Skip to content

docs: call Reflexio a learning platform, not a layer #466

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
guangyu-reflexio merged 2 commits into from
Aug 25, 2026
Merged

docs: call Reflexio a learning platform, not a layer #466

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1ba3070
docs: call Reflexio a learning platform, not a layer
guangyu-reflexio Aug 25, 2026
ccfbf64
docs: fix the SDK README's category claim
guangyu-reflexio Aug 25, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion AI_AGENT_INTEGRATION.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -162,7 +162,7 @@ REFLEXIO_API_KEY="..."
```

Implementation rule: if Reflexio is unavailable, the agent must continue
normally. Treat Reflexio as a best-effort learning layer, not as a dependency
normally. Treat Reflexio as a best-effort learning platform, not as a dependency
that can break user work.

## Configure an LLM Provider
Expand Down
4 changes: 2 additions & 2 deletions benchmark/gdpval/RESULTS.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,10 @@
Modern AI agents already try to improve themselves between runs: they
cache skills, remember prior plans, and get faster on tasks they have
seen before. This raises a pointed question: **is there still room
for an external self-improvement layer to add measurable value on top
for an external learning platform to add measurable value on top
of an agent that is already self-improving?**

We test **Reflexio**, a layer that reads a completed run of a task
We test **Reflexio**, a learning platform that reads a completed run of a task
and produces a concrete, copy-pasteable recipe for the next run of
the same task, on **five real knowledge-work tasks** from the public
OpenAI GDPVal benchmark. These five tasks were selected because the
Expand Down
2 changes: 1 addition & 1 deletion client_dist/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
[![License: Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-green)](https://github.com/ReflexioAI/reflexio/blob/main/LICENSE)
[![Downloads](https://static.pepy.tech/badge/reflexio-ai/month)](https://pepy.tech/project/reflexio-ai)

The official Python SDK for [Reflexio](https://www.reflexio.ai/) — the adaptive memory layer for AI agents. Reflexio automatically extracts user profiles, generates playbooks, and evaluates agent performance from conversation data. This client provides type-safe, sync-first access to the full Reflexio API. For source code and contributions, see the [GitHub repository](https://github.com/ReflexioAI/reflexio).
The official Python SDK for [Reflexio](https://www.reflexio.ai/) — the learning platform for AI agents. Reflexio automatically extracts user profiles, generates playbooks, and evaluates agent performance from conversation data. This client provides type-safe, sync-first access to the full Reflexio API. For source code and contributions, see the [GitHub repository](https://github.com/ReflexioAI/reflexio).

## Table of Contents

Expand Down
Loading