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Build enterprise Agentic RAG systems with retrieval, memory, and tool orchestration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ragent" yet — see the docs or source repo.
Design an enterprise Agentic RAG solution based on company policies, product docs, and FAQs. Include document parsing, multi-route retrieval, query rewriting, conversation memory, and tool-calling workflows, then provide modules and implementation steps.
A practical architecture, workflow design, and implementation checklist for an enterprise knowledge assistant.
I need to improve answer accuracy in a customer support scenario. Analyze optimization points in intent detection, retrieval, reranking, and answer generation, and design a multi-route retrieval and deep-reasoning strategy.
Recommendations, strategy design, and actionable improvements for the retrieval pipeline.
Create an engineering plan for a zero-to-one enterprise RAG project, covering data ingestion, document chunking, index building, conversation memory, MCP tool integration, evaluation metrics, and rollout phases.
A complete delivery blueprint with system modules, phase goals, and evaluation plans.
Intelligent RAG tool that chooses between private knowledge and web search.
Enable AI agents to read, write, and evolve memory across apps.
Build a self-hosted RAG pipeline for code search, findings, and CI integration.
Give AI agents persistent memory and a knowledge graph across sessions.
Search claims documents, retrieve clauses, and answer questions with governed local LLM workflows.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.