01 Platform
Rafflesia gives AI labs Gymnasium-compatible simulated wet lab environments — pipetting, cell culture, plate handling — so RL agents can learn experimental design without consuming real reagents or equipment time.
6
environments
at launch
<5ms
step latency
per simulation step
10×
faster iteration
vs. real wet lab
100%
Gym-compatible
drop-in for any RL loop
02 Core capabilities
High-fidelity simulation
Environments grounded in real wet lab physics and stochastic noise profiles. Train agents on the same variability they will face in real experiments.
Gymnasium-compatible API
Drop-in compatibility with standard RL frameworks. Structured action spaces, dense reward signals, and reproducible seeds for fair policy comparison.
Standardised benchmarks
Published baselines and leaderboards across modalities. Measure how well a policy generalises across plate formats, reagent concentrations, and noise levels.
03 Environments
Liquid Handling v1
PipettingSuccess rate
0.94
Plate Transfer v2
Plate HandlingReward (↑)
1.21
Cell Culture v1
CulturingSuccess rate
0.87
Widefield Imaging v1
ImagingSuccess rate
0.91
Synthesis v1
ChemistryReward (↑)
—
Bioprocess v1
FermentationSuccess rate
—
04 How it works
Connect your framework
Install the Rafflesia client. Works with Stable Baselines3, RLlib, CleanRL, and any framework that speaks the Gymnasium interface.
pip install rafflesia import rafflesia as gym
Pick an environment
Choose a lab modality and configure your plate format, reagent concentrations, and noise profile.
env = gym.make("LiquidHandling-v1",
plate="96-well",
noise=0.02)Train and benchmark
Run your policy loop. Metrics stream to the dashboard. Compare against published baselines or share runs with collaborators.
obs, _ = env.reset() for step in range(steps): action = policy(obs) obs, r, done, _ = env.step(action)
05 Who it's for
Frontier AI labs
Build RL agents that can plan and execute multi-step biological experiments. Replace costly wet lab iteration with fast simulation rollouts.
AI-bio startups
Accelerate drug discovery, bioprocess optimisation, and automated synthesis with agents trained entirely in simulation before touching real equipment.
Research teams
Reproduce results, share environments, and compare policies with standardised benchmarks — the missing infrastructure layer for AI biology research.
06 Early access
If your team is building RL agents for biological applications and needs high-fidelity simulation environments, we want to hear from you. Early access partners shape the roadmap.