Building a Fully Local Multi-Agent Assistant with OpenClaw and Ollama
A developer on r/openclaw is building a fully local personal AI assistant (think Jarvis) using OpenClaw as the agent framework, Ollama for local inference, and a MINISFORUM AI X1 with Ryzen AI 9 HX470, 96GB RAM, and 2TB NVMe (GPU via Oculink planned). The goal: a proactive multi-agent system that integrates smart home, documents, calendar, health, and communications — all locally, with no sensitive data leaving the infrastructure.
Stack Details
- Agent Framework: OpenClaw
- Inference Engine: Ollama
- Models: qwen3.5:35b-a3b (main), gemma3:4b (home), mistral:7b (life/gmail)
- MCP Servers: Home Assistant, Gmail
- Interface: Telegram Bot, future STT integration into smart home
Sub-Agent Architecture
The main routing agent delegates to specialized sub-agents:
- HA Agent – smart home control and debugging (started)
- Gmail Agent – email management (started)
- Life Agent – calendar, to-do, grocery list management (tbb)
- Health Agent – health and sport data monitoring (tbb)
- Research Agent – web + document RAG (in paperless ngx on NAS) (tbb)
- Dev Agent – coding tasks with separate coding, testing, doc agents (tbb)
Challenges & Open Questions
- Context bloat: Context size grows very large even for simple messages. Configuration likely needs better MCP server scoping and sub-agent tool isolation.
- MCP per-agent scoping: No native way to restrict MCP servers to specific agents yet. Seeking recommended workarounds given an open bug.
- Sub-agent config: Looking for a well-structured
agents.listexample for this multi-agent setup. - Local model selection: Reliable tool-calling with Ollama under 32GB VRAM — any recommendations?
- Inference environment: Considering switching to llama.cpp instead of Ollama if it provides better control.
The developer is open to feedback on approach and configuration. If you've tackled similar multi-agent scaling issues or have MCP scoping workarounds, join the discussion on Reddit.
📖 Read the full source: r/openclaw
👀 See Also
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