Unify
A research lab studying continual learning: production agents whose model weights keep updating from their own work, gated by evals and forgetting checks
Treat Unify as research output first. The homepage describes a concrete loop — capture traces via one SDK call, convert corrections and retries into rewards and verifiers, run RL weight updates continually, then gate each checkpoint through your eval suite plus a forgetting check — but the published evidence is methodological. "Why we built Continual-ARC" (September 2026) is the honest piece to read: on a stream of ARC tasks across three open agent harnesses, memory on versus wiped saved almost nothing, while a worked-example floor and a one-program-per-task library did far better. That is a lab reporting a result against its own interest. If you are evaluating adaptation-after-deployment
Verified 21h ago · liveness 64/100 · cite: rightaichoice.com/tools/unify
- Research engineers evaluating continual learning for agents
- Teams that already run agents in production and have an eval suite
- Readers tracking continual-learning methods and honest benchmarks
- Anyone studying catastrophic forgetting and how to measure it
- Teams shopping for an agent platform to run sales, ops or support
- Buyers comparing per-seat or credit-based agent pricing
- Users wanting a deployable tool with onboarding and an integration catalogue
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Skip Unify if you need an out-of-the-box agent platform with onboarding, a published integration list and a workflow to run sales, ops or support — this is a research lab publishing methods and benchmark results, reached by email or "Talk to us."
Unify's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Unify — A research lab studying continual learning: production agents whose model weights keep updating from their own work, gated by evals and forgetting checks. Best for Research engineers evaluating continual learning for agents, Teams that already run agents in production and have an eval suite, Readers tracking continual-learning methods and honest benchmarks. Free to use.
What's new in Unify
Checked todayAcross the latest 3 updates: 3 news mentions.
Why we built Continual-ARC
Unify ran three open agent harnesses on a stream of ARC tasks with memory on and with memory wiped. Memory saved them almost nothing; a worked-example floor and a one-program-per-task library did far better.
The harness is not enough
A position piece on agent evaluation harnesses: the harness alone does not tell you whether a method works.
A fast brain and a slow brain for spoken agents
A blog post on applying fast-and-slow processing ideas to spoken agents.
What people actually say about Unify — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
109 mentions across 7 sources (Hacker News, YouTube, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 28, 2026.
Average across the 7 sources that answered — each source counts once, not each post.
- +Zero-setup onboarding via a phone call, no tech needed.
- +Role-specific AI teammates for sales, ops, accounts, support.
- +Persistent memory adapts to workflows and improves over time.
- +Pre-built workflow templates save time on common tasks.
- +Integrated voice, video, and screen-sharing capabilities.
- −No real user reviews exist to validate performance claims.
- −Credit-based pricing can become expensive for heavy users.
- −Limited integrations beyond Slack, Notion, and CRM.
- −No offline or mobile standalone app—web only.
- −Lack of community discussions raises trust concerns.
- • Credits may deplete faster on complex tasks or large models
- • No flat-rate unlimited plan—unpredictable monthly costs
Viability Score
How well maintained and how widely used is Unify? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Continual post-training of an agent's model on its own production work
- One SDK call streams agent traces: tool calls, corrections, retries and outcomes
- Domain-specific rewards and verifiers built from captured signals
- Reinforcement learning weight updates applied continually
- Every checkpoint runs your eval suite before it goes live
- Forgetting check gating each checkpoint alongside evals
- Human approval step for deciding what ships
- Four-step documented loop: capture, score, train, ship
- Continual-ARC benchmark for continual learning evaluation
- Public research agenda
- Blog with research posts and position papers
- Position piece "The harness is not enough"
- Contact via hello@unify.ai
About Unify
Unify is a research lab working on continual learning for production agents. Its premise, stated on the homepage, is that most agents are frozen at deployment — they repeat the same mistakes until someone rewrites the prompt or a new base model ships. Unify changes the model itself rather than the prompt: a single SDK call streams your agent's traces (tool calls, corrections, retries and outcomes), those signals are turned into rewards and verifiers for your domain, reinforcement learning updates the model's weights on its own work, and every checkpoint runs your eval suite plus a forgetting check before you approve what goes live. The lab describes this in four steps — capture, score, train, ship — and frames the work around consolidation, plasticity, and benchmarks that measure them honestly. Published output includes the Continual-ARC benchmark write-up "Why we built Continual-ARC" (September 2026), the position piece "The harness is not enough" (August 2026, per the blog index), and "A fast brain and a slow brain for spoken agents" (February 2026), plus a public research agenda and a contact address. The site offers a blog and a "Talk to us" path rather than a self-serve product tour. If you follow how models adapt after deployment instead of being retrained from scratch, or you care about measuring catastrophic forgetting honestly, this is a source to read rather than a tool to buy.
