Reflexio - Open-Source AI Agent Learning Platform

What Is Reflexio? A Plain-Language Explanation

Reflexio is a behavioral learning platform for AI agents. If you run a support bot, coding assistant, or internal agent, you have probably watched the same mistake happen twice: it refunds the wrong line item, skips a required confirmation, or loops until a human takes over. Retraining the foundation model is slow and expensive. One-off prompt edits do not survive the next release.

Reflexio sits next to your agent and turns real conversations — user corrections, failed tool paths, and successful outcomes — into readable rules the agent retrieves before the next message. There are no weight updates and no fine-tune queue. When a user fixes the agent once, that lesson can apply to the next thousand similar sessions, with an audit trail and a delete button if the rule goes stale.

Think of it as experiential memory for agents: not "what the user said," but "how the agent should act differently next time."

How the Agent Learning Loop Works

Reflexio wraps your existing LLM calls with a lightweight publish → extract → retrieve loop:

1. Publish — After a session, your agent sends the transcript, tool calls, and outcome signals (resolved, corrected, escalated) to Reflexio.
2. Extract — Reflexio distills recurring failures and wins into learnings: short, human-readable rules your team can rewrite, approve, or reject.
3. Retrieve — At the start of the next session, only relevant learnings are injected before the first model call, which keeps token cost down.

Start with the portable integration skill for Codex, Claude Code, or Cursor, or wire the same loop through the Python SDK, REST API, or CLI. LangChain, AutoGen, CrewAI, and custom stacks are supported.

What Reflexio Does With Your Logs

Continuous self-improvement

Every conversation feeds back. Reflexio spots patterns that keep failing, promotes them into learnings, and retires older rules when newer sessions contradict them — so agents track policy changes instead of freezing on launch-day behavior.

Self-tuning learnings

Each learning is scored on live impact: which sessions it helped and which it did not. Reflexio revises wording from those cases instead of leaving a bad rule forever.

Evaluation and measurable impact

You define success — ticket closed, no escalation, task completed. Reflexio scores sessions against that definition and ties results to the learnings that were active, so you know whether a change actually helped users.

Review, approval, and instant revoke

Open any learning to see the evidence behind it. Rewrite, approve, reject, or delete. Rejection removes it from retrieval immediately. Optional approval-required mode blocks learnings from going live without human sign-off — important for regulated or customer-facing agents.

Who Should Use Reflexio

Good fit:

• Production agents with repeat traffic: support, sales, coding assistants, recruiting, internal ops.
• Teams that need auditable behavioral change without full model retraining.
• Organizations that must keep data in their VPC, own database, or air-gapped environment.

Poor fit:

• One-off demos with almost no volume — not enough signal for stable rules.
• Teams that cannot define success or will never review learnings; stale rules will accumulate.

The product site highlights support, coding agents, sales assistants, data analysts, and recruiting workflows as common starting points.

Reflexio vs. Vector Memory

Reflexio is not another RAG layer. It answers "how should the agent behave differently next time?" with governance, not "what did the user mention once?"

Pricing and Plan Limits

As of September 2026, Reflexio publishes these tiers on its official pricing page:

• Free — $0/month — 100K input tokens/month, 100 learnings generated/month, 1,000 search requests/month; data retained while your account is active; includes a 30-day Pro trial; community support.
• Pro — $299/month — 10M input tokens/month, 10K learnings/month, 100K search requests/month; priority support.
• BYOC self-hosted — custom — Run in your AWS, GCP, or Azure; data stays in your infrastructure; self-managed token usage; configurable retention; contact sales for onboarding.

Sign-up is open with email, Google, or GitHub. Book a demo on the site if you need help sizing a deployment.

Deployment and Data Control

The API your agent calls stays the same across modes:

• Managed — Reflexio operates the service with an isolated schema per organization.
• BYOK — Your keys for OpenAI, Anthropic, DeepSeek, Qwen, xAI, or custom endpoints.
• Your database — Managed service with learnings stored in your Supabase or Postgres.
• BYOC — Reflexio runs inside your cloud account.
• Self-host — Single-tenant on infrastructure you control, with no connection back to Reflexio — for air-gapped or vendor-independence requirements.

The platform documents user data export/erase on request and background conflict resolution so contradictory learnings do not drift behavior over time.

Frequently Asked Questions

Does Reflexio retrain my foundation model?

No. Improvements are runtime learnings — retrieved rules and preferences — not new model weights. Rolling back is as simple as rejecting a rule.

How is this different from fine-tuning or RLHF?

Fine-tuning and RLHF change the model checkpoint. Reflexio captures behavioral corrections from production and applies them on the next session, with human veto and measurable impact per deployment.

Can I self-host Reflexio?

Yes. The core is Apache 2.0 open source on GitHub (ReflexioAI/reflexio). Managed tiers add hosted convenience; BYOC and full self-host options exist for data-residency requirements.

What do I need to get started?

Create a free account, choose an integration path (agent skill, Python SDK, REST, or CLI), publish your first session, review the extracted learning, and enable retrieval on the next run. See the documentation for SDK and CLI reference.

Is Reflexio only for chatbots?

No. Any tool-using agent with repeatable workflows — refunds, code edits, CRM updates — can publish sessions and retrieve learnings. It targets multi-step agents, not single-turn FAQ bots alone.

Official Resources and Developer Community

• Documentation and API reference
• Pricing
• GitHub repository (Apache 2.0)
• Integration skill for coding agents