#shell #ai_development_tools #awesome_list #llmops #mlops

https://github.com/tensorchord/Awesome-LLMOps

#python #ai #automl #data_science #deep_learning #devops_tools #hacktoberfest #llm #llmops #machine_learning #metadata_tracking #ml #mlops #pipelines #production_ready #pytorch #tensorflow #workflow #zenml

https://github.com/zenml-io/zenml

#python #ai #ai_alignment #ai_safety #ai_test #ai_testing #artificial_intelligence #cicd #explainable_ai #llmops #machine_learning #machine_learning_testing #ml #ml_safety #ml_test #ml_testing #ml_validation #mlops #model_testing #model_validation #quality_assurance

https://github.com/Giskard-AI/giskard

#jupyter_notebook #ai #aihub #argo #automl #gpt #inference #kubeflow #kubernetes #llmops #mlops #notebook #pipeline #pytorch #spark #vgpu #workflow

https://github.com/tencentmusic/cube-studio

#typescript #agent #ai #anthropic #backend_as_a_service #chatbot #gemini #genai #gpt #gpt_4 #llama3 #llm #llmops #nextjs #openai #orchestration #python #rag #workflow #workflows

Dify is an open-source platform for developing AI applications, especially those using Large Language Models (LLMs). It offers a user-friendly interface to build and test AI workflows, integrate various LLMs, and manage models. Key features include a visual workflow builder, comprehensive model support (including GPT, Mistral, and more), a prompt IDE for crafting and testing prompts, RAG pipeline capabilities for document ingestion and retrieval, and agent capabilities with pre-built tools like Google Search and DALL·E.

Using Dify, you can quickly move from prototyping to production with features like observability to monitor application performance and backend-as-a-service for easy integration into your business logic. You can deploy Dify via their cloud service or self-host it in your environment. This makes it highly versatile and beneficial for developers looking to leverage AI efficiently in their projects.

https://github.com/langgenius/dify

#python #amd #cuda #gpt #inference #inferentia #llama #llm #llm_serving #llmops #mlops #model_serving #pytorch #rocm #tpu #trainium #transformer #xpu

vLLM is a library that makes it easy, fast, and cheap to use large language models (LLMs). It is designed to be fast with features like efficient memory management, continuous batching, and optimized CUDA kernels. vLLM supports many popular models and can run on various hardware including NVIDIA GPUs, AMD CPUs and GPUs, and more. It also offers seamless integration with Hugging Face models and supports different decoding algorithms. This makes it flexible and easy to use for anyone needing to serve LLMs, whether for research or other applications. You can install vLLM easily with `pip install vllm` and find detailed documentation on their website.

https://github.com/vllm-project/vllm

#python #ai #aws #developer_tools #gpt_4 #llm #llmops #python

Phidata is a tool that helps you build smart AI agents with memory, knowledge, tools, and reasoning. You can use it to create agents that can search the web, get financial data, or even write and run Python code. Here’s how it benefits you You can install Phidata using a simple command `pip install -U phidata`.
- **Versatile Agents** Agents can use reasoning to solve problems step-by-step and access knowledge bases to provide accurate information.
- **User-Friendly Interface** It includes built-in monitoring and debugging tools to help you track and fix issues with your agents.

Overall, Phidata makes it easy to create and manage intelligent AI agents that can perform complex tasks efficiently.

https://github.com/phidatahq/phidata

#jupyter_notebook #agent_based_framework #agent_oriented_programming #agentic #agentic_agi #chat #chat_application #chatbot #chatgpt #gpt #gpt_35_turbo #gpt_4 #llm_agent #llm_framework #llm_inference #llmops

AutoGen is a tool that helps you build AI systems where agents can work together and perform tasks on their own or with human help. It makes it easier to create scalable, distributed, and resilient AI applications. Here are the key benefits Agents can talk to each other using asynchronous messages.
- **Scalable** You can add your own agents, tools, and models to the system.
- **Multi-Language Support** It includes features to track and debug how the agents interact.

Using AutoGen, you can develop and test your AI systems locally and then move them to a cloud environment as needed. This makes it simpler to build and manage advanced AI projects.

https://github.com/microsoft/autogen

#python #ai_gateway #anthropic #azure_openai #bedrock #gateway #langchain #llm #llm_gateway #llmops #openai #openai_proxy #vertex_ai

LiteLLM is a tool that helps you use different AI models from various providers like OpenAI, Azure, and Huggingface in a simple way. Here’s how it benefits you You can call any AI model using a consistent format, making it easier to switch between different providers.
- **Consistent Output** You can set budgets and rate limits for your projects, helping you manage costs and usage efficiently.
- **Retry and Fallback Logic** It supports streaming responses and asynchronous calls, which can improve performance.
- **Logging and Observability**: You can easily log data to various tools like Lunary, Langfuse, and Slack, helping you monitor and analyze your AI usage.

