- Composio: Streamline agent development with tool integrations.
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
This article features a curated list of the top data science articles published in July, covering topics such as LLM apps, chatGPT, data visualization, multi-agent AI systems, and essential data science skills for 2024.
Chip Huyen analyzed 845 open source AI tool repositories on GitHub using keywords like gpt, llm, and generative ai. He categorized these into infrastructure, model development, application development, and applications layers. There was significant growth in application development and model development layers in 2023, with popular applications being coding, bots, and info aggregation. Tools like Qdrant, Pinecone, and LanceDB emerged in infrastructure layer. Notable contributors include lucidrains, ggerganov, Illyasviel, xtekky, and RNN-based models like RWKV.
