khoj-ai/khoj
khoj
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
Usage guide
khoj is an open-source project around agent, assistant, chat with 35,380 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating khoj for Python AI workflows.
- Comparing a GitHub project with 35,380 stars and current repository activity.
Pros
- khoj has visible GitHub traction with 35,380 stars. Topics: agent, ai, assistant.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the AGPL-3.0 terms fit your use case.
Production readiness
khoj should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
AGPL-3.0 is reported by GitHub; review the repository license before redistribution or commercial use.
khoj architecture preview
khoj's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI / Claude / Gemini / Llama, Vector index, GitHub / Discord / WhatsApp, and returns Grounded answers / search results.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://khoj.dev
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI / Claude / Gemini / Llama
Model calls are likely routed through OpenAI, Claude, Gemini, Llama based on README and topic signals.
OpenAI, Claude, Gemini, Llama
Context
Vector index
Context comes from Vector index, which constrains what the model or runtime can use.
Vector index
Tools
GitHub / Discord / WhatsApp
Tool adapters let the runtime act outside the model through GitHub / Discord / WhatsApp.
GitHub, Discord, WhatsApp
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Featured video
Biology ScienceSK
कोशिका | What is cell | koshika ki khoj, aakar, akriti | Discovery, Shape, Size of cell | ScienceSK
265,481 views · 2021-08-10
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
khoj depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/khoj-ai/khoj.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
Your AI second brain. Self-hostable. Get answers from the web or your
This is one of the documented reasons to evaluate khoj before choosing a stack.
Focus area: agent
This is one of the documented reasons to evaluate khoj before choosing a stack.
Image project comparison
Compare khoj with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official khoj setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented khoj example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is khoj?
khoj is an open-source image project. Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
How do I install khoj?
Start with the official README. The first detected setup step is: git clone https://github.com/khoj-ai/khoj.git.
Is khoj beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can khoj be used commercially?
GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does khoj need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt khoj?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.