek-coder-v2** | 33B | 128K | ✅ Yes | ✅ Yes |
| **mistral-small3.1** | - | 128K | ✅ Yes | ✅ Yes |

**Context Window Configuration:**

For models that don't report context window via Ollama's API:

```bash
# Create custom Modelfile with extended context
cat > ~/qwen-custom.modelfile <<EOF
FROM qwen2.5-coder:7b
PARAMETER num_ctx 65536
PARAMETER temperature 0.7
EOF

# Create custom model
ollama create qwen2.5-coder-64k -f ~/qwen-custom.modelfile
```

### 3.4 Models for Small VPS (≤8B Parameters)

For resource-constrained environments (2-4GB RAM):

| Model | Quantization | RAM Required | VRAM Required | Performance |
|-------|-------------|--------------|---------------|-------------|
| **Llama 3.1 8B** | Q4_K_M | ~5GB | ~6GB | Good |
| **Llama 3.2 3B** | Q4_K_M | ~2.5GB | ~3GB | Basic |
| **Qwen 2.5 7B** | Q4_K_M | ~5GB | ~6GB | Good |
| **Qwen 2.5 3B** | Q4_K_M | ~2.5GB | ~3GB | Basic |
| **DeepSeek 7B** | Q4_K_M | ~5GB | ~6GB | Good |
| **Phi-4 4B** | Q4_K_M | ~3GB | ~4GB | Moderate |

**⚠️ Verdict for 2GB VPS:** Running local LLMs is **NOT viable**. Use external APIs only.

---

## 4. OpenRouter Integration (Fallback Strategy)

### 4.1 Overview

OpenRouter provides a unified API gateway to multiple LLM providers, enabling:
- Single API key access to 200+ models
- Automatic failover between providers
- Free tier models for cost-conscious deployments
- Unified billing and usage tracking

### 4.2 Configuration

**Environment Variable Setup:**

```bash
export OPENROUTER_API_KEY="sk-or-v1-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
```

**OpenClaw Configuration:**

```json
{
  "models": {
    "providers": {
      "openrouter": {
        "apiKey": "${OPENROUTER_API_KEY}",
        "baseUrl": "https://openrouter.ai/api/v1"
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "openrouter/anthropic/claude-sonnet-4-6",
        "fallbacks": [
          "openrouter/google/gemini-3.1-pro",
          "openrouter/meta/llama-3.3-70b-instruct",
          "openrouter/google/gemma-3-4b-it:free"
        ]
      }
    }
  }
}
```

### 4.3 Recommended Free/Cheap Models on OpenRouter

For cost-conscious VPS deployments:

| Model | Cost | Context | Best For |
|-------|------|---------|----------|
| **google/gemma-3-4b-it:free** | Free | 128K | General tasks, simple automation |
| **meta/llama-3.1-8b-instruct:free** | Free | 128K | General tasks, longer contexts |
| **deepseek/deepseek-chat-v3.2** | $0.53/M | 64K | Code generation, reasoning |
| **xiaomi/mimo-v2-flash** | $0.40/M | 128K | Fast responses, basic tasks |
| **qwen/qwen3-coder-next** | $1.20/M | 128K | Code-focused tasks |

