DocsGPT
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.
Open-source Zi Wei Dou Shu (Purple Star Astrology) chart-calculation engine based on Ni Haixia's Tianji system, with a complete charting algorithm, the Four Transformations system, a pattern knowledge base and original classical-text data.
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
A vector index built on TurboQuant, written in Rust with Python bindings.
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
Agent framework and applications built upon Qwen>=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Open-source NotebookLM alternative. Research the open web with live data(Reddit, YT, IG, TikTok, Indeed, Google Search, Maps etc) through one platform, API or MCP server. Join our Discord: https://discord.gg/ejRNvftDp9
A lightweight, lightning-fast, in-process vector database
Mainly documents knowledge and interview questions for LLM algorithm (application) engineers.
A powerful tool for creating datasets for LLM fine-tuning, RAG and evaluation.
ConardLi's open-source Skills collection, featuring web design, knowledge retrieval, image generation, and more.
Unified framework for building enterprise RAG pipelines with small, specialized models
Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots.
Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. Maintained by Orchestra Research.
A tutorial on building LLM applications aimed at beginner developers. Read online: https://datawhalechina.github.io/llm-universe/
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Your AI trading terminal assistant for US stocks, commodities, forex, and crypto.
Practical LLM application development, part one: a full-stack guide to RAG technology. Read online: https://datawhalechina.github.io/all-in-rag/
A cross-platform Markdown AI note-taking software. It is to make sure useful fragments do not disappear before you have time to turn them into something complete.