Reflexio
Behavioral learning that makes AI agents better over time
Reflexio launched on September 1, 2026 with 295 votes, #51 of 8,097 launches that month and more than 99% of that year's launches.
In plain words
Reflexio is a behavioral learning platform that enables AI agents to improve their performance over time by autonomously reflecting on interactions and learning from user feedback. It captures corrections, failed attempts, and successful outcomes to create reusable behavior changes that agents can apply to future tasks. The system integrates with existing AI applications through Python, REST, or CLI, making learned behaviors visible and reversible. It is designed for teams building AI agents that need to reduce repeated mistakes and continuously enhance agent capabilities based on real-world usage.
written from the facts on this page · September 2026
From the sources
Turn static AI agents into self-improving systems. Reflexio autonomously reflects on interactions, corrects mistakes, and learns new skills from user feedback.
Reflexio turns user corrections, failed paths, and successful outcomes into behavior changes your agents reuse — each one visible, and revocable. “ I found two unfamiliar charges — $49.99 and $9.99. Refund both? ” Start with the portable skill, or wire the same retrieve-and-publish loop through Python, REST, or the CLI. Follow this skill to integrate Reflexio into my agent: https://github.com/ReflexioAI/reflexio/blob/main/skills/integrate-reflexio/SKILL.md Run this from your agent application's repository. The skill inspects the existing lifecycle, implements the Reflexio loop, and verifies the changed path. The lessons are already in your logs. Four things Reflexio does with them. Every…from reflexio.ai
Pricing, as stated on its site
Free tier, paid plans from $299/mo — Free tier with 100K input tokens/month, 100 learnings/month, and 1,000 search requests/month. Pro tier at $299/month for production scale with 10M input tokens/month, 10K learnings/month, and 100K sea
checked September 2026 · prices change
Does a similar job
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- MRMorph Reflexes – Multi-head classifiers for agent tracesJun 2026 · ▲20
The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…

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