### 4.4 Hybrid Configuration (Recommended for Timmy)

A production-ready configuration for the Hermes VPS:

```json
{
  "models": {
    "providers": {
      "openrouter": {
        "apiKey": "${OPENROUTER_API_KEY}",
        "models": [
          {
            "id": "google/gemma-3-4b-it:free",
            "name": "Gemma 3 4B (Free)",
            "contextWindow": 131072,
            "maxTokens": 8192,
            "cost": { "input": 0, "output": 0 }
          },
          {
            "id": "deepseek/deepseek-chat-v3.2",
            "name": "DeepSeek V3.2",
            "contextWindow": 64000,
            "maxTokens": 8192,
            "cost": { "input": 0.00053, "output": 0.00053 }
          }
        ]
      },
      "ollama": {
        "baseUrl": "http://localhost:11434",
        "apiKey": "ollama-local",
        "models": [
          {
            "id": "llama3.2:3b",
            "name": "Llama 3.2 3B (Local Fallback)",
            "contextWindow": 128000,
            "maxTokens": 4096,
            "cost": { "input": 0, "output": 0 }
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "openrouter/google/gemma-3-4b-it:free",
        "fallbacks": [
          "openrouter/deepseek/deepseek-chat-v3.2",
          "ollama/llama3.2:3b"
        ]
      },
      "maxIterations": 10,
      "timeout": 90
    }
  }
}
```

---

## 5. Hardware Constraints & VPS Viability

### 5.1 System Requirements Summary

| Component | Minimum | Recommended | Notes |
|-----------|---------|-------------|-------|
| **CPU** | 2 vCPU | 4 vCPU | Dedicated preferred over shared |
| **RAM** | 4 GB | 8 GB | 2GB causes OOM with external APIs |
| **Storage** | 40 GB SSD | 80 GB NVMe | Docker images are ~10-15GB |
| **Network** | 100 Mbps | 1 Gbps | For API calls and model downloads |
| **OS** | Ubuntu 22.04/Debian 12 | Ubuntu 24.04 LTS | Linux required for production |

### 5.2 2GB RAM VPS Analysis

**Can it work?** Yes, with severe limitations:

✅ **What works:**
- Text-only agents with external API providers
- Single Telegram/Discord channel
- Basic file operations and shell commands
- No browser automation

❌ **What doesn't work:**
- Local LLM inference via Ollama
- Browser automation (Chromium needs 2-4GB)
- Multiple concurrent channels
- Python environment-heavy skills

**Required mitigations for 2GB VPS:**

```bash
# 1. Create substantial swap
sudo fallocate -l 4G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

# 2. Configure swappiness
echo 'vm.swappiness=60' | sudo tee -a /etc/sysctl.conf
sudo sysctl -p

# 3. Limit Node.js memory
export NODE_OPTIONS="--max-old-space-size=1536"

# 4. Use external APIs only - NO OLLAMA
# 5. Disable browser skills
# 6. Set conservative concurrency limits
```

### 5.3 4-bit Quantization Viability

**Qwen 2.5 7B Q4_K_M on 2GB VPS:**
- Model size: ~4.5GB
- RAM required at runtime: ~5-6GB
- **Verdict:** Will cause immediate OOM on 2GB VPS
- **Even with 4GB VPS:** Marginal, heavy swap usage, poor performance

**Viable models for 4GB VPS with Ollama:**
- Llama 3.2 3B Q4_K_M (~2.5GB RAM)
- Qwen 2.5 3B Q4_K_M (~2.5GB RAM)
- Phi-4 4B Q4_K_M (~3GB RAM)

---

## 6. Security Configuration

### 6.1 Network Ports

| Port | Purpose | Exposure |
|------|---------|----------|
| **18789/tcp** | OpenClaw Gateway (WebSocket/HTTP) | **NEVER expose to internet** |
| **11434/tcp** | Ollama API (if running locally) | Localhost only |
| **22/tcp** | SSH | Restrict to known IPs |

**⚠️ CRITICAL:** Never expose port 18789 to the public internet. Use Tailscale or SSH tunnels for remote access.

### 6.2 Tailscale Integration

Tailscale provides zero-configuration VPN mesh for secure remote access:

```bash
# Install Tailscale
curl -fsSL https://tailscale.com/install.sh | sh
sudo tailscale up

# Get Tailscale IP
tailscale ip
# Returns: 100.x.y.z

# Configure OpenClaw to bind to Tailscale
cat > ~/.openclaw/openclaw.json <<EOF
{
  "gateway": {
    "bind": "tailnet",
    "port": 18789
  },
  "tailscale": {
    "mode": "on",
    "resetOnExit": false
  }
}
EOF
```

