AI Habitat
AI Habitat is an open-source simulation platform from Meta AI Research for embodied AI research. It provides high-performance 3D simulated environments for training and evaluating AI agents on navigation, manipulation, and human-robot collaboration tasks. Habitat-Sim delivers 10,000+ FPS simulation and Habitat-Lab provides a modular library for defining tasks, training agents, and running benchmarks.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles AI Habitat the way a machine reads it — 52 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — AI Habitat scores 39.5/100 (thin), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile AI Habitat
Each block below is one kind of artifact we hold for AI Habitat. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
AI Habitat
AI Habitat simulation framework for embodied AI research, including Habitat-Sim (high-performance 3D simulator) and Habitat-Lab (modular training library). Supports navigation, ...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Ai Habitat Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Ai Habitat Finops
FINOPSFeatures 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
High-Performance Simulation
Habitat-Sim achieves 10,000+ FPS on a single GPU and 8,000+ steps/second for robot simulation, enabling fast RL training.
Photorealistic 3D Environments
Supports HM3D, MatterPort3D, Gibson, Replica, and HSSD datasets with high visual fidelity.
Physics-Enabled Simulation
Bullet physics engine integration for realistic object interactions and manipulation tasks.
Robot Support via URDF
Configurable robot models including Fetch mobile manipulator, Franka arm, and AlienGo quadruped.
Configurable Sensors
RGB, depth, semantic, and egomotion sensors for varied agent perception configurations.
Modular Task Framework
Habitat-Lab provides modular task definition, agent configuration, and benchmarking tools.
Imitation and Reinforcement Learning
Built-in support for IL and RL training pipelines for embodied AI agents.
Human-Robot Collaboration
Habitat 3.0 co-habitat supports humans, avatars, and robots sharing simulated environments.
Parallelizable Across Clusters
Designed for large-scale distributed training across GPU clusters.
Annual Benchmark Challenge
Habitat Challenge on EvalAI provides standardized evaluation of navigation and manipulation agents.
Scroll within the panel for all 10 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Ai Habitat Context
JSON-LDSpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
AI Habitat API Rules
SPECTRALJSON Schema 8
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
AgentConfig
JSON SCHEMAAgentObservation
JSON SCHEMAEpisode
JSON SCHEMANavigationGoal
JSON SCHEMAObservation
JSON SCHEMASensorSpec
JSON SCHEMASimulatorConfig
JSON SCHEMATaskConfig
JSON SCHEMAScroll within the panel for all 8 ·
JSON Structure 8
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Ai Habitat Agent Config Structure
JSON STRUCTUREAi Habitat Agent Observation Structure
JSON STRUCTUREAi Habitat Episode Structure
JSON STRUCTUREAi Habitat Navigation Goal Structure
JSON STRUCTUREAi Habitat Observation Structure
JSON STRUCTUREAi Habitat Sensor Spec Structure
JSON STRUCTUREAi Habitat Simulator Config Structure
JSON STRUCTUREAi Habitat Task Config Structure
JSON STRUCTUREScroll within the panel for all 8 ·
Examples 8
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Ai Habitat Episode Example
EXAMPLEScroll within the panel for all 8 ·
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Embodied Navigation Research
Train and evaluate AI agents on point-goal, object-goal, and image-goal navigation tasks in 3D environments.
Robot Manipulation Research
Develop manipulation skills for pick-and-place, rearrangement, and tool use with simulated robot arms.
Human-Robot Collaboration
Research human-robot teaming for household tasks using the PARTNR benchmark and Habitat 3.0.
Reinforcement Learning Training
Fast simulation enables RL agents to explore millions of environment steps for policy learning.
Dataset Creation and Annotation
Generate synthetic data, annotations, and demonstrations for embodied AI training datasets.
Integrations 6
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
PyTorch
Deep learning framework integration for neural network training and inference.
HuggingFace
Datasets and models available on HuggingFace Hub at ai-habitat organization.
EvalAI
Habitat Challenge evaluation hosted on EvalAI platform for standardized benchmarking.
Conda / conda-forge
Conda package distribution via conda-forge and aihabitat channels.
Bullet Physics
Bullet physics engine for realistic rigid-body simulation and manipulation.
ROS
Robot Operating System integration for sim-to-real transfer research.
Resources
Every other property we hold for AI Habitat — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 2
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Build 6
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
← All providers · Data indexed from github.com/api-evangelist/ai-habitat · machine-readable index on apis.io