Compare
How While. compares.
Most tools in this space watch an agent and tell you what broke. whileai is an open-source RL and SFT post-training library: it builds a dataset from the traces, trains an open model on your own compute with your own keys, and measures the gain on a held-out set with an interval. Each page below says what the other tool does well, with sources.
Why you need While.
Monitoring tells you the agent failed. It does not make the agent stop failing.
The fix is a change to the model. That takes three things: training data from the failures, a training run, and a held-out evaluation with an interval on the difference.
While. does all three. The other tools on this page stop at the red check.
- HarborBenchmark tasks in sandboxes. Rollouts you take to a trainer.
- RaindropProduction monitoring. Simulations replays traffic against a change.
- Judgment LabsProduction traces, judges, behaviors as rates, offline tests.
- LangChainLangSmith traces, evals and agent hosting. Engine edits prompts and code, not the model.
- TRLHugging Face's trainer. A dataset in, a model out.
- SkyRLRL over agent environments. A task in, a model out.
- prime-rlPrime Intellect's RL trainer over environments. A task in, a model out. whileai hands it an environment.
- While.Open-source RL and SFT post-training library. Your traces or simulated rollouts in. Train on Modal or Prime Intellect with your own keys. A paired held-out delta with an interval out.
While. vs Raindrop
Raindrop finds problems in a running agent. While. trains the model on the failures with SFT and RL, on your own compute, and proves the change on a held-out set.
Read the comparison
While. vs Judgment Labs
Judgment Labs finds the failure and measures how often it happens. While. trains the model on the failures with SFT and RL, on your own compute, and proves the change on a held-out set.
Read the comparison
While. vs Harbor
Harbor scores agents on tasks someone wrote. While. trains the model on the failures with SFT and RL, on your own compute, and proves the change on a held-out set.
Read the comparison
While. vs LangChain
LangSmith watches the agent and edits its prompts and code. While. trains the model on the failures with SFT and RL, on your own compute, and proves the change on a held-out set.
Read the comparison
Missing one? Email jacob@while.ai and we will add it.