--- title: "waggle-dance vs LazyLLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-merge-waggle-dance-vs-lazyagi-lazyllm" tools: ["agi-merge-waggle-dance", "lazyagi-lazyllm"] --- # waggle-dance vs LazyLLM *GraphCanon updated Aug 15, 2026* ## Verdict Pick waggle-dance if waggle-dance is an AI-driven knowledge work automation tool built in TypeScript with experimental concurrent agent execution for faster task resolution; pick LazyLLM if critical facts for LazyLLM. [waggle-dance](https://waggledance.ai) reports 171 GitHub stars, 12 forks, and 0 open issues, last pushed Dec 17, 2023. [LazyLLM](https://docs.lazyllm.ai/) has 3.9k stars, 404 forks, and 41 open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [waggle-dance's repository](https://github.com/agi-merge/waggle-dance) and [LazyLLM's repository](https://github.com/LazyAGI/LazyLLM). | | [waggle-dance](/tools/agi-merge-waggle-dance.md) | [LazyLLM](/tools/lazyagi-lazyllm.md) | | --- | --- | --- | | Tagline | Knowledge work automation with AI agents | Easiest and laziest way for building multi-agent LLMs applications. | | Stars | 171 | 3,866 | | Forks | 12 | 404 | | Open issues | 0 | 41 | | Language | TypeScript | Python | | Adopt for | Waggle-dance is an AI-driven knowledge work automation tool built in TypeScript with experimental concurrent agent execution for faster task resolution. | Critical facts for LazyLLM | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Model Training | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [waggle-dance](/tools/agi-merge-waggle-dance.md) | [LazyLLM](/tools/lazyagi-lazyllm.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 971d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 0 | 41 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/agi-merge-waggle-dance/trust.md) | [trust report](/tools/lazyagi-lazyllm/trust.md) | ## Shared compatibility - **Python**: [waggle-dance](/tools/agi-merge-waggle-dance.md) - Python runtime; [LazyLLM](/tools/lazyagi-lazyllm.md) - Python runtime ## Decision facts: waggle-dance - **Adopt for:** Waggle-dance is an AI-driven knowledge work automation tool built in TypeScript with experimental concurrent agent execution for faster task resolution. ## Decision facts: LazyLLM - **Pricing:** freemium - LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects. - **Requirements:** Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary. - **Adopt for:** Critical facts for LazyLLM ## Choose when ### Choose waggle-dance if… - waggle-dance is primarily TypeScript; LazyLLM is Python. - License: waggle-dance is MIT, LazyLLM is Apache-2.0. - Tags unique to waggle-dance: agent, autogpt, langchain, weaviate. - waggle-dance ships Docker support for self-hosted deployment. - You need a system that prioritizes speed and concurrency in executing tasks ### Choose LazyLLM if… - LazyLLM is primarily Python; waggle-dance is TypeScript. - License: LazyLLM is Apache-2.0, waggle-dance is MIT. - Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects.. - Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary.. - Tags unique to LazyLLM: agents, ai-agent, deep-learning, framework. - - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ## When NOT to use waggle-dance - Your project demands high stability over experimental features - You require extensive customization of agent execution behavior beyond what Waggle-dance offers initially ## When NOT to use LazyLLM - - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools. - - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable. ## Common questions ### What is the difference between waggle-dance and LazyLLM? waggle-dance: Knowledge work automation with AI agents. LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose waggle-dance over LazyLLM? Choose waggle-dance over LazyLLM when waggle-dance is primarily TypeScript; LazyLLM is Python; License: waggle-dance is MIT, LazyLLM is Apache-2.0; Tags unique to waggle-dance: agent, autogpt, langchain, weaviate; waggle-dance ships Docker support for self-hosted deployment; You need a system that prioritizes speed and concurrency in executing tasks. ### When should I choose LazyLLM over waggle-dance? Choose LazyLLM over waggle-dance when LazyLLM is primarily Python; waggle-dance is TypeScript; License: LazyLLM is Apache-2.0, waggle-dance is MIT; Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects.; Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary.; Tags unique to LazyLLM: agents, ai-agent, deep-learning, framework; - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ### When should I avoid waggle-dance? Your project demands high stability over experimental features You require extensive customization of agent execution behavior beyond what Waggle-dance offers initially ### When should I avoid LazyLLM? - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools. - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable. ### Is waggle-dance or LazyLLM more popular on GitHub? LazyLLM has more GitHub stars (3,866 vs 171). Stars measure visibility, not whether either tool fits your constraints. ### Are waggle-dance and LazyLLM open source? Yes - both are open-source projects on GitHub (waggle-dance: MIT, LazyLLM: Apache-2.0). ### Where can I find alternatives to waggle-dance or LazyLLM? GraphCanon lists graph-backed alternatives at [waggle-dance alternatives](/tools/agi-merge-waggle-dance/alternatives) and [LazyLLM alternatives](/tools/lazyagi-lazyllm/alternatives) ([waggle-dance markdown twin](/tools/agi-merge-waggle-dance/alternatives.md), [LazyLLM markdown twin](/tools/lazyagi-lazyllm/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/agi-merge-waggle-dance-vs-lazyagi-lazyllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, waggle-dance or LazyLLM? waggle-dance: Archived. LazyLLM: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for waggle-dance and LazyLLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [waggle-dance trust report](/tools/agi-merge-waggle-dance/trust); [LazyLLM trust report](/tools/lazyagi-lazyllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-merge-waggle-dance`](/api/graphcanon/graph?tool=agi-merge-waggle-dance) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_