vercel-labs/knowledge-agent-template

What it solves

This project provides a template for building AI agents that can access and search a custom knowledge base without the need for complex embedding pipelines or vector databases. It eliminates the infrastructure overhead associated with traditional RAG (Retrieval-Augmented Generation) by using direct file-system searches.

How it works

The system syncs content from various sources (such as GitHub repositories and YouTube transcripts) into a snapshot repository. When a user asks a question, the agent operates within an isolated Vercel Sandbox. Instead of using vector search, the agent uses standard bash tools like grep, find, and cat to search and read the files directly. It also features a complexity router that sends simple questions to cheaper models and complex ones to more powerful LLMs to optimize costs.

Who it’s for

Developers who want to deploy a knowledge-based AI agent across multiple platforms (Web, GitHub, Discord) quickly and using a deterministic, explainable search method rather than a vector-based approach.

Highlights

• No-Vector RAG: Uses file-based search (grep, find, cat) for deterministic and instant results without embeddings.
• Multi-Platform Deployment: A single agent can be deployed as a web chat app, a GitHub bot, and a Discord bot simultaneously.
• AI-Powered Admin: Includes an admin panel with a dedicated agent that can answer operational questions about usage, errors, and performance using natural language.
• ** Shannon Sandbox Pool**: Uses a shared pool of read-only sandboxes to ensure fast startup times (under 100ms) and resource efficiency.
• Complexity Routing: Automatically routes queries to different LLM models based on the difficulty of the question to reduce costs.