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LazyLLM

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Easiest and laziest way for building multi-agent LLMs applications.

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OSS

LazyLLM

Added 1 June 2026

#agents #ai-agent #data #deep-learning #documentation-tool #finetuning #framework #knowlege-graph

Overview

LazyLLM is an open-source Python framework for building multi-agent applications powered by large language models. It abstracts orchestration and communication between agents, aiming to reduce boilerplate code and development time.

Best for

Best for
Developers seeking a low-friction way to build and test multi-agent LLM applications

Use cases

  • Rapidly prototype multi-agent chatbots or assistants
  • Coordinate multiple LLM calls for complex reasoning workflows
  • Experiment with agent roles and message passing in research projects

Notes

LazyLLM is an open-source Python framework for building multi-agent applications powered by large language models. It abstracts orchestration and communication between agents, aiming to reduce boilerplate code and development time.

3,839 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Rapidly prototype multi-agent chatbots or assistants
  • Coordinate multiple LLM calls for complex reasoning workflows
  • Experiment with agent roles and message passing in research projects

Pros

  • Simplifies multi-agent setup with minimal code
  • Active open-source community with 3800+ stars
  • Fully Python-based, easy to integrate with existing Python projects

Cons

  • Limited to Python ecosystem (no direct support for other languages)
  • May lack fine-grained control for advanced agent behaviors
  • Documentation and stability depend on evolving community contributions

Indexed from awesome-llm and enriched against its public facts.

Pros

  • Simplifies multi-agent setup with minimal code
  • Active open-source community with 3800+ stars
  • Fully Python-based, easy to integrate with existing Python projects

Cons

  • Limited to Python ecosystem (no direct support for other languages)
  • May lack fine-grained control for advanced agent behaviors
  • Documentation and stability depend on evolving community contributions
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