Learn from real interactions.
Reflexio turns user corrections, failed paths, and successful outcomes into reviewed behavior changes your agents can reuse safely.
Agent handled only the first suspicious charge and missed the broader user intent.
Agent searched all recent charges, surfaced both unauthorized transactions, and resolved them in the first conversation.
Why teams trust Reflexio
Interaction to behavior change
How It Works: One Missed Pattern, Learned
Watch one missed pattern become a learned behavior change, then replay the same customer issue with the learning applied.
Waiting for the second interaction...
Waiting for the second interaction...
Waiting for the second interaction...
Core Capabilities
From Static Agents to Self-Improving Systems
Your agents interact with users thousands of times, but the lessons from those interactions are trapped in logs. Reflexio closes that loop.
Self-Improvement Loop
When a user corrects or undoes your agent's action, Reflexio captures the feedback along with the conditions that triggered it — turning one-off fixes into permanent behavioral improvements that compound with every interaction.
Optimized Execution Path
The first time your agent works through a task, Reflexio remembers the path that worked. The next time the same task comes around, it skips the trial-and-error — taking a shorter, proven route that gets faster and more reliable with every repeat.
See the Impact
Prove your agent is actually improving, not just changing. Run built-in A/B tests to track response win rate, or use shadow deployment to compare new and current behavior side by side before you ship.
Quick Start
Up and Running in Three Lines
Publish an interaction, and Reflexio learns from it. Next time, retrieve the context it captured — no retraining, no prompt engineering.
# pip install reflexio-ai
from reflexio import ReflexioClient
client = ReflexioClient()
# Publish a support conversation
client.publish_interaction(
user_id="user_123",
session_id="session_001",
interactions=[
{"role": "User", "content": "Show me last week's sales report"},
{"role": "Agent", "content": "Here's the daily breakdown..."},
{"role": "User", "content": "I always want the 7-day rolling average"},
],
)
# Next time: retrieve what Reflexio learned
context = client.search(query="sales report", user_id="user_123")Architecture
What You're Wiring Into
A continuous learning loop that turns every interaction into actionable improvement — no retraining required.
Your Agent
AI-powered assistant
Learning Store
Persistent context
Your Agent
Learning Store
Your Agent
AI-powered assistant
Reflexio
Learn & Evaluate
Learning Store
Persistent context
Simple Integration
Wrap your existing LLM calls with a lightweight SDK — no agent rewrite needed
Actionable Signals
Automatically extract triggering conditions and actionable feedback from user corrections
Evolving Intelligence
Learned behaviors consolidate and resolve conflicts automatically over time
Precise Context Injection
Only inject relevant signals at the moment of inference — reducing token waste
Why Choose Us
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
Treat agent learning like software deployments. Version behaviors, test with shadow deployment, and roll back bad learning like a git commit.
Low-Cost Learning Extraction
When a user corrects your agent's tool usage or process, Reflexio extracts that as actionable feedback for all future similar scenarios.
Conflict Resolution
A background process de-duplicates and resolves conflicting learning signals, preventing behavioral drift and learning rot.
Data Privacy
Bring your own storage. Your users' learning data never stays on our servers. 100% data ownership guaranteed.
The Difference
Not Just Another Memory Layer
See how Reflexio fundamentally differs from traditional approaches
| Traditional MemoryVector DBs, Mem0, Zep | ReflexioBehavioral learning layer |
|---|---|
Stores what users said | Learns how agents should act |
Remembers user facts | Builds actionable behavioral rules |
Static memory records | Self-evolving learned behaviors |
No validation | Shadow deployment testing |
Passive retrieval | Active learning loop |
Generic conversation context | Domain-specific signal extraction |
"What did the user say?" | "How should the agent behave differently next time?" |
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