--- title: "LlamaFactory vs lagent" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-internlm-lagent" tools: ["hiyouga-llamafactory", "internlm-lagent"] --- # LlamaFactory vs lagent *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when tags unique to LlamaFactory: ai, deepseek, fine-tuning, gemma; pick lagent when tags unique to lagent: llm, python, transformers. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [lagent](https://github.com/InternLM/lagent) has 2.3k stars, 236 forks, and 23 open issues, last pushed Jul 6, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [lagent's repository](https://github.com/InternLM/lagent). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [lagent](/tools/internlm-lagent.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | A lightweight framework for building LLM-based agents | | Stars | 73,157 | 2,268 | | Forks | 8,937 | 236 | | Open issues | 1,067 | 23 | | Language | Python | Python | | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [lagent](/tools/internlm-lagent.md) | | --- | --- | --- | | Days since push | 0d | 5d | | Open issues (now) | 1.1k | 23 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/internlm-lagent/trust.md) | ## 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 LlamaFactory if… - Tags unique to LlamaFactory: ai, deepseek, fine-tuning, gemma. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. - More GitHub stars (73k vs 2.3k) - visibility, not fit. ### Choose lagent if… - Tags unique to lagent: llm, python, transformers. - Also covers AI Agents. - Leaner open-issue backlog (23). ## 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 ## When NOT to use lagent - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between LlamaFactory and lagent? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. lagent: A lightweight framework for building LLM-based agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over lagent? Choose LlamaFactory over lagent when Tags unique to LlamaFactory: ai, deepseek, fine-tuning, gemma; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA; More GitHub stars (73k vs 2.3k) - visibility, not fit. ### When should I choose lagent over LlamaFactory? Choose lagent over LlamaFactory when Tags unique to lagent: llm, python, transformers; Also covers AI Agents; Leaner open-issue backlog (23). ### 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 ### When should I avoid lagent? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is LlamaFactory or lagent more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 2,268). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and lagent open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, lagent: Apache-2.0). ### Where can I find alternatives to LlamaFactory or lagent? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [lagent alternatives](/tools/internlm-lagent/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [lagent markdown twin](/tools/internlm-lagent/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/hiyouga-llamafactory-vs-internlm-lagent.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or lagent? LlamaFactory: Very active. lagent: 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 LlamaFactory and lagent? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [lagent trust report](/tools/internlm-lagent/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - 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/_