LazyLLM

LazyLLM is a low-code development tool for building multi-agent LLMs(large language models) applications. It assists developers in creating complex AI applications at very low costs and enables continuous iterative optimization. LazyLLM offers a convenient workflow for application building and provides numerous standard processes and tools for various stages of the application development process.

The AI application development process based on LazyLLM follows the prototype building -> data feedback -> iterative optimizationworkflow. This means you can quickly build a prototype application using LazyLLM, then analyze bad cases using task-specific data, and subsequently iterate on algorithms and fine-tune models at critical stages of the application to gradually enhance the overall performance.

Features

• Convenient AI Application Assembly Process: Even if you are not familiar with large models, you can still easily assemble AI applications with multiple agents using our built-in data flow and functional modules, just like Lego building.
• One-Click Deployment of Complex Applications: We offer the capability to deploy all modules with a single click. Specifically, during the POC (Proof of Concept) phase, LazyLLM simplifies the deployment process of multi-agent applications through a lightweight gateway mechanism, solving the problem of sequentially starting each submodule service (such as LLM, Embedding, etc.) and configuring URLs, making the entire process smoother and more efficient. In the application release phase, LazyLLM provides the ability to package images with one click, making it easy to utilize Kubernetes' gateway, load balancing, and fault tolerance capabilities.
• Cross-Platform Compatibility: Switch IaaS platforms with one click without modifying code, compatible with bare-metal servers, development machines, Slurm clusters, public clouds, etc. This allows developed applications to be seamlessly migrated to other IaaS platforms, greatly reducing the workload of code modification.
• Support for Grid Search Parameter Optimization: Automatically try different base models, retrieval strategies, and fine-tuning parameters based on user configurations to evaluate and optimize applications. This makes hyperparameter tuning efficient without requiring extensive intrusive modifications to application code, helping users quickly find the best configuration.
• Efficient Model Fine-Tuning: Support fine-tuning models within applications to continuously improve application performance. Automatically select the best fine-tuning framework and model splitting strategy based on the fine-tuning scenario. This not only simplifies the maintenance of model iterations but also allows algorithm researchers to focus more on algorithm and data iteration, without handling tedious engineering tasks.