JARVIS - AI Agent Frameworks Tool

Overview

JARVIS is an open-source agent framework from Microsoft that connects large language models with specialized expert models from the broader machine-learning community. The project focuses on orchestration: high-level task planning, automated model selection, and coordinated execution across multiple model types and tools. JARVIS exposes multiple interaction surfaces — a command-line interface, a Gradio demo, and web APIs — making it adaptable for research prototypes, developer experimentation, and production integrations. According to the GitHub repository, JARVIS is designed to be deployment-agnostic: it supports local setups and cloud-hosted deployments and emphasizes modular connectors so teams can plug in third-party models, tool chains, or custom components. The repository includes examples and demo flows that illustrate chaining LLM reasoning with domain-specific expert models, enabling workflows such as multimodal processing, code generation with verification, and data extraction that use model orchestration rather than single-model approaches.

Installation

Install via docker:

git clone https://github.com/microsoft/JARVIS.git

cd JARVIS

docker build -t jarvis .

docker run -p 8080:8080 --env-file .env jarvis

Key Features

• Orchestrates multi-model workflows: plans tasks and dispatches sub-tasks to LLMs and expert models.
• Multiple interfaces: CLI, Gradio demo, and REST web APIs for interactive and programmatic use.
• Model selection: dynamically routes requests to specialized models based on task and capability.
• Cloud and local deployment: modular connectors for running components locally or in cloud environments.
• Extensible adapters: integrate third-party models and tools for domain-specific pipelines.

Community

JARVIS is hosted on GitHub where development, examples, and issue tracking are centralized. The repository contains demo apps (including a Gradio demo), contribution guidelines, and example integrations to help adopters reproduce and extend orchestration flows. Community engagement primarily occurs via GitHub Issues, pull requests, and the project’s example directories; contributors can add connectors, example workflows, and bug fixes. For the most current status, open issues, and contribution instructions, consult the repository README and issue tracker on GitHub.

Key Information

• Category: Agent Frameworks
• Type: AI Agent Frameworks Tool