Khoj Freemium

What is Khoj?

Khoj is an AI-powered research assistant designed for people who work with large volumes of information. Unlike generic chatbots, Khoj is built to search across your own documents — PDFs, notes, code repositories, and web bookmarks — and provide grounded, cited answers drawn from your knowledge base.

As an open-source project, Khoj can be self-hosted for full data privacy, or used via the cloud for convenience. It integrates with popular note-taking apps like Obsidian and Emacs, and supports multiple LLM backends including GPT-4, Claude, and local models. Khoj excels at helping researchers, analysts, and students make sense of complex information landscapes.

Product Features

• Cross-Document Semantic Search — Search across all your indexed documents using natural language queries, with results ranked by semantic relevance rather than keyword matching.
• Grounded AI Responses with Citations — Every answer includes inline citations linking back to the source document, so you can verify accuracy and trace reasoning.
• Multi-Format Document Support — Index PDFs, Markdown files, Org-mode notes, Word documents, images (via OCR), and web pages for unified search.
• Obsidian & Emacs Integration — Deep integration with Obsidian and Emacs Org-mode means your AI assistant lives inside your existing knowledge management workflow.
• Multi-LLM Backend Support — Choose from OpenAI, Anthropic, Google, or local models via Ollama, giving you full control over cost, speed, and data privacy.
• Self-Hosted Option — Deploy Khoj on your own server with Docker for complete data sovereignty, meeting enterprise compliance requirements.

Product Highlights

• True Open Source — Khoj is fully open-source under AGPL, meaning you can audit, modify, and self-host without vendor lock-in, unlike proprietary alternatives.
• Citation-First Architecture — Every response is grounded in your actual documents with traceable citations, eliminating hallucination risks common in generic AI assistants.
• Offline-First with Local Models — When paired with Ollama, Khoj works entirely offline, making it ideal for sensitive research environments with air-gapped networks.
• Incremental Indexing — Only new or changed documents are re-indexed, keeping your search index fresh without redundant processing.

Use Cases

• Academic researchers reviewing literature — PhD students index hundreds of PDF papers and use Khoj to find relevant prior work, compare findings, and synthesize literature reviews with cited evidence.
• Legal professionals searching case files — Lawyers index case documents and contracts, then query Khoj to find specific clauses, precedents, or regulatory references across thousands of pages.
• Software engineers navigating large codebases — Developers index code repositories alongside design docs, using Khoj to trace architecture decisions and find relevant implementation details.
• Policy analysts comparing regulations — Policy researchers index regulations from multiple jurisdictions and use Khoj to compare requirements, identify conflicts, and draft compliance summaries.
• Knowledge managers maintaining organizational wikis — Teams index their internal documentation and use Khoj as an always-on research assistant that answers questions with links to internal sources.