Framework Integrations

FrameworkDescription
AutoGenFramework from Microsoft building LLM applications using multiple conversational agents.
CanopyFramework from Pinecone for building RAG applications using LLMs and knowledge bases.
Cheshire CatFramework to create personalized AI assistants using custom data.
CrewAICrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks.
DocArrayPython library for managing data in multi-modal AI applications.
DSPyFramework for algorithmically optimizing LM prompts and weights.
dsRAGHigh-performance Python retrieval engine for unstructured data.
FeastOpen-source feature store to operate production ML systems at scale as a set of features.
Fifty-OneToolkit for building high-quality datasets and computer vision models.
GenkitFramework to build, deploy, and monitor production-ready AI-powered apps.
HaystackLLM orchestration framework to build customizable, production-ready LLM applications.
LakechainPython framework for deploying document processing pipelines on AWS using infrastructure-as-code.
LangchainPython framework for building context-aware, reasoning applications using LLMs.
Langchain-GoGo framework for building context-aware, reasoning applications using LLMs.
Langchain4jJava framework for building context-aware, reasoning applications using LLMs.
LangGraphPython, Javascript libraries for building stateful, multi-actor applications.
LlamaIndexA data framework for building LLM applications with modular integrations.
Mem0Self-improving memory layer for LLM applications, enabling personalized AI experiences.
MemGPTSystem to build LLM agents with long term memory & custom tools
Neo4j GraphRAGPackage to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python.
Pandas-AIPython library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language
RagbitsPython package that offers essential “bits” for building powerful Retrieval-Augmented Generation (RAG) applications.
Rig-rsRust library for building scalable, modular, and ergonomic LLM-powered applications.
Semantic RouterPython library to build a decision-making layer for AI applications using vector search.
Spring AIJava AI framework for building with Spring design principles such as portability and modular design.
SuperduperFramework for building flexible, compositional AI apps which may be applied directly to databases.
SwarmPython framework for managing multiple AI agents that can work together.
SycamoreDocument processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data.
TestcontainersFramework for providing throwaway, lightweight instances of systems for testing
txtaiPython library for semantic search, LLM orchestration and language model workflows.
Vanna AIPython RAG framework for SQL generation and querying.
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