LazyLLM
ActiveDescription
LazyLLM is a lightweight multi-agent LLM application framework offering the easiest way to build multi-agent LLM apps, with built-in RAG, knowledge graph, fine-tuning, and integration with LangChain and LlamaIndex ecosystems.
Key Features
- Low-code multi-agent application assembly with built-in data flow and functional modules like Lego blocks
- One-click deployment with lightweight gateway for POC and Kubernetes packaging for production
- Cross-platform compatibility across bare-metal, Slurm clusters, and public clouds without code changes
- Unified experience for online and local models, inference frameworks, fine-tuning, and vector databases
- Built-in RAG pipeline with document splitting, retrieval, reranking, and intent classification
Use Cases
💡 Rapid prototyping of multi-agent AI applications with iterative data feedback and optimization
💡 Building RAG chatbots with document ingestion, retrieval, and reranking in a few lines of code
💡 Deploying AI applications across different cloud providers with zero code modification
💡 Fine-tuning models within applications to continuously improve domain-specific performance
Categories
Quick Start
pip install lazyllm. Quick chatbot: import lazyllm; chat = lazyllm.OnlineChatModule(); lazyllm.WebModule(chat).start().wait(). For local model: lazyllm.TrainableModule('internlm2-chat-7b'). For CLI: lazyllm run chatbot --model=internlm2-chat-7b. See docs at https://docs.lazyllm.ai/.