Behind the Verdict
Unify's homepage makes one claim that is worth taking seriously: most agents are frozen at deployment, and everything they learn after that — the corrections, the retries, the outcomes — is thrown away. The lab's answer is to change the weights instead of the prompt, and it lays out the loop explicitly. One SDK call captures traces: tool calls, corrections, retries, outcomes. Those signals become rewards and verifiers for your domain so the model has a definition of "better" that fits your work. Reinforcement learning then updates the weights continually, and every checkpoint runs your eval suite and a forgetting check, with you approving what goes live. As a design, that is the right shape: the forgetting check is the part most teams skip, and putting it in the deploy gate is the difference between continual learning and slow-motion drift. The published research is where the lab earns its credibility, and it does so by publishing a negative result. "Why we built Continual-ARC" (September 2026) ran three open agent harnesses on a stream of ARC tasks with memory on and with memory wiped. Memory saved them almost nothing. A worked-example floor and a one-program-per-task library did far better. A lab that publishes "the feature we built didn't move the needle, here's what did" is worth reading, and the companion position piece "The harness is not enough" (August 2026) follows the same instinct about evaluation. Earlier work, "A fast brain and a slow brain for spoken agents" (February 2026), shows the agenda ranges past text agents. The weaknesses are the usual ones for research-stage work. The pages describe the mechanism at a methodological level — capturing traces, scoring, RL weight updates, eval plus forgetting checks — and name no underlying model, no benchmarks for the production loop itself (only for the ARC study), and no numbers on how much an agent's own production work actually improves it. There is no product tour, no integration catalogue, and no onboarding material on the pages we reached; the call to action is "Talk to us" and an email address. If you are an engineer deciding whether to build continual post-training yourself, the four-step loop is a reasonable blueprint and the forgetting check belongs in your deploy gate. If you arrived looking for a platform to run sales, ops or support, you are in the wrong place, and the honest move is to read the blog rather than shop.
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Real-world workflow fit
Concrete scenarios for the personas Unify actually fits — and what changes day-one when you adopt it.
You wire one SDK call into your agent to stream tool calls, corrections, retries and outcomes, turn those signals into rewards and verifiers for your support domain, and let RL update the weights on that work — with your eval suite plus a forgetting check running on every checkpoint before you approve a ship.
Outcome: The agent's model absorbs your team's corrections and retries instead of waiting for a prompt rewrite or a new base model, and the forgetting check stops a checkpoint from shipping if it regresses on prior behaviour.
You read "Why we built Continual-ARC" (September 2026) before building your own forgetting benchmark, noting that memory on versus wiped barely moved three open agent harnesses on ARC tasks while a worked-example floor and a one-program-per-task library did far better, then read "The harness is not enough" for the evaluation argument.
Outcome: You avoid a memory-only baseline that measures nothing, and you pick a baseline that actually separates methods.
You read the four-step loop (capture, score, train, ship) as a blueprint, compare it against your current prompt-rewrite workflow, and contact hello@unify.ai about how the eval and forgetting gates would map onto your existing eval suite.
Outcome: You either adopt the loop internally with the forgetting gate at deploy, or you conclude prompt iteration is still enough for your workload and stop there.
Use Cases
- Post-train a production agent on its own tool calls, corrections, retries and outcomes instead of rewriting prompts
- Evaluate whether adaptation-after-deployment is worth the engineering by reading the Continual-ARC negative result first
- Use the forgetting check concept when designing a deploy gate for models that keep updating
- Read "The harness is not enough" before arguing about agent evaluation harnesses in your team's reading group
- Track the consolidation-vs-plasticity tradeoff when assessing continual-learning methods
Models Under the Hood
as of 2026-10-03
Limitations
- The pages we reached describe the mechanism at a methodological level — capturing traces, scoring, RL weight updates, eval plus forgetting checks — and name no underlying model, no price, and no onboarding path beyond "Talk to us" and hello@unify.ai.
- The Continual-ARC study (September 2026) is the one substantive result described: across three open agent harnesses on a stream of ARC tasks, memory on versus wiped saved almost nothing, while a worked-example floor and a one-program-per-task library did far better.
- That is a benchmark study, not evidence that the production loop improves any given agent, and no numbers are published for that.
- Claims on the site are said to carry a number or a citation, so read the blog rather than the homepage for detail.
as of 2026-10-09
Verification history
We have re-verified Unify 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Unify's pricing actually pencils out — and where peers do it cheaper.
Unify's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Setup time & first value
How long it actually takes to get something useful out of Unify — broken out by persona, not the marketing-page minute.
For the research reading, plan an evening: the Continual-ARC write-up is listed at roughly 15 minutes and "The harness is not enough" at about 4 minutes, with the rest of the blog behind them. For the production loop, expect real engineering — one SDK call to start streaming traces, then the work of defining rewards, verifiers and the eval and forgetting gates for your domain. Contact runs
Switching to or from Unify
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a prompt-rewrite workflow: move from editing system prompts after each failure to streaming traces via one SDK call and letting corrections and retries become weight updates.
- →From a retrain-from-scratch cycle: replace periodic full retrains with continual RL updates on the agent's own production work, keeping your existing eval suite as the ship gate.
- →From memory-layer-only adaptation: read the Continual-ARC result first — memory on versus wiped barely moved three open harnesses on ARC tasks, so pair any memory store with weight updates.
- ↗To a managed agent platform: if you need onboarding, an integration catalogue and a per-seat workflow, Unify's research output differs in kind from a platform you subscribe to.
- ↗To building in-house: take the four-step loop (capture, score, train, ship) and the forgetting-check gate as a blueprint and run it on your own stack.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Unify”, and we withheld 6: 6 could not be judged, because “Unify” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Unify.
Official links
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