whereismyai
rag_control
rag_control: Runtime governance and security layer that manages execution control and observability for RAG systems.
rag_control is an enterprise-grade governance, security, execution control, and observability layer for Retrieval-Augmented Generation systems. It addresses compliance challenges by providing policy-based generation, runtime enforcement, and organization-level rules. Users can define generation policies for temperature, output length, citation requirements, and external knowledge restrictions, while validating responses before returning them to users. The platform includes comprehensive audit logging, distributed tracing with OpenTelemetry integration, and 18+ metrics covering throughput, latency, quality, costs, and errors. It is built for developers and organizations deploying secure RAG systems in production environments.
rag_control at a glance
Key features
- Define and enforce multiple generation policies
- Control temperature and output tokens
- Enforce citation requirements
- Apply data classification retrieval filters
- Track full request and response lifecycles
- Monitor token usage, latency, and error rates
- Integrate OpenTelemetry for distributed tracing
Use it for
- Enforce generation policies and citation requirements
- Audit RAG system requests and policy decisions
- Monitor performance metrics and token usage
- Control access based on data classification
Alternatives to rag_control
- My RAG — Compare Python and Java implementations sharing identical data.
- UltraRAG — Build complex RAG workflows using modular MCP components and YAML configuration.
- Code Graph RAG — It builds knowledge graphs from codebases using Tree-sitter parsing.
- RAGFlow — Provides an integrated platform combining data processing with agent workflows.
- llmware — Build private, local RAG pipelines with 300+ prepackaged models and integrated document parsing.
- Daytona — It provisions sandboxes in under 90 milliseconds with full security isolation.
- RAG-Anything — It unifies text and multimodal document retrieval within a single framework.
- RAG Evaluation Starter — Runs five metrics locally with no SaaS signup or mandatory API key required.
- OpenAPPA — Tracks data flows algebraically to prevent prompt injection without breaking agents.
- New Relic — Unified visibility across applications, infrastructure, and AI models.
- VoltAgent — Offers open-source flexibility with built-in supervisor agent orchestration and RAG.
- ODS — Automates complex local AI infrastructure setups with one command.
All rag_control alternatives