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

title, weight

title	weight
Frameworks	20

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.
DocArray	Python library for managing data in multi-modal AI applications.
DSPy	Framework for algorithmically optimizing LM prompts and weights.
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.
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.
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
Pandas-AI	Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language
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.
txtai	Python library for semantic search, LLM orchestration and language model workflows.
Vanna AI	Python RAG framework for SQL generation and querying.