--- title: "caffe vs 3D-Mem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bvlc-caffe-vs-umass-embodied-agi-3d-mem" tools: ["bvlc-caffe", "umass-embodied-agi-3d-mem"] --- # caffe vs 3D-Mem *GraphCanon updated Jul 11, 2026* ## Verdict Pick caffe when caffe is primarily C++; 3D-Mem is Python; pick 3D-Mem when 3D-Mem is primarily Python; caffe is C++. [caffe](http://caffe.berkeleyvision.org/) reports 35k GitHub stars, 18k forks, and 1.2k open issues, last pushed Jul 31, 2024. [3D-Mem](https://umass-embodied-agi.github.io/3D-Mem/) has 264 stars, 17 forks, and 3 open issues, last pushed Oct 2, 2025. Figures are from public GitHub metadata via [caffe's repository](https://github.com/BVLC/caffe) and [3D-Mem's repository](https://github.com/UMass-Embodied-AGI/3D-Mem). | | [caffe](/tools/bvlc-caffe.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Tagline | Caffe: a fast open framework for deep learning. | [CVPR 2025] Source codes for the paper "3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning" | | Stars | 34,574 | 264 | | Forks | 18,458 | 17 | | Open issues | 1,209 | 3 | | Language | C++ | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Computer Vision, Vector Databases | Computer Vision, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caffe](/tools/bvlc-caffe.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 710d | 281d | | Open issues (now) | 1.2k | 3 | | Full report | [trust report](/tools/bvlc-caffe/trust.md) | [trust report](/tools/umass-embodied-agi-3d-mem/trust.md) | ## Choose when ### Choose caffe if… - caffe is primarily C++; 3D-Mem is Python. - License: caffe is Other, 3D-Mem is MIT. - Tags unique to caffe: c++, deep-learning, machine-learning, vision. ### Choose 3D-Mem if… - 3D-Mem is primarily Python; caffe is C++. - License: 3D-Mem is MIT, caffe is Other. - Tags unique to 3D-Mem: ai, computer-vision, embodied-ai, python. - Also covers Model Training. ## When NOT to use caffe - Last GitHub push was 710 days ago (dormant maintenance, Jul 31, 2024). Validate activity before betting a new project on caffe. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use 3D-Mem - Last GitHub push was 282 days ago (slowing maintenance, Oct 2, 2025). Validate activity before betting a new project on 3D-Mem. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between caffe and 3D-Mem? caffe: Caffe: a fast open framework for deep learning.. 3D-Mem: [CVPR 2025] Source codes for the paper "3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning". See the comparison table for live GitHub stats and shared categories. ### When should I choose caffe over 3D-Mem? Choose caffe over 3D-Mem when caffe is primarily C++; 3D-Mem is Python; License: caffe is Other, 3D-Mem is MIT; Tags unique to caffe: c++, deep-learning, machine-learning, vision. ### When should I choose 3D-Mem over caffe? Choose 3D-Mem over caffe when 3D-Mem is primarily Python; caffe is C++; License: 3D-Mem is MIT, caffe is Other; Tags unique to 3D-Mem: ai, computer-vision, embodied-ai, python; Also covers Model Training. ### When should I avoid caffe? Last GitHub push was 710 days ago (dormant maintenance, Jul 31, 2024). Validate activity before betting a new project on caffe. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid 3D-Mem? Last GitHub push was 282 days ago (slowing maintenance, Oct 2, 2025). Validate activity before betting a new project on 3D-Mem. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is caffe or 3D-Mem more popular on GitHub? caffe has more GitHub stars (34,574 vs 264). Stars measure visibility, not whether either tool fits your constraints. ### Are caffe and 3D-Mem open source? Yes - both are open-source projects on GitHub (caffe: Other, 3D-Mem: MIT). ### Where can I find alternatives to caffe or 3D-Mem? GraphCanon lists graph-backed alternatives at [caffe alternatives](/tools/bvlc-caffe/alternatives) and [3D-Mem alternatives](/tools/umass-embodied-agi-3d-mem/alternatives) ([caffe markdown twin](/tools/bvlc-caffe/alternatives.md), [3D-Mem markdown twin](/tools/umass-embodied-agi-3d-mem/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/bvlc-caffe-vs-umass-embodied-agi-3d-mem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caffe or 3D-Mem? caffe: Dormant. 3D-Mem: 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 caffe and 3D-Mem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caffe trust report](/tools/bvlc-caffe/trust); [3D-Mem trust report](/tools/umass-embodied-agi-3d-mem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bvlc-caffe`](/api/graphcanon/graph?tool=bvlc-caffe) - 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/_