Reflexio is an AI agent learning platform. Its core goal is to turn user corrections, failed paths, and successful outcomes into reusable behavior changes for agents, so that agents improve their behavior over time and no longer repeat mistakes.
Purpose and positioning: Reflexio emphasizes that it is "not just another memory layer." Traditional memory tools store what users have said, and you still need to dig through logs to find problems; whether the memory helped and how to undo it cannot be determined. Reflexio focuses on "how the agent should behave differently next time," capturing corrections together with their trigger conditions to form readable rules that enter a queue you control.
Main capabilities:
- Self-improvement loop: continuous learning, rather than one-time learning. In the official example, in March, "refunds allowed within 30 days" was disabled, and in June, "the refund window is now 14 days" took effect.
- Self-tuning learnings: each learning is tuned based on the evidence that produced it.
- Evaluation & impact: it can be determined whether a learning actually helped.
- Review & control: every learning is auditable and under your control; it supports three states—approved, pending, and rejected—and rejected ones are removed from retrieval.
How it works: a loop—your agent publishes what happened, Reflexio extracts what should be done differently, and the next run reads this back, all without retraining the model.
Integration methods: existing LLM calls can be wrapped with a lightweight SDK, with no need to rewrite the agent. Officials provide a portable skill, and also support connecting the same "retrieve—publish" loop via Python, REST, or CLI; prompts can be provided to Codex, Claude Code, or Cursor to run the skill from the agent application repository, which will inspect the existing lifecycle, implement the Reflexio loop, and verify the changed paths. The specific installation commands and code examples were not provided in the original text.
Features: AI-powered assistant, learning and evaluation, persistent context, simple integration, executable signals (automatically extracting trigger conditions and feedback from corrections), evolving intelligence (learned behaviors merge over time and conflicts are resolved), precise context injection (injecting relevant signals only at inference time to reduce token costs).
Deployment and data control: from fully managedIt can run fully self-hosted, while the APIs called by the agents always remain unchanged. In fully managed mode, there is no operations overhead, the service is run by the official provider, and organizations get an independently isolated schema; in bring-your-own-model-credentials mode, use your own OpenAI, Anthropic, DeepSeek, Qwen, xAI, or custom endpoint credentials; you can also negotiate model contracts or define traffic visibility policies; in self-hosted (BYOC) mode, you can run single-tenant within your own cloud account, connect to a database you own, and have your team deploy and operate it, with learning data stored in a Supabase project or Postgres instance you own, making it suitable for regulated environments or scenarios with hard account boundary requirements. This mode only reports usage counts and never reports content.
Limitations and information not provided: specific pricing and billing methods are not provided in the original text; complete installation commands, code examples, details of the supported model list, and the complete list of available platforms/frameworks are all not provided in the original text, and the official documentation should be consulted for accuracy.
