# OpenHands - AI Coding Agent

OpenHands is an autonomous AI software development agent that can execute code, browse the web, and interact with your development environment.

## Deployment Options

### Option 1: CLI Mode (Recommended) ✅

The CLI runs directly on the host machine without Docker sandbox containers. This is the **recommended approach** for homelab setups.

**Why CLI is better for homelab:**
- No Docker-in-Docker networking issues
- More private (see [Privacy Considerations](#privacy-considerations))
- Simpler setup and maintenance
- Works reliably on Linux hosts

#### Installation

```bash
# Install uv (fast Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.local/bin/env

# Install OpenHands CLI
uv tool install openhands --python 3.12
```

#### Configuration

Create a wrapper script for easy usage:

```bash
cat > ~/openhands-cli.sh << 'EOF'
#!/bin/bash
export PATH=$HOME/.local/bin:$PATH
export LLM_MODEL=anthropic/claude-sonnet-4-20250514
export LLM_API_KEY=REDACTED_API_KEY

if [ $# -eq 0 ]; then
    openhands --override-with-envs --always-approve
else
    openhands --override-with-envs "$@"
fi
EOF
chmod +x ~/openhands-cli.sh
```

#### Usage

```bash
# Interactive TUI mode
~/openhands-cli.sh

# Headless mode (for scripts/automation)
~/openhands-cli.sh --headless -t "Write a Python script that lists files"

# REDACTED_APP_PASSWORD
~/openhands-cli.sh --headless -f task.txt

# Resume a conversation
~/openhands-cli.sh --resume <conversation-id>
```

### Option 2: Docker GUI Mode (Has Issues)

The Docker-based GUI spawns runtime containers dynamically. On Linux, these containers cannot resolve `host.docker.internal`, causing MCP (Model Context Protocol) failures.

**Known issues:**
- Runtime containers fail to connect back to main container
- `host.docker.internal` not resolvable in spawned containers
- Error: `Server error '500 Internal Server Error' for url 'http://host.docker.internal:XXXXX/api/conversations'`

If you still want to try Docker GUI, the compose file is at:
`hosts/vms/homelab-vm/openhands.yaml`

---

## Privacy Considerations

### CLI vs Docker GUI Privacy

| Aspect | CLI Mode | Docker GUI Mode |
|--------|----------|-----------------|
| Code execution | Runs on host directly | Runs in isolated containers |
| Network isolation | None (host network) | Partial (container network) |
| Data exposure | Full host access | Limited to mounted volumes |
| API calls | Direct to LLM provider | Direct to LLM provider |

**Both modes send your code/prompts to the LLM provider** (Anthropic, OpenAI, etc.) unless you use a local model.

### What Data Leaves Your Network?

When using cloud LLMs (Claude, GPT-4, etc.):
- Your prompts and task descriptions
- Code snippets you ask it to analyze/modify
- File contents it reads to complete tasks
- Command outputs

**To keep everything local, you need a local LLM.**

---

## Running Fully Local (Maximum Privacy)

For complete privacy, run OpenHands with a local LLM. No data leaves your network.

### Option A: Ollama (Easiest)

1. **Install Ollama** (if not already running):
   ```bash
   # On homelab VM or dedicated machine
   curl -fsSL https://ollama.com/install.sh | sh
   
   # Pull a capable coding model
   ollama pull deepseek-coder-v2:16b
   # Or for more capability (needs ~32GB RAM):
   ollama pull qwen2.5-coder:32b
   ```

2. **Configure OpenHands CLI for Ollama**:
   ```bash
   cat > ~/openhands-local.sh << 'EOF'
   #!/bin/bash
   export PATH=$HOME/.local/bin:$PATH
   export LLM_MODEL=ollama/deepseek-coder-v2:16b
   export LLM_BASE_URL=http://localhost:11434
   export LLM_API_KEY=ollama  # Required but not used
   
   openhands --override-with-envs "$@"
   EOF
   chmod +x ~/openhands-local.sh
   ```

3. **Run**:
   ```bash
   ~/openhands-local.sh --headless -t "Create a hello world Python script"
   ```

### Option B: Use Existing Ollama Stack

If you already have Ollama running (e.g., on Atlantis), point OpenHands to it:

```bash
export LLM_MODEL=ollama/deepseek-coder-v2:16b
export LLM_BASE_URL=http://atlantis.local:11434
export LLM_API_KEY=ollama
```

### Recommended Local Models for Coding

| Model | VRAM Needed | Quality | Speed |
|-------|-------------|---------|-------|
| `deepseek-coder-v2:16b` | ~12GB | Good | Fast |
| `qwen2.5-coder:32b` | ~24GB | Better | Medium |
| `codellama:34b` | ~26GB | Good | Medium |
| `deepseek-coder:33b` | ~26GB | Better | Slower |

### Option C: Local vLLM/text-generation-inference

For maximum performance with local models:

```yaml
# docker-compose for vLLM
version: '3.8'
services:
  vllm:
    image: vllm/vllm-openai:latest
    runtime: nvidia
    ports:
      - "8000:8000"
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    command: >
      --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
      --trust-remote-code
      --max-model-len 32768
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
```

Then configure OpenHands:
```bash
export LLM_MODEL=openai/deepseek-coder-v2
export LLM_BASE_URL=http://localhost:8000/v1
export LLM_API_KEY=dummy
```

---

## Privacy Comparison Summary

| Setup | Privacy Level | Performance | Cost |
|-------|---------------|-------------|------|
| Claude/GPT-4 API | ❌ Low (data sent to cloud) | ⚡ Excellent | 💰 Pay per use |
| Ollama + small model | ✅ High (fully local) | 🐢 Good | 🆓 Free |
| vLLM + large model | ✅ High (fully local) | ⚡ Very Good | 🆓 Free (needs GPU) |

---

## Troubleshooting

### CLI won't start
```bash
# Ensure PATH includes local bin
export PATH=$HOME/.local/bin:$PATH

# Reinstall if needed
uv tool install openhands --python 3.12 --force
```

### "Headless mode requires existing settings"
Use `--override-with-envs` flag to bypass the settings requirement:
```bash
openhands --headless --override-with-envs -t "your task"
```

### Local model is slow
- Use a smaller model (7B-16B parameters)
- Ensure you have enough RAM/VRAM
- Consider quantized models (Q4_K_M, Q5_K_M)

### Ollama connection refused
```bash
# Check if Ollama is running
systemctl status ollama

# Start it
sudo systemctl start ollama

# Or run manually
ollama serve
```

---

## Related Services

- **Ollama** (`Atlantis/ollama/`) - Local LLM inference
- **Perplexica** (`homelab_vm/perplexica.yaml`) - AI-powered search ([docs](../services/individual/perplexica.md))

## References

- [OpenHands Documentation](https://docs.openhands.dev/)
- [OpenHands CLI Guide](https://docs.openhands.dev/openhands/usage/cli/installation)
- [Ollama](https://ollama.com/)
- [LiteLLM Supported Models](https://docs.litellm.ai/docs/providers)