JARVIS (HuggingGPT)
microsoft/JARVIS
Microsoft research system where an LLM plans a job and calls specialist Hugging Face models to execute multi-step AI tasks.
GitHub repository
Overview
JARVIS / HuggingGPT is the classic “LLM as controller + expert models as workers” demo. ChatGPT (or another LLM) plans stages; models from Hugging Face do vision, speech, etc. Repo (copy/paste): https://github.com/microsoft/JARVIS
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Link
Installation guide
Not installed into LimeDock.
1. Copy: https://github.com/microsoft/JARVIS 2. Read the README — start with lite / Hugging Face Space before full local deploy. 3. Configure OpenAI/Azure + model endpoints as documented. 4. Try CLI or Gradio demo modes.
How to use it
**Simple example** You ask: “Describe this image, then generate a captioned audio summary.”
1. JARVIS plans: vision model → language model → speech model. 2. Each expert model runs its stage. 3. Results chain back into one answer. 4. You learn the pattern: planner LLM + tool/model executors.
Example prompts
- “Explain HuggingGPT’s four stages in plain English.”
- “What’s the lightest way to try JARVIS without deploying models locally?”
- “How is JARVIS different from a single-model chat assistant?”
Use cases and examples
- Learning multi-model agent architecture
- Research demos of task planning + model selection
- Inspiration before building your own tool-using agent
Prerequisites
- API keys for the LLM controller
- Hugging Face access for expert models (depending on mode)
- Python environment if running locally
Tips
- Treat it as research/education — not a turnkey SaaS ops stack.
- Lite config first; full local model deploy is heavy.