Retrieve learned context at inference time
Searches recent interactions and learned artifacts before the next response so the agent can reuse relevant context instead of starting from scratch.
Reflexio
Reflexio is a learning platform for AI agents that captures interaction feedback, turns it into reusable behavior, and retrieves that context before the next response. It supports hosted and local OSS workflows, with Python, REST, and CLI integration paths.
Overview
Reflexio is a learning platform for AI agents that turns interaction history into reusable behavioral guidance. Instead of treating corrections as one-off events, it captures what happened, extracts the relevant trigger and action, and feeds that back into the next run so the agent can change how it behaves.
The product is built around a publish-and-retrieve loop: an agent retrieves learned context before responding, then publishes the completed interaction back to Reflexio. The site positions this as a way to help support agents, coding agents, sales assistants, data analysts, and recruiting workflows improve from real conversations while keeping the resulting learnings visible, controllable, and reversible.
Core capabilities
Publish interactions into reusable learnings
Turns user corrections, failed paths, and successful outcomes into learnings or playbooks that the agent can apply in later conversations.
Evaluation against defined outcomes
Scores sessions against success criteria such as whether the user's problem was solved, whether they corrected the agent, and whether a human had to step in.
Auditable learning control
Shows each learning with its supporting evidence and lets teams rewrite, approve, reject, or delete it from retrieval.
Conflict resolution and consolidation
Uses a background process to de-duplicate and resolve conflicting learning signals so behavior does not drift as new evidence arrives.
Flexible deployment and integration paths
Supports multiple integration paths, including Python, REST, CLI, hosted enterprise, and local OSS workflows.
Practical use cases
Customer support agents that improve from corrections
Support teams can capture repeated corrections, such as unresolved billing issues or missed follow-up questions, and turn them into future handling rules that apply across similar tickets.
Existing agent applications that need a learning loop
Builders can connect Reflexio to an existing agent codebase without rewriting the entire app, then retrieve learned context before inference and publish the completed turn afterward.
Production evaluation and governance
Teams running production agents can review outcomes, compare learned behavior against control responses, and decide whether a learning is good enough to keep using.
Local OSS development and diagnostics
Organizations that prefer local control can run the open-source stack with a local backend and SQLite, then use the CLI to publish interactions, inspect profiles, and manage playbooks.
Controlled deployment across managed or self-hosted environments
Teams with privacy or infrastructure requirements can choose managed, BYOK, or self-hosted deployment patterns while keeping the same API-facing workflow.
Pros and Cons
Pros
- Captures corrections and outcomes as reusable behavior rather than leaving them buried in logs.
- Lets teams audit, approve, reject, rewrite, or delete learned behavior instead of treating memory as opaque state.
- Supports both hosted and local OSS workflows, with Python, REST, and CLI access documented.
- Includes evaluation signals so teams can inspect whether a learning actually helped before relying on it.
- Uses retrieval-time context injection, which keeps only relevant learned signals in the prompt.
Cons
- The public materials do not show detailed setup guidance for every integration path, so teams may need to rely on the docs to choose the right workflow.
- The site describes fully managed, BYOK, and fully self-hosted approaches, but the exact operational boundaries and infrastructure requirements are not fully spelled out on the homepage.
- Some capabilities are presented at a high level in the marketing pages rather than with exhaustive technical detail, especially outside the documented Python and CLI examples.
FAQ
How does Reflexio fit into an agent workflow?
Reflexio is designed to sit in the loop between an agent's interaction history and its next response. The docs show a minimum integration pattern where you search for learned context before the agent responds, then publish the completed turn back into Reflexio so it can update what it knows.
Is Reflexio only for local development?
The documentation shows both Hosted Enterprise and Local OSS paths. The Local OSS setup uses the reflexio-ai package, a local FastAPI backend, and SQLite storage, while the docs also mention hosted enterprise quickstart and a web portal for reviewing outcomes.
What integration options does Reflexio support?
The docs and homepage show Python, REST via cURL, the CLI, and a portability-oriented SDK flow. The site also references a Claude Code plugin and a lightweight integration skill for wiring Reflexio into existing agent repositories.
What does Reflexio learn from interactions?
Reflexio produces learnings, user profiles, playbooks, and evaluation signals. The homepage emphasizes that learnings are auditable, can be rewritten or revoked, and can be approved, rejected, or deleted from retrieval.
What pricing options are available?
The pricing page lists Free, Pro, and BYOC self-hosted options. Free is available at $0/month, Pro is listed at $299/month, and BYOC is offered as a custom self-hosted deployment with dedicated support and onboarding.
Quick Facts
- Category
- AI Agent Platform
- Platform type
- Learning platform for AI agents
- Primary users
- Teams building support agents, coding agents, sales assistants, data analysts, and recruiting workflows
- Integrations
- Python, REST, CLI, hosted enterprise, local OSS, and a Claude Code plugin reference
- Pricing
- Free tier, Pro plan at $299/month, and BYOC self-hosted deployment
- Website
- reflexio.ai
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