Agent learning and evaluation layer that converts real interaction outcomes and corrections into persistent, reviewable behavioral guidance.
Reflexio is an open-source learning and evaluation layer for AI agents. It ingests conversations, corrections, successful paths and optional expert responses, then extracts user profiles and reusable behavioral playbooks. Those learnings can be retrieved into later agent runs without retraining the underlying foundation model.12
The system is more than a memory store: it tracks evidence behind learnings, supports approval/rejection/deletion, resolves conflicts and evaluates whether a learning improved subsequent sessions. The current platform also supports shadow/control comparisons and success metrics tied back to specific learnings.1
The open-source package exposes Python SDK, REST and CLI surfaces. It supports multiple model providers through configurable APIs and can run locally with SQLite or against owned infrastructure.23
Current hosted pricing starts with Free at $0/month: 100K input tokens, 100 generated learnings and 1,000 searches per month, plus a 30-day Pro trial. Pro is $299/month and raises those limits to 10M input tokens, 10K learnings and 100K searches. BYOC self-hosted deployments are custom-priced.4
The Apache-2.0 local OSS package remains a meaningful free/self-host option. Reflexio also describes managed, BYOK, customer-owned database, BYOC and fully self-hosted patterns, giving regulated teams several ways to control data location.1
Reflexio emphasizes auditable and revocable behavioral change: every learned behavior can be inspected, modified, approved, rejected or removed, and rejected learnings stop being retrieved.1 That is a strong safety property for systems that continuously adapt from user feedback.
The repository publishes GDPVal and retrieval-latency benchmarks, including large planning-step/token reductions. These are useful reproducible first-party claims but are not independent validation and are not imported as external rating inputs.2
The local OSS quick-start currently runs the FastAPI backend without authentication by default. Documentation presents it as a local service on localhost; operators should not expose that default endpoint to an untrusted network without adding appropriate access controls.3
The fixed 50-source panel found no current exact-product professional review, independent benchmark or user-rating signal. Four panel domains were inaccessible through the lawful crawler path and were not bypassed. Confidence therefore remains LOW despite the product's strong documented architecture.
Reflexio is best for teams running persistent agents where user corrections and successful procedures repeat: customer support, internal operations, coding agents and domain-specific assistants. It is especially relevant when teams want agents to improve behavior while retaining human visibility and the ability to revoke a bad learning.1
It is less useful for short-lived stateless chat flows, or when a team already has a carefully maintained rules/evaluation system and does not need automated extraction. Buyers considering hosted Pro should measure how many corrections are actually converted into useful, stable playbooks before committing.
the open-source core, auditable/revocable learnings, expert-learning path, evaluation loop, hybrid retrieval, multiple deployment modes and clear Free/BYOC options.12 The system addresses a real gap between ordinary memory and controlled behavioral adaptation.
limited independent evidence, new-product maturity and a relatively high $299 hosted Pro price.4 Local OSS also places more security/operations responsibility on the user, particularly because the default local server does not require authentication.3 AiToolMap therefore scores the design strongly but keeps confidence LOW.
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