landing_page/qdrant-landing/content/documentation/frameworks/_index.md

title, weight, partition

title	weight	partition
Frameworks	20	build

Framework Integrations

Framework	Description
AutoGen	Framework from Microsoft building LLM applications using multiple conversational agents.
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.
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.
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.
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.