--- title: "FlagAI vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/flagai-open-flagai-vs-hiyouga-llamafactory" tools: ["flagai-open-flagai", "hiyouga-llamafactory"] --- # FlagAI vs LlamaFactory *GraphCanon updated Aug 16, 2026* ## Verdict Pick FlagAI if flagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license; pick LlamaFactory if llamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. [FlagAI](https://github.com/FlagAI-Open/FlagAI) reports 3.9k GitHub stars, 416 forks, and 22 open issues, last pushed Jul 13, 2026. [LlamaFactory](https://llamafactory.readthedocs.io) has 74k stars, 9.1k forks, and 1.1k open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [FlagAI's repository](https://github.com/FlagAI-Open/FlagAI) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [FlagAI](/tools/flagai-open-flagai.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Fast, easy-to-use framework for large-scale AI models. | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 3,870 | 74,132 | | Forks | 416 | 9,071 | | Open issues | 22 | 1,113 | | Language | Python | Python | | Adopt for | FlagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license. | LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FlagAI](/tools/flagai-open-flagai.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 33d | 2d | | Open issues (now) | 22 | 1.1k | | Stars delta | +2 (30d) | +803 (30d) | | Open issues delta | 0 (30d) | +39 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/flagai-open-flagai/trust.md) | [trust report](/tools/hiyouga-llamafactory/trust.md) | ## Shared compatibility - **Python**: [FlagAI](/tools/flagai-open-flagai.md) - Python runtime; [LlamaFactory](/tools/hiyouga-llamafactory.md) - Python runtime ## Decision facts: FlagAI - **Adopt for:** FlagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license. ## Decision facts: LlamaFactory - **Adopt for:** LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. ## Choose when ### Choose FlagAI if… - Tags unique to FlagAI: extensible, fast, large-scale models. - FlagAI ships Docker support for self-hosted deployment. - When you prioritize speed and extensibility during the development of large-scale AI models with a focus on easy-to-use interfaces. ### Choose LlamaFactory if… - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (74k vs 3.9k) - visibility, not fit. ## When NOT to use FlagAI - If your project necessitates a deep level of customization that might not be supported by FlagAI's framework. - If you are working with smaller datasets, the overhead provided by FlagAI’s scalability features could be unnecessary and potentially inefficient. ## When NOT to use LlamaFactory - When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. - If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ## Common questions ### What is the difference between FlagAI and LlamaFactory? FlagAI: Fast, easy-to-use framework for large-scale AI models.. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose FlagAI over LlamaFactory? Choose FlagAI over LlamaFactory when Tags unique to FlagAI: extensible, fast, large-scale models; FlagAI ships Docker support for self-hosted deployment; When you prioritize speed and extensibility during the development of large-scale AI models with a focus on easy-to-use interfaces. ### When should I choose LlamaFactory over FlagAI? Choose LlamaFactory over FlagAI when Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (74k vs 3.9k) - visibility, not fit. ### When should I avoid FlagAI? If your project necessitates a deep level of customization that might not be supported by FlagAI's framework. If you are working with smaller datasets, the overhead provided by FlagAI’s scalability features could be unnecessary and potentially inefficient. ### When should I avoid LlamaFactory? When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ### Is FlagAI or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (74,132 vs 3,870). Stars measure visibility, not whether either tool fits your constraints. ### Are FlagAI and LlamaFactory open source? Yes - both are open-source projects on GitHub (FlagAI: Apache-2.0, LlamaFactory: Apache-2.0). ### Where can I find alternatives to FlagAI or LlamaFactory? GraphCanon lists graph-backed alternatives at [FlagAI alternatives](/tools/flagai-open-flagai/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([FlagAI markdown twin](/tools/flagai-open-flagai/alternatives.md), [LlamaFactory markdown twin](/tools/hiyouga-llamafactory/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/flagai-open-flagai-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FlagAI or LlamaFactory? FlagAI: Steady. LlamaFactory: 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 FlagAI and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FlagAI trust report](/tools/flagai-open-flagai/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=flagai-open-flagai`](/api/graphcanon/graph?tool=flagai-open-flagai) - 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/_