--- title: "LazyLLM vs agentflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lazyagi-lazyllm-vs-simonmesmith-agentflow" tools: ["lazyagi-lazyllm", "simonmesmith-agentflow"] --- # LazyLLM vs agentflow *GraphCanon updated Aug 16, 2026* ## Verdict Pick LazyLLM if critical facts for LazyLLM; pick agentflow if agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. [LazyLLM](https://docs.lazyllm.ai/) reports 3.9k GitHub stars, 404 forks, and 41 open issues, last pushed Aug 7, 2026. [agentflow](https://github.com/simonmesmith/agentflow) has 320 stars, 27 forks, and 13 open issues, last pushed Aug 11, 2023. Figures are from public GitHub metadata via [LazyLLM's repository](https://github.com/LazyAGI/LazyLLM) and [agentflow's repository](https://github.com/simonmesmith/agentflow). | | [LazyLLM](/tools/lazyagi-lazyllm.md) | [agentflow](/tools/simonmesmith-agentflow.md) | | --- | --- | --- | | Tagline | Easiest and laziest way for building multi-agent LLMs applications. | Complex LLM Workflows from Simple JSON | | Stars | 3,866 | 320 | | Forks | 404 | 27 | | Open issues | 41 | 13 | | Language | Python | Python | | Adopt for | Critical facts for LazyLLM | Agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Model Training | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LazyLLM](/tools/lazyagi-lazyllm.md) | [agentflow](/tools/simonmesmith-agentflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1100d | | Open issues (now) | 41 | 13 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/lazyagi-lazyllm/trust.md) | [trust report](/tools/simonmesmith-agentflow/trust.md) | ## Shared compatibility - **Python**: [LazyLLM](/tools/lazyagi-lazyllm.md) - Python runtime; [agentflow](/tools/simonmesmith-agentflow.md) - Python runtime ## 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 ## Decision facts: agentflow - **Adopt for:** Agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. ## Choose when ### Choose LazyLLM if… - License: LazyLLM is Apache-2.0, agentflow 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. - Also covers Model Training. - - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ### Choose agentflow if… - License: agentflow is MIT, LazyLLM is Apache-2.0. - Tags unique to agentflow: json, large language models, python, workflow-management. - Also covers LLM Frameworks. - When you need to rapidly prototype LLM workflows with minimal coding via JSON configs ## 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. ## When NOT to use agentflow - Avoid if requiring advanced customization that goes beyond basic JSON configurations - Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution ## Common questions ### What is the difference between LazyLLM and agentflow? LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. agentflow: Complex LLM Workflows from Simple JSON. See the comparison table for live GitHub stats and shared categories. ### When should I choose LazyLLM over agentflow? Choose LazyLLM over agentflow when License: LazyLLM is Apache-2.0, agentflow 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; Also covers Model Training; - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ### When should I choose agentflow over LazyLLM? Choose agentflow over LazyLLM when License: agentflow is MIT, LazyLLM is Apache-2.0; Tags unique to agentflow: json, large language models, python, workflow-management; Also covers LLM Frameworks; When you need to rapidly prototype LLM workflows with minimal coding via JSON configs. ### 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. ### When should I avoid agentflow? Avoid if requiring advanced customization that goes beyond basic JSON configurations Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution ### Is LazyLLM or agentflow more popular on GitHub? LazyLLM has more GitHub stars (3,866 vs 320). Stars measure visibility, not whether either tool fits your constraints. ### Are LazyLLM and agentflow open source? Yes - both are open-source projects on GitHub (LazyLLM: Apache-2.0, agentflow: MIT). ### Where can I find alternatives to LazyLLM or agentflow? GraphCanon lists graph-backed alternatives at [LazyLLM alternatives](/tools/lazyagi-lazyllm/alternatives) and [agentflow alternatives](/tools/simonmesmith-agentflow/alternatives) ([LazyLLM markdown twin](/tools/lazyagi-lazyllm/alternatives.md), [agentflow markdown twin](/tools/simonmesmith-agentflow/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/lazyagi-lazyllm-vs-simonmesmith-agentflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LazyLLM or agentflow? LazyLLM: Very active. agentflow: Dormant. 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 LazyLLM and agentflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LazyLLM trust report](/tools/lazyagi-lazyllm/trust); [agentflow trust report](/tools/simonmesmith-agentflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lazyagi-lazyllm`](/api/graphcanon/graph?tool=lazyagi-lazyllm) - 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/_