Learning platform for AI agents
Learn from real interactions.Improve behavior. Stop repeating mistakes.
Reflexio turns user corrections, failed paths, and successful outcomes into behavior changes your agents reuse — each one visible, and revocable.
“There's a $49.99 charge on my card I don't recognize.”
Missed the second charge
“I've refunded the $49.99 charge.”
The user came back 10 minutes later.
“There's also a $9.99 one.”
2 conversations
Caught both charges at once
“I found two unfamiliar charges — $49.99 and $9.99. Refund both?”
Nothing. The user was already done.
1 conversation
- 1Search the full window of recent charges before resolving any single one.
- 2Present everything unfamiliar in one message, and ask whether to refund together.
Let your coding agent integrate Reflexio.
Start with the portable skill, or wire the same retrieve-and-publish loop through Python, REST, or the CLI.
Give this prompt to Codex, Claude Code, or Cursor.
Follow this skill to integrate Reflexio into my agent:https://github.com/ReflexioAI/reflexio/blob/main/skills/integrate-reflexio/SKILL.mdRun this from your agent application's repository. The skill inspects the existing lifecycle, implements the Reflexio loop, and verifies the changed path.
Use integrate-reflexioWhat you are wiring into.
One loop. Your agent publishes what happened, Reflexio extracts what to do differently, and the next run reads it back. Nothing is retrained.
Your Agent
AI-powered assistant
Reflexio
Learning & evaluation
Learning Store
Persistent context
Your Agent
Learning Store
Your Agent
AI-powered assistant
Reflexio
Learning & evaluation
Learning Store
Persistent context
Simple integration
Wrap your existing LLM calls with a lightweight SDK — no agent rewrite needed.
Actionable signals
Triggering conditions and feedback, extracted from corrections automatically.
Evolving intelligence
Learned behaviors consolidate, and conflicts between them resolve, over time.
Precise context injection
Only the relevant signals, at the moment of inference — which keeps token cost down.
Not another memory layer
Memory remembers.Reflexio changes behavior.
Traditional memory
What memory tools store
- Stores what users said
- You read the logs to find the problem
- Facts a model may or may not retrieve
- No way to tell whether a memory helped
- No way to undo a bad memory
Reflexio
Behavioral learning platform
- Learns how the agent should act
- The correction and its trigger are captured together
- Rules you can read, in a queue you control
- Responses scored against the un-augmented one
- Reject one and it drops out of retrieval
“What did the user say?”
“How should the agent behave differently next time?”
Static agents, made self-improving.
The lessons are already in your logs. Four things Reflexio does with them.
Self-improvement loop
It keeps learning, not just once.
- Refunds allowed within 30 daysretired
- Refund window is now 14 dayslive
Self-tuning learnings
Every learning is tuned by the evidence it produces.
Evaluation & impact
Know whether it actually helped.
Review & control
Every learning is auditable, and under your control.
Check all recent charges first
- approvedretrievable
- pendingretrievable
- rejecteddrops out of retrieval
Why Reflexio?
Built different from the ground up to create agents that actually learn.
- Autonomy through reflection
- Agents think back on their performance and optimize their own logic — not just retrieve stored facts.
- Full extraction control
- Tunable extractors that look for business-specific signals — a churn signal in customer service, a syntax error in a coding agent.
- Safe behavioral evolution
- Every learned behavior is scored against a control response, and rejecting one revokes it from retrieval immediately.
- Low-cost learning extraction
- A user's correction becomes actionable feedback for every similar scenario that follows.
- Data rights
- Your users can have their data exported or permanently erased on request. Bring your own storage or cloud.
- Conflict resolution
- A background process de-duplicates and resolves conflicting learning signals, preventing behavioral drift and learning rot.
Your data, your keys, your cloud.
Different teams draw the line in different places. Reflexio runs anywhere from fully managed to fully self-hosted — and the API your agent calls never changes.
Managed
Nothing to operate. We run the service, and your organization gets its own isolated schema.
No ops budget.
BYOK
Your own provider credentials — OpenAI, Anthropic, DeepSeek, Qwen, xAI, or a custom endpoint.
Negotiated model contracts, or a policy on who sees your traffic.
Your database
We run the service; the learning data sits in a Supabase project or Postgres instance you own.
Data that must stay queryable, backed up and retained in-house.
Ships usage counts only — never content.
BYOC / Self-host
Reflexio runs inside your own cloud account, single-tenant, against a database you own — deployed and operated by your team.
Regulated environments with a hard account perimeter.