ollama-localManage and use local Ollama models. Use for model management (list/pull/remove), chat/completions, embeddings, and tool-use with local LLMs. Covers OpenClaw sub-agent integration and model selection guidance.
Install via ClawdBot CLI:
clawdbot install Timverhoogt/ollama-localWork with local Ollama models for inference, embeddings, and tool use.
Set your Ollama host (defaults to http://localhost:11434):
export OLLAMA_HOST="http://localhost:11434"
# Or for remote server:
export OLLAMA_HOST="http://192.168.1.100:11434"
# 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"
See references/models.md for full model list and selection guide.
Quick picks:
qwen3:4bqwen2.5-coder:7bllama3.1:8bdeepseek-r1:8bSome local models support function calling. Use ollama_tools.py:
# 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
Spawn local model sub-agents with sessions_spawn:
# 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/
Spawn multiple local agents for collaborative tasks:
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"])
For custom integrations, use the Ollama API directly:
# 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"}'
Connection refused?
ollama serveModel not loading?
Slow responses?
:7b instead of :30b)OpenClaw sub-agent falls back to default model?
ollama:default auth profile exists in OpenClaw configollama/modelname:tagGenerated Feb 24, 2026
Software development teams use the skill to run local models like qwen2.5-coder for automated code review and bug detection. It integrates into CI/CD pipelines via direct API calls, ensuring privacy and reducing reliance on cloud services.
Companies in regulated industries deploy local Ollama models to handle customer inquiries, using chat and tool-use features for tasks like weather lookups. This keeps sensitive data in-house and complies with data sovereignty laws.
Researchers use the skill to generate embeddings from local models for text analysis in projects like literature reviews. It allows offline processing of large datasets without internet dependency, ideal for secure or remote environments.
Design agencies employ the parallel agents feature to spawn multiple local models for brainstorming sessions. Each agent, such as architect or reviewer, contributes specialized insights, enhancing creative workflows without cloud costs.
Organizations set up local models to answer employee questions about internal policies or codebases using chat completions. It reduces IT support overhead and ensures quick, confidential access to proprietary information.
Offer tailored setup and integration of the skill into client systems, such as deploying local models for specific use cases like code review or customer support. Revenue comes from project-based fees and ongoing maintenance contracts.
Provide managed services where clients host Ollama models on-premises or in private clouds, with support for configuration, troubleshooting, and updates. Revenue is generated through subscription plans based on model size and support levels.
Develop courses and certifications for using the skill, covering topics from basic model management to advanced tool-use and sub-agent integration. Revenue streams include course fees, certification exams, and corporate training packages.
š¬ Integration Tip
Ensure the OLLAMA_HOST environment variable is correctly set and test connectivity with a simple list command before deploying complex workflows to avoid common issues like connection refusals.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Gemini CLI for one-shot Q&A, summaries, and generation.
Research any topic from the last 30 days on Reddit + X + Web, synthesize findings, and write copy-paste-ready prompts. Use when the user wants recent social/web research on a topic, asks "what are people saying about X", or wants to learn current best practices. Requires OPENAI_API_KEY and/or XAI_API_KEY for full Reddit+X access, falls back to web search.
Check Antigravity account quotas for Claude and Gemini models. Shows remaining quota and reset times with ban detection.
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates opencla...
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.