Skip to main content

Prompt Perfection at Your Fingertips

Project description

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

👨‍💻 Contributing

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

Training

Banner


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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llmstudio-1.0.5.tar.gz (6.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llmstudio-1.0.5-py3-none-any.whl (8.2 kB view details)

Uploaded Python 3

File details

Details for the file llmstudio-1.0.5.tar.gz.

File metadata

  • Download URL: llmstudio-1.0.5.tar.gz
  • Upload date:
  • Size: 6.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.3 Linux/6.8.0-1021-azure

File hashes

Hashes for llmstudio-1.0.5.tar.gz
Algorithm Hash digest
SHA256 15b14dfd89ea2d8b246f7d4e2da4e90e17adaabe008e0162a01d1f33b1d60702
MD5 60736e6853626fe63bdadc08cbcfb119
BLAKE2b-256 319537cb2a9cd0b1bc34b54d7ae22b065185519d1f60034f285142305989ed5c

See more details on using hashes here.

File details

Details for the file llmstudio-1.0.5-py3-none-any.whl.

File metadata

  • Download URL: llmstudio-1.0.5-py3-none-any.whl
  • Upload date:
  • Size: 8.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.3 Linux/6.8.0-1021-azure

File hashes

Hashes for llmstudio-1.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 9b2f28bc5f1dcb50a26c2dce496319b4f40de4e25fc849d142a2c076a1daee9b
MD5 5a3e47b1be951faa97618fc4d5fa4fbc
BLAKE2b-256 ccb6751af479d315e63cf78c83dd3f5f948188fa618df35822ef5e6832a74fba

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page