--- title: "Deploy Khoj — Self-Hosted AI Second Brain" description: "Self-host Khoj — chat with your docs, AI second brain" category: "AI/ML" url: https://railway.com/deploy/khoj-ai-second-brain --- # Deploy Khoj — Self-Hosted AI Second Brain Self-host Khoj — chat with your docs, AI second brain **[Deploy Khoj — Self-Hosted AI Second Brain on Railway](https://railway.com/template/khoj-ai-second-brain)** - **Creator:** SilverBanana - **Category:** AI/ML ## Template content ### Postgres https://devicons.railway.app/i/postgresql.svg - **Image:** ghcr.io/railwayapp-templates/postgres-ssl:18 ### Khoj https://avatars.githubusercontent.com/u/134046886?v=4 - **Image:** ghcr.io/khoj-ai/khoj-cloud - **Public domain:** Yes ### Terrarium https://res.cloudinary.com/asset-cloudinary/image/upload/v1777310664/khoj-logo_dwyiol.webp - **Image:** ghcr.io/khoj-ai/terrarium ### SearxNG https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/searxng.svg - **Image:** searxng/searxng:latest ## Documentation # Deploy and Host Khoj on Railway Khoj is an open-source personal AI assistant and "second brain" — chat with your documents, run semantic search across your notes, browse the web, and build custom agents, all self-hosted so your knowledge stays private. Backed by Y Combinator with 34k+ GitHub stars, it connects to OpenAI, Anthropic, or Gemini and turns your files into a searchable, conversational knowledge base. This template deploys Khoj's full stack — server, pgvector database, code sandbox, and web search — pre-wired, so you have a private AI assistant over your own data in minutes. --- ## What This Template Deploys | Service | Purpose | | --- | --- | | **Khoj** | The assistant server, chat UI, and document engine (Django + Next.js) | | **PostgreSQL + pgvector** | Vector embeddings of your documents for semantic search | | **Terrarium** | Sandboxed Python execution for agents (data analysis, code) | | **SearxNG** | Privacy-focused metasearch for the assistant's web research | All four connect over Railway's private network, pre-wired so the assistant, its memory, code execution, and web search work together out of the box. --- ## About Hosting Khoj is a capable multi-service assistant, and a few specifics make it deploy cleanly on Railway — all handled here. **Four services, pre-wired — the setup that's hard to do by hand.** Khoj isn't a single container: it needs the server, a pgvector database, the Terrarium code-execution sandbox, and SearxNG for web search, all connected. This template wires them over the private network so document search, agent code execution, and web research work immediately, instead of assembling four services and their URLs manually. **pgvector, not standard Postgres.** Khoj stores document embeddings and runs semantic search through the pgvector extension, which Railway's managed Postgres doesn't include — so this template uses the `pgvector/pgvector:pg16` image. This is what lets Khoj retrieve the right context from your notes by meaning, not just keywords. **Reduce `GUNICORN_WORKERS` to fit Railway's RAM.** This is the key Railway-specific tuning: Khoj defaults to 6 Gunicorn workers, which exceeds an 8 GB plan and causes out-of-memory crashes. Set `GUNICORN_WORKERS` to 2–3 so the server stays within the plan limit. With cloud LLM APIs, Khoj then runs comfortably in about 4 GB. This template sets a sensible worker count for Railway. **Use cloud LLM APIs — not local models.** Khoj supports local models via Ollama, but that needs a GPU and 8–16 GB RAM, which isn't practical on Railway. Use cloud APIs instead: set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` (or add them in the admin panel), and you bring your own keys, paying each provider directly. **Set your admin and Django secret.** `KHOJ_ADMIN_EMAIL` and `KHOJ_ADMIN_PASSWORD` create your admin account, and `KHOJ_DJANGO_SECRET_KEY` (a long random 50+ character string) secures sessions — keep it stable. After deploy, sign in at `/server/admin` to add API keys and configure chat models. **Your data persists in Postgres.** Documents, embeddings, chat history, and agent configurations live in the pgvector database, so they survive redeploys — back it up to preserve your second brain. Typical cost: **~$10–20/month** on Railway for the four services, plus your LLM provider usage. Khoj is AGPL-3.0 and free. --- ## How It Compares | | Khoj (self-hosted) | ChatGPT | NotebookLM | Cloud AI assistant | | --- | --- | --- | --- | --- | | Document search | Yes — semantic | Per-chat uploads | Notebooks | Varies | | Custom agents | Yes | GPTs | No | Varies | | Web search | Built-in (SearxNG) | Yes | No | Varies | | Provider choice | OpenAI/Anthropic/Gemini | OpenAI | Google | Vendor | | Data ownership | Full — your infra | Vendor | Google | Vendor | | Self-hostable | Yes | No | No | No | ChatGPT and NotebookLM are capable but keep your documents on a vendor's servers and lock you to one provider. Cloud AI assistants remove setup but bill monthly and hold your data. Khoj's edge is a private, self-hosted second brain — semantic search over your own documents, custom agents, and web research — with your choice of provider and your knowledge on infrastructure you own. --- ## Deploy in Under 5 Minutes 1. Click **Deploy on Railway** — the four services build and wire together (~5 minutes) 2. Confirm `GUNICORN_WORKERS` is set low (2–3) and pgvector is connected 3. Set `KHOJ_ADMIN_EMAIL`, `KHOJ_ADMIN_PASSWORD`, and `KHOJ_DJANGO_SECRET_KEY` 4. Open `/server/admin`, sign in, and add your OpenAI, Anthropic, or Gemini API key 5. Open the Khoj UI, upload documents, and start chatting with your knowledge base Khoj indexes your files into pgvector so you can search and chat over them by meaning. --- ## Common Use Cases - **Chat with your notes** — semantic search and conversation over your own documents and Markdown - **Personal second brain** — a private, searchable knowledge base you talk to, on your infrastructure - **Research assistant** — combine your documents with live web search for grounded answers - **Private document Q&A** — ask questions across PDFs, Word docs, and notes without a cloud service --- ## Configuration | Variable | Required | Description | | --- | --- | --- | | `KHOJ_ADMIN_EMAIL` | Required | Admin account email for the Khoj panel | | `KHOJ_ADMIN_PASSWORD` | Required | Admin password — set a strong value | | `KHOJ_DJANGO_SECRET_KEY` | Required | 50+ char random string securing sessions — keep stable | | `GUNICORN_WORKERS` | Pre-set | `2–3` to stay within Railway's RAM limit (default 6 OOMs) | | `OPENAI_API_KEY` / `ANTHROPIC_API_KEY` / `GEMINI_API_KEY` | Recommended | Your LLM provider key(s) | | Postgres connection | Auto-injected | pgvector database credentials via Railway references | > **Lower `GUNICORN_WORKERS`, and pgvector is required.** The default 6 workers exceed Railway's 8 GB plan and cause OOM crashes — set 2–3. Khoj needs the pgvector image (not stock Postgres) for semantic search; this template uses it. > **Bring cloud API keys, not local models.** Local inference needs a GPU Railway lacks — use OpenAI, Anthropic, or Gemini keys. Set a stable `KHOJ_DJANGO_SECRET_KEY` and strong admin credentials. --- ## Dependencies for Khoj Hosting - Railway account — ~$10–20/month for the four services, plus LLM provider usage - PostgreSQL with pgvector (included and wired for embeddings) - An LLM provider key (OpenAI, Anthropic, or Gemini) for cloud inference - Optional: search API keys (Serper, Firecrawl, Jina) for enhanced web research ### Deployment Dependencies - [Khoj GitHub Repository](https://github.com/khoj-ai/khoj) - [Khoj Documentation](https://docs.khoj.dev/) - [Khoj Self-Host Guide](https://docs.khoj.dev/get-started/setup/) - [Railway Private Networking](https://docs.railway.com/guides/private-networking) ### Implementation Details The template deploys Khoj's full four-service stack over Railway's private network: the Khoj server (Django backend with a Next.js frontend baked in), a `pgvector/pgvector:pg16` PostgreSQL database for document embeddings, the Terrarium sandbox (`ghcr.io/khoj-ai/terrarium`) for isolated Python execution by agents, and SearxNG for privacy-focused web search. Standard Postgres lacks the pgvector extension Khoj needs for semantic retrieval, which is why the pgvector image is used. A Railway-specific requirement is reducing `GUNICORN_WORKERS` from the default 6 to 2–3, since the default exceeds the 8 GB plan limit and causes out-of-memory crashes; with cloud LLM APIs, Khoj then runs comfortably in around 4 GB. Local model inference via Ollama is impractical on Railway due to the RAM and GPU constraints, so cloud provider keys (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY`) are the practical path, added as environment variables or through the admin panel at `/server/admin`. `KHOJ_ADMIN_EMAIL` and `KHOJ_ADMIN_PASSWORD` create the admin account, and `KHOJ_DJANGO_SECRET_KEY` (a long random string) must remain stable to keep sessions valid. Documents, embeddings, chat history, and agent configurations persist in the pgvector database, which is the component to back up. --- ## Frequently Asked Questions **Why does Khoj need pgvector?** It stores document embeddings and runs semantic search through the pgvector extension, which Railway's managed Postgres doesn't include. This template uses the `pgvector/pgvector:pg16` image so retrieval by meaning works. **Why is Khoj running out of memory?** The default 6 Gunicorn workers exceed Railway's 8 GB plan. Set `GUNICORN_WORKERS` to 2–3, and with cloud LLM APIs Khoj runs comfortably in about 4 GB. This template sets a sensible worker count. **Can I use local models?** Not practically on Railway — local inference via Ollama needs a GPU and more RAM than the plan allows. Use cloud API keys (OpenAI, Anthropic, or Gemini) instead. **How do I add my API key?** Sign in at `/server/admin` and add your OpenAI, Anthropic, or Gemini key under Server Chat Settings, or set it as an environment variable on the Khoj service. **Does my data persist?** Yes — documents, embeddings, chat history, and agents live in the pgvector database and survive redeploys. Back it up to preserve your knowledge base. --- ## Why Deploy Khoj 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 Khoj on Railway you get a private AI second brain with the hard parts solved — four services pre-wired, pgvector for semantic search, worker count tuned for Railway's RAM, and BYOK cloud providers. Chat with your documents and build custom agents, self-hosted on infrastructure you own. ## Similar templates - [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform. - [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage. - [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack Open this page in a browser: https://railway.com/deploy/khoj-ai-second-brain