**Tailscale vs SSH Tunnel:**

| Feature | Tailscale | SSH Tunnel |
|---------|-----------|------------|
| Setup | Very easy | Moderate |
| Persistence | Automatic | Requires autossh |
| Multiple devices | Built-in | One tunnel per connection |
| NAT traversal | Works | Requires exposed SSH |
| Access control | Tailscale ACL | SSH keys |

### 6.3 Firewall Configuration (UFW)

```bash
# Default deny
sudo ufw default deny incoming
sudo ufw default allow outgoing

# Allow SSH
sudo ufw allow 22/tcp

# Allow Tailscale only (if using)
sudo ufw allow in on tailscale0 to any port 18789

# Block public access to OpenClaw
# (bind is 127.0.0.1, so this is defense in depth)

sudo ufw enable
```

### 6.4 Authentication Configuration

```json
{
  "gateway": {
    "bind": "127.0.0.1",
    "port": 18789,
    "auth": {
      "mode": "token",
      "token": "your-64-char-hex-token-here"
    },
    "controlUi": {
      "allowedOrigins": [
        "http://localhost:18789",
        "https://your-domain.tailnet-name.ts.net"
      ],
      "allowInsecureAuth": false,
      "dangerouslyDisableDeviceAuth": false
    }
  }
}
```

**Generate secure token:**

```bash
openssl rand -hex 32
```

### 6.5 Sandboxing Considerations

OpenClaw executes arbitrary shell commands and file operations by default. For production:

1. **Run as non-root user:**
```bash
sudo useradd -r -s /bin/false openclaw
sudo mkdir -p /home/openclaw/.openclaw
sudo chown -R openclaw:openclaw /home/openclaw
```

2. **Use Docker for isolation:**
```bash
docker run --security-opt=no-new-privileges \
  --cap-drop=ALL \
  --read-only \
  --tmpfs /tmp:noexec,nosuid,size=100m \
  openclaw/openclaw:latest
```

3. **Enable dmPolicy for channels:**
```json
{
  "channels": {
    "telegram": {
      "dmPolicy": "pairing"  // Require one-time code for new contacts
    }
  }
}
```

---

## 7. MCP (Model Context Protocol) Tools

### 7.1 Overview

MCP is an open standard created by Anthropic (donated to Linux Foundation in Dec 2025) that lets AI applications connect to external tools through a universal interface. Think of it as "USB-C for AI."

### 7.2 MCP vs OpenClaw Skills

| Aspect | MCP | OpenClaw Skills |
|--------|-----|-----------------|
| **Protocol** | Standardized (Anthropic) | OpenClaw-specific |
| **Isolation** | Process-isolated | Runs in agent context |
| **Security** | Higher (sandboxed) | Lower (full system access) |
| **Discovery** | Automatic via protocol | Manual via SKILL.md |
| **Ecosystem** | 10,000+ servers | 5400+ skills |

**Note:** OpenClaw currently has limited native MCP support. Use `mcporter` tool for MCP integration.

### 7.3 Using MCPorter (MCP Bridge)

```bash
# Install mcporter
clawhub install mcporter

# Configure MCP server
mcporter config add github \
  --url "https://api.github.com/mcp" \
  --token "ghp_..."

# List available tools
mcporter list

# Call MCP tool
mcporter call github.list_repos --owner "rockachopa"
```

### 7.4 Popular MCP Servers

| Server | Purpose | Integration |
|--------|---------|-------------|
| **GitHub** | Repo management, PRs, issues | `mcp-github` |
| **Slack** | Messaging, channel management | `mcp-slack` |
| **PostgreSQL** | Database queries | `mcp-postgres` |
| **Filesystem** | File operations (sandboxed) | `mcp-filesystem` |
| **Brave Search** | Web search | `mcp-brave` |

