Agentic
RAG Sandbox
Build, test, and reason with your data using agentic retrieval and tools. Extend your assistant with MCP—not prompts.
What are the key findings from the research paper?
Based on the retrieved documents, the key findings include three main areas of improvement...
Most RAG demos break in real usage.
Generic retrieval systems fail when they hit production complexity. Agentic RAG Sandbox was built to fix these problems at the system level.
What you can do with the sandbox
Intelligent Data Ingestion
Upload PDF, DOCX, CSV, JSON, and text files. Context-aware chunking preserves meaning with local embeddings via transformers.js.
High Quality Retrieval
Adaptive vector search with fallback logic. Ordered context reconstruction with confidence-aware injection and source attribution.
Agentic Tool Use
Planner-driven decision making with MCP-based tool discovery. Clean separation between reasoning and execution.
Secure & Personal
Email/password auth with strict row-level security. Each user's data stays completely isolated.
How the system thinks
Instead of forcing every query through retrieval, the system plans first, retrieves only when needed, and uses tools when documents aren't enough.
Query
User sends a question
Plan
System decides approach
Retrieve
Fetch relevant context
Execute
Run MCP tools if needed
Respond
Grounded, cited answer
Planning First
The planner evaluates each query to decide if retrieval, tools, or direct response is best.
Confidence Scoring
Context is assembled with confidence scores, allowing the LLM to weight information appropriately.
Ordered Context
Retrieved chunks are ordered for coherent narrative flow, not random context stuffing.
More than a chatbot
Traditional RAG
Agentic RAG Sandbox
Built for people who care about systems
Not a consumer toy. A learning and prototyping environment.
Developers
Building RAG or agent pipelines
Researchers
Experimenting with retrieval
Students
Learning LLM architecture
Builders
Prototyping assistants
Built with modern, inspectable tech
Client → Edge Functions → Vector DB → LLM → Response
Experiment with agentic RAG the right way.
A transparent, secure, and extensible environment for building retrieval augmented generation systems.