HuggingGPT, also known as JARVIS, is an AI agent developed by Microsoft Research that uses large language models (LLMs) to orchestrate and leverage a wide range of expert models available on the Hugging Face Hub. It aims to solve complex AI tasks that require multi-modal understanding and multi-step execution by intelligently selecting and combining specialized models.

How much does HuggingGPT cost?

HuggingGPT is a free and open-source project developed by Microsoft Research. While it runs on the Hugging Face platform, which offers paid services for compute and storage, the HuggingGPT agent itself does not have a direct cost.

What can HuggingGPT do?

HuggingGPT can decompose complex AI tasks into manageable sub-tasks, select appropriate specialized models from the Hugging Face Hub to address each sub-task, execute these models, and integrate their outputs to achieve the overall goal. It supports multi-modal tasks involving text, image, audio, video, and 3D data.

Is HuggingGPT open source?

Yes, HuggingGPT (the JARVIS project) is open source, with its code available on GitHub under an MIT license. This allows developers and researchers to inspect, modify, and extend its capabilities.

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AI Agents

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HuggingGPT

Open Source(MIT)

Open source

Multi-modal AI

LLM orchestration

Best for researchers

Developer-focused

Orchestrate large language models with the vast ML community for complex AI task execution.

Workflow

Coding

GPT-3.5

OPEN_SOURCE

ADVANCED Complexity

HYBRID

Last updated: May 13, 2026

huggingface.co

Quick Answer

HuggingGPT is an AI agent developed by Microsoft Research that orchestrates large language models with specialized models from the Hugging Face ecosystem to solve complex, multi-modal AI tasks. It enables LLMs to act as intelligent controllers, leveraging diverse tools for multi-step problem-solving. Best for researchers and developers building advanced AI workflows. It is a free and open-source project.

Decision Intelligence

Best For

Researchers exploring multi-modal AI task automation

Developers building complex AI applications requiring diverse model capabilities

Experimenting with LLM orchestration of specialized ML models

Not Ideal For

Users seeking a simple, no-code AI solution

Single-task, straightforward AI problems that don't require model orchestration

Applications requiring real-time, low-latency responses without careful optimization

Fit Score

Complexity

ADVANCED

Company Size

SOLO

SMALL

MEDIUM

ENTERPRISE

Team Size

INDIVIDUAL

SMALL_TEAM

LARGE_TEAM

Skill Level

EXPERT

About HuggingGPT

HuggingGPT, powered by Microsoft's JARVIS system, is an AI agent that orchestrates large language models (LLMs) with various expert models from the Hugging Face ecosystem to solve complex AI tasks. It leverages the strengths of different models for multi-modal and multi-step problem-solving. This agent acts as a central coordinator, enabling LLMs to utilize specialized tools and models for diverse applications.

AI-extracted from public sources. May contain errors. Methodology · Are you HuggingGPT? Claim listing

What Makes HuggingGPT Unique

HuggingGPT uniquely acts as an LLM-based controller that leverages the Hugging Face ecosystem, allowing a large language model to select and execute specialized models (tools) for multi-modal and multi-step AI task completion.

Capabilities

Orchestrates large language models with specialized ML models

Decomposes complex tasks into sub-tasks

Selects appropriate expert models (tools) for sub-tasks

Executes models and processes their outputs

Supports multi-modal inputs and outputs (text, image, audio, video, 3D)

Facilitates multi-step problem-solving

Integrates with Hugging Face Hub models and datasets

Task Categories

Coding

Research

Workflow Orchestration

Content Creation

Data Analysis

Pros

+Access to a vast array of specialized models on Hugging Face
+Enables LLMs to perform multi-modal and multi-step tasks
+Open-source project for transparency and customization
+Facilitates complex AI workflow automation

Limitations

-Requires familiarity with ML models and the Hugging Face ecosystem
-Performance dependent on the capabilities and compatibility of integrated models
-May require significant computational resources for complex tasks
-Primarily developer-focused with no direct end-user interface