getumbrel/llama-gpt
llama-gpt
A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support!
Usage guide
llama-gpt is an open-source project around chatgpt, code-llama, codellama with 10,944 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 TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its 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 llama-gpt for TypeScript AI workflows.
- Comparing a GitHub project with 10,944 stars and current repository activity.
Pros
- llama-gpt has visible GitHub traction with 10,944 stars. Topics: ai, chatgpt, code-llama.
- 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 MIT terms fit your use case.
Production readiness
llama-gpt should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
llama-gpt architecture preview
llama-gpt's main path starts at the entry surface, runs through Coding agent runtime, combines OpenAI / Llama, Runtime context, GitHub, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://apps.umbrel.com/app/llama-gpt
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
OpenAI / Llama
Model calls are likely routed through OpenAI, Llama based on README and topic signals.
OpenAI, Llama
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub
Tool adapters let the runtime act outside the model through GitHub.
GitHub
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
llama-gpt uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/getumbrel/llama-gpt.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100%
This is one of the documented reasons to evaluate llama-gpt before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate llama-gpt before choosing a stack.
All project comparison
Compare llama-gpt with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official llama-gpt 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 llama-gpt 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 llama-gpt?
llama-gpt is an open-source all project. A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support!
How do I install llama-gpt?
Start with the official README. The first detected setup step is: git clone https://github.com/getumbrel/llama-gpt.git.
Is llama-gpt beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can llama-gpt be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does llama-gpt 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 llama-gpt?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.