Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.

Qt C++ library for working with AI/LLM Providers and MCP

A Python wrapper for the Chemistry Development Kit (CDK)

Python, LlamaIndex, LangChain, 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco, Nuxeo DBs. 14 data sources (10 auto-sync), KG auto-building, Ontologies, LLMs, Docling, LlamaParse, LiteParse, GraphRAG, RAG, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, REST, MCP Server. Options: Langflow, CocoIndex

Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation

Transparent and Efficient Financial Analysis

🌐Web Agent Protocol (WAP) - Record and replay user interactions in the browser with MCP support

🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!

Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift

Pixelize the real world on-chain

A chatbot implementation compatible with MCP (terminal / streamlit supported)

AI said it finished. Flyto2 shows the proof.

Solid Tumor Associative Modeling in Pathology

The python library for research and development in NLP, multimodal LLMs, Agents, ML, Knowledge Graphs, and more.

Open source version of Claude Managed Agents. Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent.

Universal memory runtime for AI agents

Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support

Umbrella package for SciTeX — reproducible science from raw data to manuscript

Connect AI agents across any network — zero config, encrypted, skill-based routing

Open-source AI assistant ecosystem with MCP integrations, multimodal workflows, IoT support, and cross-platform voice interaction.

A modular Python framework implementing the Model Context Protocol (MCP). It features a standardized client-server architecture over StdIO, integrating LLMs with external tools, real-time weather data fetching, and an advanced RAG (Retrieval-Augmented Generation) system.

Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization

Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java26. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.