🗞 LazyLLM looks like a super simple low-code open-source framework for building multi-agent applications

💡LazyLLM supports a variety of applications that leverage multi-agent systems and LLMs like RAG, fine-tuning, content-generation etc.

and defines workflows such as pipeline, parallel, diverter, if, switch, and loop etc.

⛳ These are the features that struck to me most:

• 👉 LazyLLM allows developers to assemble AI applications easily using pre-built modules and data flows, even if they are not familiar with large models.
• 👉 The framework offers a one-click deployment feature which can be particularly useful during the Proof of Concept phase, as it manages the sequential start-up and configuration of various services like LLMs and embeddings.
• 👉 It supports deployment across different infrastructure platforms such as bare-metal servers, Slurm clusters, and public clouds.
• 👉 It has a built-in support for grid search and allows automatic exploration of different model configurations, retrieval strategies, and fine-tuning parameters to optimize application performance

The features aren’t flashy or anything new, but LazyLLM is built in a straightforward way.

The repository is pretty new and actively being developed, but it’s nice to see so many low-code approaches being built. It helps developers from diverse backgrounds quickly build a prototype

Github 👉 https://github.com/LazyAGI/LazyLLM