Compare
How While. compares.
Most tools in this space watch an agent and tell you what broke. While. is a mid-training and post-training platform: the open-source SDK builds and optimizes a dataset from the traces, an open model is trained on it with SFT and RL, and the gain is measured on a held-out set. 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. No traffic, no serving, no proof.
- While.Your traffic or simulated situations in. A trained model on your endpoint, proved on a held-out test.
While. vs Raindrop
Raindrop finds problems in a running agent. While. learns from production traces and continuously improves the model with mid-training and post-training SFT and RL.
Read the comparison
While. vs Judgment Labs
Judgment Labs finds the failure and measures how often it happens. While. learns from production traces and continuously improves the model with mid-training and post-training SFT and RL.
Read the comparison
While. vs Harbor
Harbor scores agents on tasks someone wrote. While. learns from production traces and continuously improves the model with mid-training and post-training SFT and RL.
Read the comparison
While. vs LangChain
LangSmith watches the agent and edits its prompts and code. While. learns from production traces and continuously improves the model with mid-training and post-training SFT and RL.
Read the comparison
Missing one? Email hello@while.ai and we will add it.