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LLMstudio

Name: LLMstudio
Author: TensorOpsAI

by TensorOpsAIPython

Gateway and management tool for calling, caching and monitoring multiple LLM providers.

Toolexperimental

Maturity: experimental because active but has never tagged a release. Derived from release and commit history, not a rating.

Stars: 388
Forks: 42
Downloads / mo: —
Last commit: 2026-07-29
License: MPL-2.0
Open issues: 9

Market and trust evidence

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In practice

Written by AI from this repository’s README · medium confidence

Prompt iteration and per request logging are scattered across notebooks with no single place to compare providers.

Use it when

Use it when you want one proxy endpoint plus a playground and request log in front of OpenAI, Anthropic, Google or Ollama models.

Not the right pick when

Thin documentation and a short README make it a weak pick if you need a well documented production observability stack.

Capabilities

LLM proxy access to OpenAI, Anthropic and Google models
custom and local models through Ollama
prompt playground UI
monitoring and logging of usage per request
LangChain integration and batch calling
smart routing and fallback between providers

Requirements

A .env file holding OPENAI_API_KEY, ANTHROPIC_API_KEY and VERTEXAI_KEY
Extras llmstudio[proxy,tracker] for the full version

Cost: Free and open source

Install

Derived from the published package name in the repository, not from a model.

Compare Repository ↗Homepage ↗PyPI ↗

Video walkthroughs

LLMstudio Open Source Project Launch

TensorOps · 2K views · ≈Aug 2024

Third-party YouTube uploads matched to this tool by title, channel and repository name on 2026-08-03. Not made, reviewed or endorsed by SkillPilot. View counts and publish months are as of the match date and the month is approximate. Nothing loads from YouTube until you press play.

What the repository ships

Has docsHas examplesCI configured

Detected from the actual files in the repository root.

README

LLMstudio by TensorOps

Prompt Engineering at your fingertips

LLMstudio logo

🌟 Features

LLMstudio UI

LLM Proxy Access: Seamless access to all the latest LLMs by OpenAI, Anthropic, Google.
Custom and Local LLM Support: Use custom or local open-source LLMs through Ollama.
Prompt Playground UI: A user-friendly interface for engineering and fine-tuning your prompts.
Python SDK: Easily integrate LLMstudio into your existing workflows.
Monitoring and Logging: Keep track of your usage and performance for all requests.
LangChain Integration: LLMstudio integrates with your already existing LangChain projects.
Batch Calling: Send multiple requests at once for improved efficiency.
Smart Routing and Fallback: Ensure 24/7 availability by routing your requests to trusted LLMs.
Type Casting (soon): Convert data types as needed for your specific use case.

🚀 Quickstart

Don't forget to check out https://docs.llmstudio.ai page.

Installation

Install the latest version of LLMstudio using pip. We suggest that you create and activate a new environment using conda

For full version:


pip install 'llmstudio[proxy,tracker]'

For lightweight (core) version:


pip install llmstudio

Create a .env file at the same path you'll run LLMstudio


OPENAI_API_KEY="sk-api_key"
ANTHROPIC_API_KEY="sk-api_key"
VERTEXAI_KEY="sk-api-key"

Now you should be able to run LLMstudio using the following command.


llmstudio server --proxy --tracker

When the --proxy flag is set, you'll be able to access the Swagger at http://0.0.0.0:50001/docs (default port)

When the --tracker flag is set, you'll be able to access the Swagger at http://0.0.0.0:50002/docs (default port)

📖 Documentation

Visit our docs to learn how the SDK works (coming soon)
Checkout our notebook examples to follow along with interactive tutorials

👨‍💻 Contributing

Head on to our Contribution Guide to see how you can help LLMstudio.
Join our Discord to talk with other LLMstudio enthusiasts.

Training

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Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here.