--- title: "LlamaFactory vs harness-books" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-wquguru-harness-books" tools: ["hiyouga-llamafactory", "wquguru-harness-books"] --- # LlamaFactory vs harness-books *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; pick harness-books when tags unique to harness-books: agentic-ai, ai-agents, ai-engineering, claude-code. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [harness-books](http://harness-books.agentway.dev/) has 2.6k stars, 308 forks, and 5 open issues, last pushed Apr 19, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [harness-books's repository](https://github.com/wquguru/harness-books). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [harness-books](/tools/wquguru-harness-books.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | 📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev | | Stars | 73,157 | 2,618 | | Forks | 8,937 | 308 | | Open issues | 1,067 | 5 | | 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 | - | | 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) | [harness-books](/tools/wquguru-harness-books.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 83d | | Open issues (now) | 1.1k | 5 | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/wquguru-harness-books/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: 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 (73k vs 2.6k) - visibility, not fit. ### Choose harness-books if… - Tags unique to harness-books: agentic-ai, ai-agents, ai-engineering, claude-code. - Also covers AI Agents. - Leaner open-issue backlog (5). ## 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 harness-books - 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 harness-books? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. harness-books: 📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over harness-books? Choose LlamaFactory over harness-books 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 (73k vs 2.6k) - visibility, not fit. ### When should I choose harness-books over LlamaFactory? Choose harness-books over LlamaFactory when Tags unique to harness-books: agentic-ai, ai-agents, ai-engineering, claude-code; Also covers AI Agents; Leaner open-issue backlog (5). ### 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 harness-books? 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 harness-books more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 2,618). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and harness-books open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LlamaFactory or harness-books? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [harness-books alternatives](/tools/wquguru-harness-books/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [harness-books markdown twin](/tools/wquguru-harness-books/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-wquguru-harness-books.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or harness-books? LlamaFactory: Very active. harness-books: Steady. 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 harness-books? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [harness-books trust report](/tools/wquguru-harness-books/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/_