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LazyAGI/LazyLLM
Easiest and laziest way for building multi-agent LLMs applications.
LazyLLM is a Python framework for building multi-agent LLM applications with a declarative flow model — you wire up pipelines, parallel branches, and conditionals as composable objects rather than writing glue code by hand. It targets teams who want to prototype quickly, then push the same code to production with fine-tuned local models and multi-node deployment, without rewriting the scaffolding each time. The ambition is higher than LangChain's: it also handles fine-tuning, inference framework selection, and multi-platform scheduling.
The flow primitives (Pipeline, Parallel, Diverter, Loop) are a genuinely clean abstraction — wiring a retriever, reranker, and LLM together in 10 lines without callback hell is a real improvement over what LangChain requires. The automatic fine-tuning framework selection (choosing between collie, peft, vllm, lightllm based on scenario) solves a real pain point; most teams burn a week on this decision. The launcher abstraction — same code runs on a laptop, a Slurm cluster, or SenseCore — is rare in this space and actually useful. The one-click deployment gateway that handles subservice startup ordering is solving a real ops headache in multi-model systems.
The repo has a strong Chinese LLM ecosystem bias: examples default to internlm2-chat-7b, community support is WeChat-first, and several online providers (SenseNova, Kimi) have no global access — if you're not in that ecosystem you're adapting examples rather than running them. There's a C++ csrc layer for sentence splitting that adds a compile-time dependency to what should be a pure Python install; this will bite CI pipelines. The framework is doing too many things at once — fine-tuning, RAG, inference serving, platform scheduling, web UI — and the surface area makes it hard to trust any single subsystem is production-hardened. When a Pipeline breaks across three parallel branches and two launchers, the traceback is going to be nearly useless.