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Reflexio
Behavioral learning that makes AI agents better over time
432 followers
Behavioral learning that makes AI agents better over time
432 followers
Reflexio makes AI agents better with every interaction. When users correct an agent, a path fails, or something works particularly well, Reflexio turns that experience into behavior the agent can reuse next time. Instead of leaving valuable lessons buried in logs, your agent continuously learns what to repeat and what to avoid — with every learning visible, testable, and reversible. Reduce task failure rate by more than 30%, while saving tokens by more than 60%.
Reflexio
Hey Product Hunt 👋
I'm Yi, co-founder of Reflexio. Before starting Reflexio, I was tech lead in Meta and adjunct professor at University of Washington teaching ML and business applications.
Today we're launching Reflexio: a learning platform that makes your AI agents fail less and burn fewer tokens, by learning from what actually happens in production.
Here's what got us started: people use AI agents every day, but agents never actually get better with use. Even with memory, an agent that failed a task yesterday will fail the same way today, across different users — because nothing connects what happened in production back to how the agent behaves next time. The online learning loop just isn't there.
We learned firsthand that closing that loop manually — reading traces, spotting failures, rewriting prompts — is a painful, never-ending job.
Reflexio autonomously observes your agent's live traces, learns from successes, failures, and user corrections, and continuously optimizes behavior. No manual tuning.
The results? In our case studies, agents with Reflexio:
🎯 Cut task failure rate by 36%
💸 Reduced token usage by 57%
📈 Improved response quality in 47% of interactions, with negligible regressions
Try it today: sign up free at reflexio.ai and get 30 days of Pro on us.
@yilu It's a real challenge when agents keep making the same mistakes across various sessions and users. Once you start scaling, manual trace analysis can quickly become overwhelming. Just a quick question about the token reduction—are you mainly achieving that through more streamlined and optimized prompts over time, or are you also cutting out any unnecessary context?
Reflexio
@liam_ward2 Awesome question. You're getting to the core of this product.
Because the agent has worked on the similar task before, Reflexio will help the agent find out what is more optimized to follow next time. This means 75% fewer internal model steps and roughly translated to 57% token reduction because less detour agent needs to make next time. We validate our impact with real customer case study https://www.reflexio.ai/blog/creao-repeatable-agent-execution and GDPval dataset
@yilu congratulations on the lauch Reflexio is a great product, my best wishes with you good luck.
@yilu The token savings are caught my attentions.
what kinds of agent have benefited the most from Reflexio so far? curious to see how well it adapts across various agents,
Congratulations teams for launch!
Reflexio
@vipul_kumar1280 thanks! While the way to learn is common across all types of agents and domains, Reflexio works best when one agents is serving many users, such as customer support agents, marketing and SDR agents, and digital employees.
@yilu great work, strong product wish best of luck to you and team
Promomix
i wonder how it handles conflicting feedback from different users. does it learn a general rule or keep the behavior context specific?
Reflexio
@irsa_doham We first learn behavioral improvements specific to each user, and when there is a common pattern, we roll them up into a generalized rule that can apply to all users. When there are conflicts among users, we will be able to learn behavior improvement for specific user group and only apply the learning to users belong to that group.
nice，can I export or delete all learnings if I decide to move off the platform
Reflexio
@peggykk absolutely, all of those user profiles and learnings are in your control and can be exported if you want to move off from it. Note that, even though learnings are generated, we have offline reinforcement learning pipeline that continuously optimize learned signals. So as there are more traffic, existing learnings will be updated as well to find even better playbook to follow
Reflexio
@peggykk Yes, you can delete a user account along with all associated learnings and metadata from Reflexio. We also support exporting all learnings upon request.
CapCut AI Suite
Saw Python, REST, and CLI, do you have any plans for a TypeScript SDK?
Reflexio
@lavana_cricko good question, yes, typescript is on our roadmap! stay tune to it. in the meanwhile, REST api endpoint is probably your best bet. let us know how it works for you and reduce your agent's failure rate!
Congratulations @yilu ✌️
Question, How can this be integrated into existing stacks like LangGraph or CrewAI?
Reflexio
@aymi_malik we provide skills for coding agent to integrate with reflexio. simply copy the following to codex or claude code, let it do the integration for you! The integration will be non-intrusive to your agent
```
Follow this skill to integrate Reflexio into my agent:
https://github.com/ReflexioAI/reflexio/blob/main/skills/integrate-reflexio/SKILL.md
```
NexaSDK for Mobile
I think I got the concept.
How do you measure "negligible regressions"? Is there an eval harness that runs before a learning gets applied?
HyNote AI
Can I review and approve learnings before they go live, or is it completely autonomous?
Reflexio
@ristan_nakko great question. You can definitely first publish user interactions and see all the learnings from the dashboard, or use the API to check them. If you don't like them, you can edit them, delete them, or even adjust your learning objective and re-generated those learnings based on your customerizations!