What Khoj is
Khoj is an AI assistant for personal knowledge and search. Khoj helps ask questions over documents, notes, and web sources, connect different LLMs, and build a personal knowledge base.
Documents, notes, and links quickly spread across places, while ordinary search does not always answer a semantic question. That makes the page useful as more than a short catalog card: it explains where the project helps and which part of the job it takes over.
The khoj-ai/khoj repository appeared on GitHub in 2021. For this kind of project, that history matters because code, examples, documentation, and community habits accumulate over time.
Why it exists
The project became noticeable as interest grew in personal AI assistants that work with user material, not only general model knowledge.
The main point of Khoj is not to replace every neighboring tool. It covers a specific part of the work: search, answers, and actions over personal or team knowledge sources. The clearer that part is, the easier it is to decide whether the project belongs in a stack.
Khoj is best judged through practice: what data goes in, which actions happen, what result comes out, and who owns support after the first run.
Inside the repository
The repository contains a Python server, interface, source indexing, model connections, settings, integrations, and documentation.
Khoj connects data sources, an index, a model, and a user interface so a question can rely on selected material.
That structure matters for maintenance. Once a project enters a real system, value comes not only from core features but also from tests, clear configuration, releases, and the ability to track behavior changes.
How people use it
It is used for personal knowledge bases, note search, research tasks, document answers, and internal assistants.
A good start is a small document folder and control questions where the correct source is known.
A good first scenario for Khoj is a small check on real data or a realistic task. It reveals limits faster than browsing a feature list.
Strengths
Khoj is strong because it can build personal AI search around owned data and different models.
It stands out because users want to control their knowledge instead of sending all material to one closed service.
Another advantage is a clear entry point. Even a large project can be studied through one scenario: install it, repeat an example, change one setting, and check the result.
Limits
The limitation is that answer quality depends on sources, indexing, chosen model, and fact checking.
Sources, model settings, access rights, and allowed indexed data should be maintained.
For long-term use, decide who updates the project, where configuration is stored, how new versions are checked, and what to do if behavior changes after an update.
Example
Control question for Khoj
This example shows how to check personal search: the question is tied to an expected source.
{
"question": "What are Acme payment terms?",
"expected_source": "contracts/acme.md",
"must_include": "30 days"
}