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 traditional RAG pipelines, such as embedding models or vector databases. It simplifies the deployment of knowledge-based agents across multiple platforms (web, GitHub, Discord) from a single codebase.

How it works

Instead of using embeddings, the system syncs content from various sources (like GitHub repositories or YouTube transcripts) into a snapshot repository. When an agent needs information, it uses a Vercel Sandbox to execute standard file-system commands like grep, find, and cat to locate and read the relevant data. This process is managed by a unified Nuxt application that handles content synchronization, sandbox management, and AI model routing.

Who it’s for

Developers who want to deploy a knowledge-based AI agent quickly without managing complex vector infrastructure, and those who need their agent to be available across multiple chat platforms simultaneously.

Highlights

• File-Based Search: Uses deterministic bash commands in isolated sandboxes instead of vector search.
• Multi-Platform Deployment: Built-in adapters for web chat, GitHub Issues, and Discord bots.
• Smart Complexity Router: Automatically routes queries to different LLMs based on the difficulty of the question to optimize cost.
• AI-Powered Admin Panel: Includes a natural language interface for managing usage stats, error logs, and source configuration.
• Shared Sandbox Pool: Reduces startup latency by reusing existing sandboxes across users and conversations.