AgentLego is an open-source library designed to enhance Large Language Model (LLM) agents by providing a rich set of versatile tool APIs. It targets developers building LLM-powered agents, offering multimodal capabilities and simplifying tool integration. The library aims to extend agent functionality beyond text processing into areas like visual perception, image generation, and speech processing.

How It Works

AgentLego provides a flexible tool interface that allows users to easily extend custom tools with arbitrary argument and output types. It supports integration with popular LLM agent frameworks such as LangChain, Transformers Agents, and Lagent. The library also enables tool serving and remote access, which is particularly beneficial for tools requiring significant computational resources like large machine learning models or specific hardware environments (e.g., GPU, CUDA).

Quick Start & Requirements

• Primary install: pip install agentlego
• Tool-specific dependencies may require additional installations (e.g., pip install -U openmim, mim install -U mmpretrain for image tools).
• Official documentation: https://agentlego.readthedocs.io/en/latest/
• Demo: https://openxlab.org.cn/apps/detail/mzr1996/AgentLego

Highlighted Details

• Supports a wide range of tools including image description, OCR, VQA, object detection, segmentation, text-to-image generation, and speech processing.
• Includes advanced capabilities like ControlNet for image generation based on edge maps, depth, or pose, and ImageBind for cross-modal generation.
• Offers tools for image editing such as expansion, object removal, and replacement.
• Facilitates direct tool usage via a simple Python API for testing and integration.

Maintenance & Community

The project is developed by InternLM. Further community and roadmap information is not explicitly detailed in the README.

Licensing & Compatibility

• License: Apache 2.0.
• Users must also comply with the licenses of the underlying models used by the tools.

Limitations & Caveats

Some tools require specific dependencies that need manual installation, potentially increasing setup complexity. The README does not detail specific hardware requirements beyond mentioning GPU/CUDA support for certain tools.