llms.txt
# Reflexio
> The learning platform for AI agents. Reflexio observes real agent-user interactions and
> turns corrections into scoped, inspectable behavioral rules — so an agent improves from
> use instead of repeating the same mistake for every new user.
Reflexio is non-parametric: improvements are evolved as skills, playbooks and context
around a fixed model, which keeps every learned behavior readable, scoped and reversible.
## Blog
- [Continuous Evaluation vs Continuous Learning for AI Agents](https://www.reflexio.ai/blog/continuous-evaluation-vs-continuous-learning-for-ai-agents): The article explains the difference between continuous evaluation, which tests agent performance, and continuous learning, which turns past experience into reusable lessons that change future behavior, and describes how the platform Reflexio combines both approaches to help AI agents avoid repeating mistakes.
Raw markdown: https://www.reflexio.ai/blog/continuous-evaluation-vs-continuous-learning-for-ai-agents.md
- [How to Measure AI Agent Behavior Improvement](https://www.reflexio.ai/blog/how-to-measure-ai-agent-behavior-improvement): A step-by-step guide on measuring AI agent behavior improvement, covering how to establish a baseline, select task-specific metrics such as success rate and retries, compare results before and after changes, and verify whether learning leads to lasting gains without regressions.
Raw markdown: https://www.reflexio.ai/blog/how-to-measure-ai-agent-behavior-improvement.md
- [Human-in-the-Loop Learning for AI Agents: Review, Approval and Escalation](https://www.reflexio.ai/blog/human-in-the-loop-learning-for-ai-agents): Learn when AI agents should act, request approval or escalate, and how human corrections can become reusable, controlled behavioural learning.
Raw markdown: https://www.reflexio.ai/blog/human-in-the-loop-learning-for-ai-agents.md
- [Outcome Based Learning for AI Agents: Turn Results Into Better Behavior](https://www.reflexio.ai/blog/outcome-based-learning-for-ai-agents): Explore how production outcomes, successful actions and failed paths can provide evidence for improving AI agent behaviour.
Raw markdown: https://www.reflexio.ai/blog/outcome-based-learning-for-ai-agents.md
- [How AI Agents Learn From User Corrections Without Retraining](https://www.reflexio.ai/blog/how-ai-agents-learn-from-user-corrections-without-retraining): Learn how AI agents can turn user corrections into evaluated, reusable behavioural guidance without retraining the underlying model.
Raw markdown: https://www.reflexio.ai/blog/how-ai-agents-learn-from-user-corrections-without-retraining.md
- [Everyone Is Building Self-Improving Agents. Almost Nobody Means the Same Thing.](https://www.reflexio.ai/blog/what-self-improving-agent-means): A nightly fine-tune and a one-line note appended to a markdown file are both sold as agents that learn from experience. They are the same loop with different parts swapped out. This is a map of the field: what changes, what decides, and when — and why the second question is the one that separates methods that compound from methods that only look like they do.
Raw markdown: https://www.reflexio.ai/blog/what-self-improving-agent-means.md
- [Your Agent Dreams. It Still Can't Tell You If It Got Better.](https://www.reflexio.ai/blog/agent-dreaming-vs-replay-gated-tuning): Dreaming makes an agent's memory better. Only measurement makes the agent better. Anthropic's Dreams, ChatGPT, Codex, Gemini CLI and OpenClaw all rewrite agent memory offline — and every gate they document checks whether the rewritten memory is tidy, not whether the agent improved.
Raw markdown: https://www.reflexio.ai/blog/agent-dreaming-vs-replay-gated-tuning.md
- [How Creao Turned Agent Exploration Into Repeatable Execution](https://www.reflexio.ai/blog/creao-repeatable-agent-execution): Creao used Reflexio to turn successful agent behavior into reusable guidance—cutting median model calls by 75%, reducing task failures by 36%, and showing positive impact from retrieved learnings in 47% of evaluated cases.
Raw markdown: https://www.reflexio.ai/blog/creao-repeatable-agent-execution.md
- [Your Agent Remembers Everything and Learns Nothing](https://www.reflexio.ai/blog/remembers-everything-learns-nothing): AI memory and agent self-improvement sit in the same slot, use the same storage, and are sold against the same failure — which is why they get confused. The difference is what has to happen before the system writes anything down. One writes when something is said. The other writes when something is judged.
Raw markdown: https://www.reflexio.ai/blog/remembers-everything-learns-nothing.md
- [From Loop Engineering to Self-Improvement: The Loop Over Loops](https://www.reflexio.ai/blog/loop-engineering-and-self-improvement): Loop engineering is how we get agents to iterate toward a goal on their own. Self-improvement is the next stage — a loop wrapped around those loops, so each pass makes the next one better. This is a field note on how the two ideas connect.
Raw markdown: https://www.reflexio.ai/blog/loop-engineering-and-self-improvement.md
- [Learn, Generalize, Reflect and Optimize (LGRO): A Practical Guide for Self-Improving AI Agents](https://www.reflexio.ai/blog/lgro-self-improving-agents): Memory remembers what happened. Learning changes what the agent does next time. LGRO — Learn, Generalize, Reflect, Optimize — is a practical framework for non-parametric, self-improving AI agents.
Raw markdown: https://www.reflexio.ai/blog/lgro-self-improving-agents.md
## Documentation
- [Docs](https://www.reflexio.ai/docs): concepts, API reference, CLI reference and getting-started guides.
- [Docs llms.txt](https://www.reflexio.ai/docs/llms.txt): machine-readable index of the documentation.
- [Docs llms-full.txt](https://www.reflexio.ai/docs/llms-full.txt): full documentation text.
## Source
- [Reflexio on GitHub](https://github.com/ReflexioAI/reflexio)
- [claude-smart, the open-source coding-agent plugin](https://github.com/reflexioAI/claude-smart)
## Optional
- [Full blog text](https://www.reflexio.ai/llms-full.txt): every post in full, as markdown.
- [RSS feed](https://www.reflexio.ai/rss.xml)