Overall, LiteLLM simplifies working with multiple AI providers, makes your code cleaner, and helps you manage resources better.

https://github.com/BerriAI/litellm

#rust #agent #ai #artificial_intelligence #automation #generative_ai #large_language_model #llm #llmops #rust #scalable_ai

Rig is a Rust library that helps you build apps using Large Language Models (LLMs) like OpenAI and Cohere. It makes it easy to integrate these models into your application with minimal code. Rig supports various vector stores like MongoDB and Neo4j, and it provides simple but powerful tools to work with LLMs. To get started, you can add Rig to your project using `cargo add rig-core` and follow the examples provided. This library is constantly improving, so your feedback is valuable. Using Rig can save you time and effort by providing a straightforward way to use LLMs in your projects.

https://github.com/0xPlaygrounds/rig

#typescript #agent_monitoring #analytics #evaluation #gpt #langchain #large_language_models #llama_index #llm #llm_cost #llm_evaluation #llm_observability #llmops #monitoring #open_source #openai #playground #prompt_engineering #prompt_management #ycombinator

Helicone is an all-in-one, open-source platform for developing and managing Large Language Models (LLMs). It allows you to integrate with various LLM providers like OpenAI, Anthropic, and more with just one line of code. You can observe and debug your model's performance, analyze metrics such as cost and latency, and fine-tune your models easily. The platform also offers a playground to test and iterate on prompts and sessions, and it supports prompt management and automatic evaluations. Helicone is enterprise-ready, compliant with SOC 2 and GDPR, and offers a generous free tier of 100k requests per month. This makes it easier to manage and optimize your LLM projects efficiently.

https://github.com/Helicone/helicone

#other #chatbot #hugging_face #llm #llm_local #llm_prompting #llm_security #llmops #machine_learning #open_ai #pathway #rag #real_time #retrieval_augmented_generation #vector_database #vector_index

Pathway's AI Pipelines help you quickly create and deploy AI applications with high accuracy. These pipelines use the latest knowledge from your data sources and offer ready-to-deploy templates for large language models. You can test these apps on your own machine and deploy them on cloud services like GCP, AWS, or Azure, or on-premises. The apps connect to various data sources such as file systems, Google Drive, and databases, and they include built-in data indexing for efficient searches. This makes it easy to extract and organize data from documents in real-time, reducing the need for separate infrastructure setups. This simplifies the process of building and maintaining AI applications, saving you time and effort.

https://github.com/pathwaycom/llm-app

#python #cloud_native #cncf #deep_learning #docker #fastapi #framework #generative_ai #grpc #jaeger #kubernetes #llmops #machine_learning #microservice #mlops #multimodal #neural_search #opentelemetry #orchestration #pipeline #prometheus

Jina-serve is a tool that helps you build and deploy AI services easily. It supports major machine learning frameworks and allows you to scale your services from local development to production quickly. You can use it to create AI services that communicate via gRPC, HTTP, and WebSockets. It has features like built-in Docker integration, one-click cloud deployment, and support for Kubernetes and Docker Compose, making it easy to manage and scale your AI applications. This makes it simpler for you to focus on the core logic of your AI projects without worrying about the technical details of deployment and scaling.

https://github.com/jina-ai/serve

#python #agents #ai #ai_agents #aiagents #developer_tools #function_calling #gpt_4 #gpt_4o #hacktoberfest #hacktoberfest2024 #javascript #js #llm #llmops #python #typescript

Composio is a powerful tool that helps AI agents work with many different apps and services. It supports over 250 tools, including popular ones like GitHub, Gmail, and Salesforce. Composio makes it easy to manage authentication across multiple accounts, which means you can securely connect your AI agents to various platforms without worrying about security issues. This integration enhances productivity by automating tasks and streamlining workflows, making it easier for developers and users to get more out of their AI tools.

https://github.com/ComposioHQ/composio

#typescript #ai #analytics #datasets #dspy #evaluation #gpt #llm #llmops #low_code #observability #openai #prompt_engineering

LangWatch helps you monitor, test, and improve AI applications by tracking performance, comparing different setups, and optimizing prompts automatically. It works with any AI tool or framework, keeps your data secure, and lets you collaborate with experts to fix issues quickly, making your AI more reliable and efficient.

https://github.com/langwatch/langwatch