Reflexio
Reflexio is a learning platform for AI agents that captures user corrections and interaction outcomes to produce auditable, tunable behavioral learnings which agents can retrieve and apply to improve future performance.
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Quick Overview
Best for: Business Operations
What it does
AI Tools software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Operations
Pricing snapshot
Free
Next step
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Reflexio
Reflexio is a learning and evaluation platform built to make AI agents self-improving by turning user corrections, failed paths, and successful outcomes into reusable behavioral learnings. It watches agent conversations, extracts actionable signals (for example: search the full window of recent charges before resolving any one), and surfaces these as auditable rules that the agent can retrieve at inference time. Reflexio is intended for developer and team use: it provides SDKs, REST and CLI integrations, an integration skill on GitHub, and multiple deployment models (managed, BYOK, BYOC, self-host).
Behavioral learning that makes AI agents better over time Discussion | Link
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Claim this listing for $29Key Features
Self-improvement loop
Every conversation feeds back into Reflexio which notices recurring failures, captures them as learnings, and retires older learnings when contradicted by newer evidence.
Self-tuning learnings
Each learning is tuned by evidence from subsequent sessions—Reflexio watches where a learning helped or fell short and revises it from those cases.
Evaluation & impact measurement
Reflexio scores conversations against user-defined success criteria (problem solved, corrections, human handoff) and traces improvements back to the learnings behind them.
Review & control (auditable)
All learnings are auditable and controllable: open a learning to see evidence, rewrite, approve, reject or delete it; a rejected learning stops being used immediately.
Simple integration (SDK, Python, REST, CLI)
Wrap existing LLM calls with a lightweight SDK or use a provided portable skill; integrations are available via Python, REST, or the CLI and a sample integration skill is on GitHub.
Actionable signal extraction
Triggering conditions and actionable feedback (e.g., churn signals or syntax errors) are extracted automatically and can be tuned with domain-specific extractors.
Precise context injection
Only relevant signals are retrieved at inference time to keep token cost down and ensure precise context injection.
Flexible deployments & data control
Deployment options include managed, BYOK (bring your own keys/models), BYOC (your cloud and DB such as Supabase/Postgres), and fully self-hosted single-tenant setups for air-gapped or regulated environments.
Pricing
Start free (the homepage advertises 'Start free' but specific plan details are not listed on the page)
Use Cases
Customer support / billing resolution
Catch and consolidate issues across a conversation (example: detecting multiple unfamiliar charges and prompting to refund them together) to reduce repeated user interactions and improve resolution quality.
Coding agent improvements
Detect and learn from corrected tool usage or process mistakes in coding assistants so future runs reuse the corrected behavior.
Sales & conversational assistants
Improve assistant behavior in sales or conversational workflows by extracting business-specific signals (e.g., churn indicators) and applying validated learnings.
Recruiting and analytics workflows
Capture domain-specific corrections and rules from interactions to make recruiting or data-analyst agents progressively more accurate and aligned with policy.
Integrations
LLM providers (BYOK)
Bring your own provider credentials: OpenAI, Anthropic, DeepSeek, Qwen, xAI and more, or a custom endpoint for model calls.
Databases & storage
Store learning data in a Supabase project or Postgres instance you own when using the managed option; BYOC and self-hosted options allow running inside your own AWS, GCP or Azure account.
Platform integrations
Integration paths via Python, REST, CLI and a reference integration skill available on GitHub.
Benefits
Limitations
No verified limitations are available.
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Sign up or click 'Start free' or 'Book a demo' on the Reflexio homepage
- 2 Step 2: Wrap your existing LLM calls with the Reflexio SDK or add the provided portable skill from the GitHub integration (Python, REST, or CLI)
- 3 Step 3: Publish agent runs to Reflexio, let it extract actionable signals, review and approve learnings, and enable retrieval at inference time
Support
docs
Product documentation and API reference available at the Docs link: https://www.reflexio.ai/docs
demo / sales
Book a demo from the site ('Book a demo' link on the homepage) to evaluate enterprise fit and deployment options
open source / repository
Integration examples, SDKs and a sample integration skill are available on the Reflexio GitHub repository: https://github.com/ReflexioAI/reflexio
API
API Reference and SDK docs available via Reflexio documentation (https://www.reflexio.ai/docs)
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