--- title: "gpt4all vs EAGLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nomic-ai-gpt4all-vs-safeailab-eagle" tools: ["nomic-ai-gpt4all", "safeailab-eagle"] --- # gpt4all vs EAGLE *GraphCanon updated Jul 12, 2026* ## Verdict Pick gpt4all when gpt4all is primarily C++; EAGLE is Python; pick EAGLE when eAGLE is primarily Python; gpt4all is C++. [gpt4all](https://nomic.ai/gpt4all) reports 77k GitHub stars, 8.3k forks, and 768 open issues, last pushed May 27, 2025. [EAGLE](https://arxiv.org/pdf/2503.01840) has 2.5k stars, 292 forks, and 101 open issues, last pushed Feb 20, 2026. Figures are from public GitHub metadata via [gpt4all's repository](https://github.com/nomic-ai/gpt4all) and [EAGLE's repository](https://github.com/SafeAILab/EAGLE). | | [gpt4all](/tools/nomic-ai-gpt4all.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Tagline | GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use. | Official Implementation of EAGLE-1 (ICML'24), EAGLE-2 (EMNLP'24), and EAGLE-3 (NeurIPS'25). | | Stars | 77,386 | 2,450 | | Forks | 8,304 | 292 | | Open issues | 768 | 101 | | Language | C++ | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | LLM Frameworks, Inference & Serving | LLM Frameworks, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt4all](/tools/nomic-ai-gpt4all.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 409d | 141d | | Open issues (now) | 768 | 101 | | Full report | [trust report](/tools/nomic-ai-gpt4all/trust.md) | [trust report](/tools/safeailab-eagle/trust.md) | ## Choose when ### Choose gpt4all if… - gpt4all is primarily C++; EAGLE is Python. - License: gpt4all is MIT, EAGLE is Other. - Tags unique to gpt4all: ai-chat, c++. ### Choose EAGLE if… - EAGLE is primarily Python; gpt4all is C++. - License: EAGLE is Other, gpt4all is MIT. - Tags unique to EAGLE: speculative-decoding, python, large-language-models. ## When NOT to use gpt4all - Last GitHub push was 410 days ago (dormant maintenance, May 27, 2025). Validate activity before betting a new project on gpt4all. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use EAGLE - Last GitHub push was 142 days ago (slowing maintenance, Feb 20, 2026). Validate activity before betting a new project on EAGLE. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between gpt4all and EAGLE? gpt4all: GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.. EAGLE: Official Implementation of EAGLE-1 (ICML'24), EAGLE-2 (EMNLP'24), and EAGLE-3 (NeurIPS'25).. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt4all over EAGLE? Choose gpt4all over EAGLE when gpt4all is primarily C++; EAGLE is Python; License: gpt4all is MIT, EAGLE is Other; Tags unique to gpt4all: ai-chat, c++. ### When should I choose EAGLE over gpt4all? Choose EAGLE over gpt4all when EAGLE is primarily Python; gpt4all is C++; License: EAGLE is Other, gpt4all is MIT; Tags unique to EAGLE: speculative-decoding, python, large-language-models. ### When should I avoid gpt4all? Last GitHub push was 410 days ago (dormant maintenance, May 27, 2025). Validate activity before betting a new project on gpt4all. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid EAGLE? Last GitHub push was 142 days ago (slowing maintenance, Feb 20, 2026). Validate activity before betting a new project on EAGLE. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is gpt4all or EAGLE more popular on GitHub? gpt4all has more GitHub stars (77,386 vs 2,450). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt4all and EAGLE open source? Yes - both are open-source projects on GitHub (gpt4all: MIT, EAGLE: Other). ### Where can I find alternatives to gpt4all or EAGLE? GraphCanon lists graph-backed alternatives at [gpt4all alternatives](/tools/nomic-ai-gpt4all/alternatives) and [EAGLE alternatives](/tools/safeailab-eagle/alternatives) ([gpt4all markdown twin](/tools/nomic-ai-gpt4all/alternatives.md), [EAGLE markdown twin](/tools/safeailab-eagle/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/nomic-ai-gpt4all-vs-safeailab-eagle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt4all or EAGLE? gpt4all: Dormant. EAGLE: Slowing. 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 gpt4all and EAGLE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt4all trust report](/tools/nomic-ai-gpt4all/trust); [EAGLE trust report](/tools/safeailab-eagle/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nomic-ai-gpt4all`](/api/graphcanon/graph?tool=nomic-ai-gpt4all) - 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/_