Ollama Local

# Ollama Local

Work with local Ollama models for inference, embeddings, and tool use.

## Configuration

Set your Ollama host (defaults to `http://localhost:11434`):

```bash export OLLAMA_HOST="http://localhost:11434" # Or for remote server: export OLLAMA_HOST="http://192.168.1.100:11434" ```

## Quick Reference

```bash # List models python3 scripts/ollama.py list

# Pull a model python3 scripts/ollama.py pull llama3.1:8b

# Remove a model python3 scripts/ollama.py rm modelname

# Show model details python3 scripts/ollama.py show qwen3:4b

# Chat with a model python3 scripts/ollama.py chat qwen3:4b "What is the capital of France?"

# Chat with system prompt python3 scripts/ollama.py chat llama3.1:8b "Review this code" -s "You are a code reviewer"

# Generate completion (non-chat) python3 scripts/ollama.py generate qwen3:4b "Once upon a time"

# Get embeddings python3 scripts/ollama.py embed bge-m3 "Text to embed" ```

## Model Selection

See [references/models.md](references/models.md) for full model list and selection guide.

**Quick picks:** - Fast answers: `qwen3:4b` - Coding: `qwen2.5-coder:7b` - General: `llama3.1:8b` - Reasoning: `deepseek-r1:8b`

## Tool Use

Some local models support function calling. Use `ollama_tools.py`:

```bash # Single request with tools python3 scripts/ollama_tools.py single qwen2.5-coder:7b "What's the weather in Amsterdam?"

# Full tool loop (model calls tools, gets results, responds) python3 scripts/ollama_tools.py loop qwen3:4b "Search for Python tutorials and summarize"

# Show available example tools python3 scripts/ollama_tools.py tools ```

**Tool-capable models:** qwen2.5-coder, qwen3, llama3.1, mistral

## OpenClaw Sub-Agents

Spawn local model sub-agents with `sessions_spawn`:

```python # Example: spawn a coding agent sessions_spawn( task="Review this Python code for bugs", model="ollama/qwen2.5-coder:7b", label="code-review" ) ```

Model path format: `ollama/<model-name>`

### Parallel Agents (Think Tank Pattern)

Spawn multiple local agents for collaborative tasks:

```python agents = [ {"label": "architect", "model": "ollama/gemma3:12b", "task": "Design the system architecture"}, {"label": "coder", "model": "ollama/qwen2.5-coder:7b", "task": "Implement the core logic"}, {"label": "reviewer", "model": "ollama/llama3.1:8b", "task": "Review for bugs and improvements"}, ]

for a in agents: sessions_spawn(task=a["task"], model=a["model"], label=a["label"]) ```

## Direct API

For custom integrations, use the Ollama API directly:

```bash # Chat curl $OLLAMA_HOST/api/chat -d '{ "model": "qwen3:4b", "messages": [{"role": "user", "content": "Hello"}], "stream": false }'

# Generate curl $OLLAMA_HOST/api/generate -d '{ "model": "qwen3:4b", "prompt": "Why is the sky blue?", "stream": false }'

# List models curl $OLLAMA_HOST/api/tags

# Pull model curl $OLLAMA_HOST/api/pull -d '{"name": "phi3:mini"}' ```

## Troubleshooting

**Connection refused?** - Check Ollama is running: `ollama serve` - Verify OLLAMA_HOST is correct - For remote servers, ensure firewall allows port 11434

**Model not loading?** - Check VRAM: larger models may need CPU offload - Try a smaller model first

**Slow responses?** - Model may be running on CPU - Use smaller quantization (e.g., `:7b` instead of `:30b`)

**OpenClaw sub-agent falls back to default model?** - Ensure `ollama:default` auth profile exists in OpenClaw config - Check model path format: `ollama/modelname:tag`