| AutoGen | Framework from Microsoft building LLM applications using multiple conversational agents. |
| Camel | Framework to build and use LLM-based agents for real-world task solving |
| Canopy | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
| Cheshire Cat | Framework to create personalized AI assistants using custom data. |
| CrewAI | CrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks. |
| DocArray | Python library for managing data in multi-modal AI applications. |
| DSPy | Framework for algorithmically optimizing LM prompts and weights. |
| dsRAG | High-performance Python retrieval engine for unstructured data. |
| Feast | Open-source feature store to operate production ML systems at scale as a set of features. |
| Fifty-One | Toolkit for building high-quality datasets and computer vision models. |
| Genkit | Framework to build, deploy, and monitor production-ready AI-powered apps. |
| Haystack | LLM orchestration framework to build customizable, production-ready LLM applications. |
| Lakechain | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
| Langchain | Python framework for building context-aware, reasoning applications using LLMs. |
| Langchain-Go | Go framework for building context-aware, reasoning applications using LLMs. |
| Langchain4j | Java framework for building context-aware, reasoning applications using LLMs. |
| LangGraph | Python, Javascript libraries for building stateful, multi-actor applications. |
| LlamaIndex | A data framework for building LLM applications with modular integrations. |
| Mastra | Typescript framework to build AI applications and features quickly. |
| Mem0 | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
| MemGPT | System to build LLM agents with long term memory & custom tools |
| Neo4j GraphRAG | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
| Pandas-AI | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
| Ragbits | Python package that offers essential “bits” for building powerful Retrieval-Augmented Generation (RAG) applications. |
| Rig-rs | Rust library for building scalable, modular, and ergonomic LLM-powered applications. |
| Semantic Router | Python library to build a decision-making layer for AI applications using vector search. |
| Spring AI | Java AI framework for building with Spring design principles such as portability and modular design. |
| Superduper | Framework for building flexible, compositional AI apps which may be applied directly to databases. |
| Swarm | Python framework for managing multiple AI agents that can work together. |
| Sycamore | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
| Testcontainers | Framework for providing throwaway, lightweight instances of systems for testing |
| txtai | Python library for semantic search, LLM orchestration and language model workflows. |
| Vanna AI | Python RAG framework for SQL generation and querying. |
