Interaction publishing
Send user-agent conversations, session information, agent versions, metadata, and retrieved learnings to Reflexio so the system can analyze completed turns.
Reflexio
Freemium
Reflexio é uma plataforma de aprendizagem para agentes de IA que transforma resultados de interações e correções dos usuários em orientações comportamentais recuperáveis. Os desenvolvedores podem conectar um ciclo de reflexão, avaliação e recuperação a agentes existentes por meio de APIs hospedadas, um SDK Python, REST ou uma CLI e um backend locais.
O que é Reflexio?
Reflexio is a behavioral learning platform for AI agents. Instead of only storing what users said, it captures interactions, corrections, and outcomes to identify how an agent should behave differently in similar situations. The resulting learnings can be retrieved before a later response and are intended to reduce repeated mistakes without retraining the underlying model.
The platform supports a loop in which an agent retrieves relevant profiles or playbooks, produces a response, and publishes the completed interaction back to Reflexio. Developers can use the hosted service, connect through Python or REST, or run the open-source local backend and CLI. Learnings can be reviewed, approved, rejected, revised, and evaluated against an un-augmented response.
O que Reflexio pode fazer?
Relevant context retrieval
Search profiles and user or agent playbooks before an agent responds, then inject selected context into the model prompt. Retrieval can be filtered and limited for the application’s token budget.
Behavioral learning extraction
Convert corrections, triggering conditions, and successful or failed outcomes into actionable signals about how the agent should act in future situations.
Evaluation and revision
Compare a candidate response with an un-augmented control response and use the resulting evidence to revise learnings over time.
Review and governance controls
Inspect learned rules and their statuses, approve or reject them, and remove rejected learnings from retrieval. The API also includes profile, playbook, configuration, evaluation, and data-rights operations.
Flexible deployment and integration
Connect through a Python SDK, REST API, CLI, or integration skill. The service can be managed by Reflexio, use customer model credentials or storage, or run in a customer AWS, GCP, or Azure account.
Casos de uso
“Customer-support agents”
A support agent can learn to inspect the full window of recent charges before resolving one unfamiliar transaction, reducing the chance of asking the customer to return with a missed issue.
“Coding agents”
Developers can use business-specific extractors and playbooks to capture recurring coding feedback, such as how an agent should respond to a syntax or implementation error.
“Sales assistants”
A sales agent can use retrieved profiles and learned playbooks to apply recurring interaction guidance when handling later conversations.
“Data-analysis agents”
Teams can publish analyst-agent interactions and retrieve relevant learned behavior for subsequent analysis tasks, while retaining a reviewable learning record.
“Local or controlled deployments”
Teams that need local development or a customer-controlled environment can use the open-source CLI and local FastAPI backend, or discuss a BYOC deployment in their own cloud account.
Perguntas frequentes
How does Reflexio fit into an existing agent?
The documented minimum loop retrieves profiles and playbooks before the agent responds, passes selected context to the LLM, and publishes the completed interaction afterward. The site says the SDK can wrap existing LLM calls without an agent rewrite.
Does Reflexio retrain the underlying model?
No. The product describes a retrieve-and-publish learning loop rather than model retraining. Learned behavioral context is retrieved and injected at inference time.
What can developers use to connect to Reflexio?
The site documents a Python SDK, REST access, a CLI, and an integration skill for coding agents. The local open-source package includes the Python SDK, CLI, and local FastAPI server.
Can teams review or undo learned behavior?
Yes. Learnings are presented as reviewable rules with statuses such as approved, pending, and rejected. The site states that rejecting a learning removes it from retrieval, and the API includes playbook and configuration management.
What deployment options are available?
Reflexio offers a managed service, split arrangements using customer model credentials or storage, and BYOC/self-hosted deployment in AWS, GCP, or Azure. The documentation also describes a local open-source backend using SQLite by default.
Informações rápidas
- Category
- AI platform for agent learning
- Primary users
- Developers and teams operating AI agents
- Core workflow
- Retrieve context, generate a response, publish the interaction, evaluate and update learnings
- Integration surfaces
- Python SDK, REST API, CLI, and integration skill
- Local setup
- reflexio-ai package with a local FastAPI backend and SQLite default
- Pricing options
- Free tier, Pro at $299/month, and custom BYOC self-hosted deployment
Análise de tráfego de Reflexio
Os dados de tráfego são apenas para referência.
- Classificação do domínio
- 4
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