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Basic Information

Internet of Agents (IoA) is an open-source framework designed to let multiple AI agents collaborate and form autonomous teams to solve complex tasks. It provides an architecture inspired by the internet for connecting heterogeneous agents across environments, tooling to run server, client and frontend components, and examples that integrate agents such as AutoGPT, Open Interpreter and ReAct. The repository includes Dockerfiles and pre-built images, compose files to launch core services and a Milvus vector service for retrieval, scripts to test on an open instruction dataset, and a simple HTTP endpoint to launch goals. The project is accompanied by documentation and a research paper that explain the layered architecture and design goals. The codebase and deployment instructions target developers and researchers who want to build, run or study distributed multi-agent collaboration systems.

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App Details

Features
IoA documents several high-level features: an internet-inspired architecture for connecting agents across environments, autonomous nested team formation so agents can form teams and sub-teams on their own, and heterogeneous agent integration to combine different skill sets. It supports asynchronous task execution and adaptive conversation flow to manage multi-agent dialogues. The repo provides practical deployment assets including Dockerfiles and pre-built images for server, client, frontend and multiple agent images, docker-compose definitions for Milvus and example stacks, build-from-source instructions, test scripts for an Open Instruction dataset, and a simple POST API to launch goals. Documentation and a linked research paper describe the layered architecture and distributed setup.
Use Cases
IoA helps developers and researchers prototype and run collaborative multi-agent systems without building orchestration from scratch. It lowers operational friction by supplying Docker images, compose files, and build instructions for server, client, frontend and agent containers, plus a Milvus service for vector storage. Users can quickly launch demo stacks that combine AutoGPT and Open Interpreter, run provided test scripts, or submit goals programmatically to evaluate team behavior. The framework emphasizes scalability and extensibility so new agent types can be added, and it documents a distributed setup for running agents across devices. The project also cites a research paper and public documentation to support understanding and experimentation.

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