Cadenya
Paid
Developers building agentic workflows
WhileAI
FreemiumAPISelf-HostedAgentic
Most agent evaluation pipelines collapse into vibes — you change a prompt, eyeball a few outputs, and ship hoping the regression you introduced isn't the one the customer finds first. WhileAI is built to replace that with a closed loop: simulate, grade, measure, select, train, repeat.
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Summary
The core workflow runs offline: throw thousands of seeded situations at your agent — broken tools, uncooperative users, edge-case inputs — grade every run pass or fail, then use statistical interval estimates to tell you whether a delta is a real gain or noise. Runs that the model always gets right and runs it always fails are dropped; only the ambiguous middle trains the model. SFT and RL pipelines run on your trainer or theirs, and the trained model serves via an OpenAI-compatible endpoint on your own subdomain. The wall appears when your grading logic gets complex — the library expects you to define and validate your own judge, and teams without existing eval infrastructure spend significant time there before touching a training run.
Bottom line: Pick WhileAI when you have a working agent and need a disciplined loop to prove whether a change actually helped — plan for extra setup time if you do not already have graded datasets or a validated judge, because that prerequisite is yours to build.
Pricing Plans
Usage-BasedDeveloper
Free
Free forever for individuals and small teams. Open-source SDK, simulations, evaluations, training data generation, local training, community support.
- Open-source SDK
- Local training workflows
- Community support
Hosting
per month
Usage-based for model storage, managed training, hosted inference, and auto-scaling.
- Managed SFT/RL
- Hosted inference
- Pay as you go
Enterprise
Custom
Custom annual contract with team features, private deployment, SSO/RBAC, and dedicated support.
- Team collaboration
- Private deployment
- SSO/RBAC
- Dedicated SLA
View full pricing on while.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose WhileAI
More options in Agent Frameworks. Compare pricing, platforms, and features side by side.
Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Offline simulation with reproducible seeds generates thousands of graded situations without an API key or network call, so you can iterate on eval design before spending anything on inference.
- Statistical interval estimates on every experiment distinguish real gains from noise, which means you stop shipping prompt changes that look like improvements on a 20-sample eyeball test and turn out to be regressions at scale.
- The select step filters out situations the agent always or never solves, so training data contains only the ambiguous runs that actually shift model behavior — avoiding the common failure of training on trivia the model already handles.
- OpenAI-compatible endpoint serving after training means your existing clients connect to the fine-tuned model with a one-line base URL change, so you do not rewire application code to test a new checkpoint.
- Apache 2.0 SDK with local workflow support means the simulation and grading loop runs in your own environment, so teams with data-residency constraints are not blocked at the first step.
Cons
- The grading step — marking every run pass or fail — requires a test or judge you have already validated for your domain. Teams without existing eval infrastructure hit this wall before a single training run; building and checking the judge is a prerequisite the tool does not shortcut, and projects with ambiguous success criteria can spend more time here than on training.
- RL and SFT training on custom agents at scale requires either the vendor's managed hosting (a paid-only feature) or your own GPU infrastructure. Teams expecting a no-infrastructure path find the self-hosted SDK hands them a training loop but not the compute — at which point they are managing their own fine-tuning stack and using WhileAI primarily as a data-selection layer, a narrower value proposition than the full loop suggests.
- The library is early-stage — the SDK version shown on the page is 0.126, Hugging Face shows 17 SDK stars, and the reproducing-the-literature feature explicitly notes that three of five tested ideas did not hold up. Teams that need a proven, stable pipeline with large community support and established integrations will find more mileage in established fine-tuning frameworks and are likely to switch rather than wait for the ecosystem to mature.
About
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- September 29, 2026
Best For
Who it's for
- AI agent developers and researchers
- Teams building and iterating on agent systems
- Users needing SFT/RL training pipelines
What it does well
- Generate RL and SFT datasets from agent simulations
- Evaluate agents with pass@1 metrics and interval estimates
- Train and serve models via OpenAI-compatible endpoints
- Run local or managed fine-tuning workflows
Integrations
Frequently Asked Questions
- Is WhileAI free?
- WhileAI offers a free option alongside paid upgrades. Check the current allowances and terms before choosing a plan.
- Does WhileAI have an API?
- Yes. WhileAI exposes a developer API. See the official documentation at https://while.ai for details.
- Can I self-host WhileAI?
- Yes. WhileAI supports self-hosting on your own infrastructure.
Curated lists that include this category
Most agent evaluation pipelines collapse into vibes
You change a prompt, eyeball a few outputs, and ship hoping the regression you introduced is not the one the customer finds first. WhileAI replaces that pattern with a closed loop of simulate, grade, measure, select, train, and repeat.
Core workflow
The offline simulation throws thousands of seeded situations at an agent, covering broken tools, uncooperative users, and edge-case inputs. Every run receives a pass or fail grade. Statistical interval estimates then show whether a change is a real gain or noise. Runs the model always solves or always fails are dropped; only the ambiguous middle becomes training data.
Training and serving
SFT and RL pipelines run on the user’s own trainer or the vendor’s managed option. The resulting model is served through an OpenAI-compatible endpoint on the user’s subdomain. The vendor states that the grading step requires a validated test or judge; without one, teams hit a wall before any training run begins.
Integrations and deployment
The tool integrates with the OpenAI SDK and AWS Bedrock. It offers both an API and a self-hosted option. Pricing follows a usage-based model.
Who it is for / who should skip it
It suits AI agent developers and researchers who already maintain eval infrastructure and need reproducible simulations plus SFT/RL pipelines. Teams without a validated grading judge or those expecting a fully managed, zero-infrastructure path should skip it; the library hands them the requirement to build and check the judge first.
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