--- title: "jax vs 3D-Mem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jax-ml-jax-vs-umass-embodied-agi-3d-mem" tools: ["jax-ml-jax", "umass-embodied-agi-3d-mem"] --- # jax vs 3D-Mem *GraphCanon updated Jul 11, 2026* ## Verdict Pick jax when license: jax is Apache-2.0, 3D-Mem is MIT; pick 3D-Mem when license: 3D-Mem is MIT, jax is Apache-2.0. [jax](https://docs.jax.dev) reports 36k GitHub stars, 3.7k forks, and 2.5k open issues, last pushed Jul 11, 2026. [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 [jax's repository](https://github.com/jax-ml/jax) and [3D-Mem's repository](https://github.com/UMass-Embodied-AGI/3D-Mem). | | [jax](/tools/jax-ml-jax.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Tagline | Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more | [CVPR 2025] Source codes for the paper "3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning" | | Stars | 35,999 | 264 | | Forks | 3,676 | 17 | | Open issues | 2,495 | 3 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Computer Vision, Evaluation & Observability, Vector Databases | Computer Vision, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [jax](/tools/jax-ml-jax.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 281d | | Open issues (now) | 2.5k | 3 | | Full report | [trust report](/tools/jax-ml-jax/trust.md) | [trust report](/tools/umass-embodied-agi-3d-mem/trust.md) | ## Shared compatibility - **Python**: [jax](/tools/jax-ml-jax.md) - Python runtime; [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) - Python runtime ## Choose when ### Choose jax if… - License: jax is Apache-2.0, 3D-Mem is MIT. - Tags unique to jax: jax. - Also covers Evaluation & Observability. ### Choose 3D-Mem if… - License: 3D-Mem is MIT, jax is Apache-2.0. - Tags unique to 3D-Mem: ai, computer-vision, embodied-ai, spatial-intelligence. - Also covers Model Training. ## When NOT to use jax - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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 jax and 3D-Mem? jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more. 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 jax over 3D-Mem? Choose jax over 3D-Mem when License: jax is Apache-2.0, 3D-Mem is MIT; Tags unique to jax: jax; Also covers Evaluation & Observability. ### When should I choose 3D-Mem over jax? Choose 3D-Mem over jax when License: 3D-Mem is MIT, jax is Apache-2.0; Tags unique to 3D-Mem: ai, computer-vision, embodied-ai, spatial-intelligence; Also covers Model Training. ### When should I avoid jax? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. 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 jax or 3D-Mem more popular on GitHub? jax has more GitHub stars (35,999 vs 264). Stars measure visibility, not whether either tool fits your constraints. ### Are jax and 3D-Mem open source? Yes - both are open-source projects on GitHub (jax: Apache-2.0, 3D-Mem: MIT). ### Where can I find alternatives to jax or 3D-Mem? GraphCanon lists graph-backed alternatives at [jax alternatives](/tools/jax-ml-jax/alternatives) and [3D-Mem alternatives](/tools/umass-embodied-agi-3d-mem/alternatives) ([jax markdown twin](/tools/jax-ml-jax/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/jax-ml-jax-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, jax or 3D-Mem? jax: Very active. 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 jax and 3D-Mem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [jax trust report](/tools/jax-ml-jax/trust); [3D-Mem trust report](/tools/umass-embodied-agi-3d-mem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jax-ml-jax`](/api/graphcanon/graph?tool=jax-ml-jax) - 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/_