
LLMstudio
Framework to bring LLM applications to production
Stars: 311

LLMstudio by TensorOps is a platform that offers prompt engineering tools for accessing models from providers like OpenAI, VertexAI, and Bedrock. It provides features such as Python Client Gateway, Prompt Editing UI, History Management, and Context Limit Adaptability. Users can track past runs, log costs and latency, and export history to CSV. The tool also supports automatic switching to larger-context models when needed. Coming soon features include side-by-side comparison of LLMs, automated testing, API key administration, project organization, and resilience against rate limits. LLMstudio aims to streamline prompt engineering, provide execution history tracking, and enable effortless data export, offering an evolving environment for teams to experiment with advanced language models.
README:
LLMstudio by TensorOps
Prompt Engineering at your fingertips
- 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.
Don't forget to check out https://docs.llmstudio.ai page.
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)
- Visit our docs to learn how the SDK works (coming soon)
- Checkout our notebook examples to follow along with interactive tutorials
- Head on to our Contribution Guide to see how you can help LLMstudio.
- Join our Discord to talk with other LLMstudio enthusiasts.
Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here.
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