Comparison
habitat-lab vs 3D-Mem
Verdict
Pick habitat-lab if habitat-Lab is a Python library for training embodied AI agents in virtual environments through deep and reinforcement learning techniques; pick 3D-Mem if 3D-Mem excels in environments where embodied AI needs spatial intelligence for exploration and reasoning in 3D scenes.
Markdown twin · habitat-lab alternatives · 3D-Mem alternatives
GraphCanon updated Sep 25, 2026
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Trust & integrity
| Signal | habitat-lab | 3D-Mem |
|---|---|---|
| Maintenance | Slowing (136d since push) As of Sep 21, 2026 · github_public_v1 | Slowing (357d since push) As of Sep 25, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 21, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 25, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- habitat-lab
- A modular high-level library to train embodied AI agents
- 3D-Mem
- 3D scene memory for embodied AI exploration and reasoning
Stars
- habitat-lab
- 3.1k
- 3D-Mem
- 277
Forks
- habitat-lab
- 690
- 3D-Mem
- 19
Open issues
- habitat-lab
- 390
- 3D-Mem
- 3
Language
- habitat-lab
- Python
- 3D-Mem
- Python
Adopt for
- habitat-lab
- Habitat-Lab is a Python library for training embodied AI agents in virtual environments through deep and reinforcement learning techniques.
- 3D-Mem
- 3D-Mem excels in environments where embodied AI needs spatial intelligence for exploration and reasoning in 3D scenes.
Persona
- habitat-lab
- -
- 3D-Mem
- -
Runtime
- habitat-lab
- -
- 3D-Mem
- -
License
- habitat-lab
- MIT
- 3D-Mem
- MIT
Last pushed
- habitat-lab
- May 7, 2026
- 3D-Mem
- Oct 2, 2025
Categories
- habitat-lab
- AI Agents, Computer Vision
- 3D-Mem
- Computer Vision
Trust and health
Days since push
- habitat-lab
- 136d
- 3D-Mem
- 357d
Open issues (now)
- habitat-lab
- 390
- 3D-Mem
- 3
Stars delta
- habitat-lab
- +56 (30d)
- 3D-Mem
- +7 (30d)
Open issues delta
- habitat-lab
- +2 (30d)
- 3D-Mem
- 0 (30d)
Full report
- habitat-lab
- Trust report
- 3D-Mem
- Trust report
Shared compatibility
- Python · habitat-lab: Python runtime · 3D-Mem: Python runtime
Choose habitat-lab if…
- Requirements: Min 8 GB RAM; Requires Docker; Python >=3.9 is required along with cmake>=3.14 for installation; For users working on machines equipped with NVIDIA GPUs, nvidia-docker installation is necessary to run the provided Docker containers.
- Tags unique to habitat-lab: computer-vision, deep-learning, reinforcement learning, research.
- Also covers AI Agents.
- habitat-lab ships Docker support for self-hosted deployment.
- Use Habitat-Lab when your project requires the simulation of complex environments for embodied AI tasks, such as navigation and interaction with objects
When NOT to use habitat-lab
- Avoid Habitat-Lab if the computational resources required for running the simulations exceed what is available or feasible in terms of cost
- Do not use Habitat-Lab when the project is solely focused on real-world data and does not necessitate virtual training environments, as setting up such a library might add unnecessary complexity
Choose 3D-Mem if…
- Tags unique to 3D-Mem: embodied-ai, spatial-intelligence.
- Use if your project involves embodied AI systems that require detailed scene understanding in 3D
- Leaner open-issue backlog (3).
When NOT to use 3D-Mem
- Avoid for environments where 2D image analysis suffices over deeper 3D spatial reasoning
- This may not be suitable if real-time performance is more critical than the richness of 3D scene intelligence
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (facebookresearch/habitat-lab) · observed Sep 21, 2026
- GitHub forks (facebookresearch/habitat-lab) · observed Sep 21, 2026
- Last push (facebookresearch/habitat-lab) · observed May 7, 2026
- License file (MIT) · observed Sep 21, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (UMass-Embodied-AGI/3D-Mem) · observed Sep 25, 2026
- GitHub forks (UMass-Embodied-AGI/3D-Mem) · observed Sep 25, 2026
- Last push (UMass-Embodied-AGI/3D-Mem) · observed Oct 2, 2025
- License file (MIT) · observed Sep 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: habitat-lab 3.1k · 3D-Mem 277 (synced Sep 21, 2026).
Common questions
- What is the difference between habitat-lab and 3D-Mem?
- habitat-lab: A modular high-level library to train embodied AI agents. 3D-Mem: 3D scene memory for embodied AI exploration and reasoning. See the comparison table for live GitHub stats and shared categories.
- When should I choose habitat-lab over 3D-Mem?
- Choose habitat-lab over 3D-Mem when Requirements: Min 8 GB RAM; Requires Docker; Python >=3.9 is required along with cmake>=3.14 for installation; For users working on machines equipped with NVIDIA GPUs, nvidia-docker installation is necessary to run the provided Docker containers; Tags unique to habitat-lab: computer-vision, deep-learning, reinforcement learning, research; Also covers AI Agents; habitat-lab ships Docker support for self-hosted deployment; Use Habitat-Lab when your project requires the simulation of complex environments for embodied AI tasks, such as navigation and interaction with objects.
- When should I choose 3D-Mem over habitat-lab?
- Choose 3D-Mem over habitat-lab when Tags unique to 3D-Mem: embodied-ai, spatial-intelligence; Use if your project involves embodied AI systems that require detailed scene understanding in 3D; Leaner open-issue backlog (3).
- When should I avoid habitat-lab?
- Avoid Habitat-Lab if the computational resources required for running the simulations exceed what is available or feasible in terms of cost Do not use Habitat-Lab when the project is solely focused on real-world data and does not necessitate virtual training environments, as setting up such a library might add unnecessary complexity
- When should I avoid 3D-Mem?
- Avoid for environments where 2D image analysis suffices over deeper 3D spatial reasoning This may not be suitable if real-time performance is more critical than the richness of 3D scene intelligence
- Is habitat-lab or 3D-Mem more popular on GitHub?
- habitat-lab has more GitHub stars (3,138 vs 277). Stars measure visibility, not whether either tool fits your constraints.
- Are habitat-lab and 3D-Mem open source?
- Yes - both are open-source projects on GitHub (habitat-lab: MIT, 3D-Mem: MIT).
- Where can I find alternatives to habitat-lab or 3D-Mem?
- GraphCanon lists graph-backed alternatives at habitat-lab alternatives and 3D-Mem alternatives (habitat-lab markdown twin, 3D-Mem markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, habitat-lab or 3D-Mem?
- habitat-lab: Slowing. 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 habitat-lab and 3D-Mem?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: habitat-lab trust report; 3D-Mem trust report.