Deploy Grep Knowledge Agent

Deploy and Host a Grep-Based Knowledge Agent with Railway

A self-hosted AI knowledge agent that replaces vector embeddings with grep, find, and cat. Give the LLM a filesystem and bash — it searches your docs deterministically, explains every step, and costs ~75% less than vector RAG. No vector database, no Vercel lock-in.

┌─────────────┐   ┌──────────────────────────────────────────┐   ┌──────────────┐
│  Chat UI    │──▶│  Web (Nuxt 4 + Nitro)                    │──▶│  Sandbox      │
│  /settings  │   │  · AI SDK agent loop + complexity router │   │  (gVisor)     │
│  /login     │   │  · Postgres (chats, sources, users)      │   │  grep/cat/    │
│             │   │  · Redis (sessions, rate limits)         │   │  find (RO)    │
└─────────────┘   │  · GitHub sync → snapshot volume         │   └──────────────┘
                  └──────────────────────────────────────────┘

About Hosting a Grep Knowledge Agent

The agent clones your GitHub repos into a snapshot volume, then uses a sandboxed shell to run read-only grep/find/cat commands against them. A complexity router classifies each question and routes to the right model — gemini-2.0-flash for trivial questions, claude-sonnet-4 for moderate, claude-opus-4 for complex. Every answer cites the files it read. Deploying on Railway gives you Postgres, Redis, persistent volumes, and private networking — all provisioned automatically with one click.

Common Use Cases

• Documentation Q&A — point it at your repo's docs/ folder and ask questions about your product
• Codebase exploration — let an LLM grep through your source code to answer architecture questions
• Internal knowledge base — sync multiple repos and search across all of them with one query
• Self-hosted alternative to Vercel's knowledge-agent-template — no vendor lock-in, bring your own AI keys
• Cost-effective RAG replacement — no embedding model, no vector DB, no chunking pipeline to maintain

Dependencies for Grep Knowledge Agent Hosting

Deployment Dependencies

• Railway — hosts the web app, sandbox service, Postgres, and Redis
• OpenAI / Anthropic / Google Gemini — at least one AI provider key (bring your own)
• GitHub OAuth App — for user authentication

Implementation Details

Why Deploy on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it. By deploying this knowledge agent on Railway, you get Postgres, Redis, persistent volumes, and private networking — all provisioned automatically with one click.

After Deploy (3 steps)

1. Set an AI provider key — at least one of:

   • OPENAI_API_KEY → platform.openai.com/api-keys
   • ANTHROPIC_API_KEY → console.anthropic.com/settings/keys
   • GOOGLE_GENERATIVE_AI_API_KEY → aistudio.google.com/apikey

2. Create a GitHub OAuth app (Settings → Developer settings → OAuth Apps) with callback URL https://.up.railway.app/api/auth/callback/github — set GITHUB_CLIENT_ID / GITHUB_CLIENT_SECRET

3. Open your app → sign in → Settings → Add GitHub source → enter owner/repo → Sync

4. Ask it anything about your repo. It answers with grep, not vectors.