# Build knowledge agents without embeddings

DevFeed: [Build knowledge agents without embeddings](<https://devfeed.tech/articles/build-knowledge-agents-without-embeddings-727.md>)

Original publisher: [Read original article](<https://vercel.com/blog/build-knowledge-agents-without-embeddings>)

Author: Ben Sabic

Published: 2026-03-19T13:00:00Z

Content type: tutorial

Language: en

Sources: [Vercel News](<https://devfeed.tech/sources/vercel-news.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [bash](<https://devfeed.tech/tags/bash.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [search](<https://devfeed.tech/tags/search.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vercel](<https://devfeed.tech/tags/vercel.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

## AI overview

The article presents an open-source knowledge-agent template that replaces vector retrieval and embeddings with filesystem search in isolated Vercel Sandboxes. It describes source syncing, shell-based search, traceable results, and deployment options.

## Source excerpt

Most knowledge agents start the same way. You pick a vector database, then build a chunking pipeline. You choose an embedding model, then tune retrieval parameters. Weeks later, your agent answers a question incorrectly, and you have no idea which chunk it retrieved or why that chunk scored highest. We kept seeing this pattern internally and for teams building agents on Vercel. The embedding stack works for semantic similarity, but it falls short when you need a specific value from structured data. The failure mode is silent: the agent confidently returns the wrong chunk, and you can't trace the path from question to answer. That's why we tried something different. We replaced our vector pipeline with a filesystem and gave the agent bash. Our sales call summarization agent went from ~$1.00 to ~$0.25 per call, and the output quality improved. The agent was doing what it already knew how to do: read files, run grep, and navigate directories. So we open-sourced the Knowledge Agent Template, a production-ready version of this architecture built on Vercel. What the template does The Knowledge Agent Template is an open source, file-system-based agent you can fork, customize, and deploy. Plug any source: GitHub repos, YouTube transcripts, documents (e.g., markdown files), or custom APIs. Ship it as a web chat app, a GitHub bot, a Discord bot, or all three at once. The template is built on Vercel Sandbox, AI SDK, and Chat SDK. Deploy to Vercel in a single click, configure your sources, and start answering questions. File-based search with Vercel Sandbox No vector database. No chunking pipeline. No embedding model. Your agent uses grep, find, and cat inside of isolated Vercel Sandboxes. Here's how it works: You add sources through the admin interface, and they're stored in Postgres Content syncs to a snapshot repository via Vercel Workflow When the agent needs to search, a Vercel Sandbox loads the snapshot The agent's bash and bash_batch tools execute file-system commands Th