---

## 8. Recommendations for Timmy Time Dashboard

### 8.1 Deployment Strategy for Hermes VPS (2GB RAM)

Given the hardware constraints, here's the recommended approach:

**Option A: External API Only (Recommended)**
```
┌─────────────────────────────────────────┐
│         Hermes VPS (2GB RAM)            │
│  ┌─────────────────────────────────┐    │
│  │     OpenClaw Gateway            │    │
│  │     (npm global install)        │    │
│  └─────────────┬───────────────────┘    │
│                │                        │
│                ▼                        │
│  ┌─────────────────────────────────┐    │
│  │   OpenRouter API (Free Tier)    │    │
│  │   google/gemma-3-4b-it:free     │    │
│  └─────────────────────────────────┘    │
│                                         │
│  NO OLLAMA - insufficient RAM          │
└─────────────────────────────────────────┘
```

**Option B: Hybrid with External Ollama**
```
┌──────────────────────┐      ┌──────────────────────────┐
│   Hermes VPS (2GB)   │      │   Separate Ollama Host   │
│  ┌────────────────┐  │      │  ┌────────────────────┐  │
│  │ OpenClaw       │  │◄────►│  │ Ollama Server      │  │
│  │ (external API) │  │      │  │ (8GB+ RAM required)│  │
│  └────────────────┘  │      │  └────────────────────┘  │
└──────────────────────┘      └──────────────────────────┘
```

### 8.2 Configuration Summary

```json
{
  "gateway": {
    "bind": "127.0.0.1",
    "port": 18789,
    "auth": {
      "mode": "token",
      "token": "GENERATE_WITH_OPENSSL_RAND"
    }
  },
  "models": {
    "providers": {
      "openrouter": {
        "apiKey": "${OPENROUTER_API_KEY}",
        "models": [
          {
            "id": "google/gemma-3-4b-it:free",
            "contextWindow": 131072,
            "maxTokens": 4096
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "openrouter/google/gemma-3-4b-it:free"
      },
      "maxIterations": 10,
      "timeout": 90,
      "maxConcurrent": 2
    }
  },
  "channels": {
    "telegram": {
      "enabled": true,
      "dmPolicy": "pairing"
    }
  }
}
```

### 8.3 Migration Path (Future)

When upgrading to a larger VPS (4-8GB RAM):

1. **Phase 1:** Enable Ollama with Llama 3.2 3B as fallback
2. **Phase 2:** Add browser automation skills (requires 4GB+ RAM)
3. **Phase 3:** Enable multi-agent routing with specialized agents
4. **Phase 4:** Add MCP server integration for external tools

---

## 9. References

1. OpenClaw Official Documentation: https://docs.openclaw.ai
2. Ollama Integration Guide: https://docs.ollama.com/integrations/openclaw
3. OpenRouter Documentation: https://openrouter.ai/docs
4. MCP Specification: https://modelcontextprotocol.io
5. OpenClaw Community Discord: https://discord.gg/openclaw
6. GitHub Repository: https://github.com/openclaw/openclaw

---

## 10. Appendix: Quick Command Reference

```bash
# Installation
curl -fsSL https://openclaw.ai/install.sh | bash

# Configuration
openclaw onboard                    # Interactive setup
openclaw configure                  # Edit config
openclaw config set <key> <value>   # Set specific value

# Gateway management
openclaw gateway                    # Start gateway
openclaw gateway --verbose          # Start with logs
openclaw gateway status             # Check status
openclaw gateway restart            # Restart gateway
openclaw gateway stop               # Stop gateway

# Model management
openclaw models list                # List available models
openclaw models set <model>         # Set default model
openclaw models status              # Check model status

# Diagnostics
openclaw doctor                     # System health check
openclaw doctor --repair            # Auto-fix issues
openclaw security audit             # Security check

# Dashboard
openclaw dashboard                  # Open web UI
```

---

*End of Research Report*