OpenClaw Setup on Ubuntu UTM VM with LLM API and Ollama Access
OpenClaw Sandboxed Setup with Multi-LLM Access
A developer has documented a working configuration for running OpenClaw in a standalone, sandboxed environment. The setup involves running OpenClaw inside an Ubuntu virtual machine using UTM on an M3 Mac, while maintaining connectivity to LLM services both locally and via external APIs.
Key Configuration Details
The solution provides OpenClaw with access only to materials available inside the Ubuntu VM, creating a controlled environment. Meanwhile, Ollama runs natively on macOS and remains accessible to OpenClaw within the Ubuntu VM on the same machine.
OpenClaw in this configuration can utilize multiple LLM APIs including:
- Gemini
- Claude
- DeepSeek
Available Resources
All sample configuration files are available at: https://github.com/parimalbajpai/openclaw/tree/main
OpenClaw-specific tips and solutions are documented in notes.txt, which includes:
- Workarounds for Ubuntu/ARM not having Chrome
- Google Workspace access via gog and gogcli
Additional Ollama configuration tips are available in separate notes.txt.
This type of sandboxed setup is particularly useful for developers who want to test AI coding agents in isolated environments while maintaining access to both local and cloud-based LLM services, allowing for controlled experimentation with different model configurations.
📖 Read the full source: r/openclaw
👀 See Also
MCP-Loci: Local Persistent Memory Server for Claude and MCP-Compatible AI
MCP-Loci is a persistent memory server that solves Claude's session-based memory limitation with five tools: remember, recall, forget, synthesize, and health. It uses hybrid BM25 keyword matching and semantic embeddings for accurate recall without requiring API keys.
Tendr Skill: Deterministic CLI Operations for Agent Memory Management
Tendr Skill is an Agent Skill that separates reasoning from execution for structured long-term memory, allowing agents to decide what needs changing while a CLI tool handles structural operations deterministically. It supports [[wikilinks]] and explicit semantic hierarchies across files.
MemAware benchmark shows RAG-based agent memory fails on implicit context retrieval
The MemAware benchmark tests whether AI agents can surface relevant past context when users don't explicitly ask for it, revealing that current memory systems score only 2.8% accuracy on hard implicit queries versus 0.8% with no memory.
SIDJUA V1.0: Self-Hosted Governance Platform for AI Agents
SIDJUA V1.0 is a free, self-hosted governance platform for AI agents that runs on Docker, including Raspberry Pi. It provides mandatory checkpoints for agent tasks, encrypted credential storage, network isolation, and granular budget controls.