Yuxi: Multi-tenant agent harness integrating LightRAG, knowledge graphs, and MCP
Yuxi is an open-source multi-tenant agent harness platform that combines a LightRAG knowledge base with knowledge graph management. Built on LangChain, Vue, and FastAPI, it supports DeepAgents, MinerU PDF parsing, Neo4j, and the Model Context Protocol (MCP). The project has accumulated 5,451 GitHub stars with modest daily traction (+47).
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LibreChat: open-source multi-provider chat interface with agents and MCP support
LibreChat is an open-source, self-hostable ChatGPT-style interface supporting a wide range of model providers including OpenAI, Anthropic, DeepSeek, Mistral, Google Vertex AI, and others. It includes agent capabilities, MCP (Model Context Protocol) integration, code interpreter, DALL-E-3, and multi-user authentication. The project has accumulated over 38,600 GitHub stars with active daily growth, indicating significant community adoption.
Multica: Open-Source Managed Agents Platform for Coding Agents
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Onyx: Open Source AI Chat Platform with Multi-LLM Support
Onyx is an open-source AI chat platform written in Python that supports multiple LLMs with advanced features. The repository has accumulated 29,665 total stars with modest daily traction (+28 today). It positions itself as an enterprise-ready AI assistant that integrates with various language model backends.
agent-teams-ai: multi-agent orchestration framework with kanban-style oversight
A TypeScript open-source project on GitHub implements a multi-agent system where autonomous agents handle tasks, communicate with each other, and review each other's work, while the user supervises via a kanban board. The framework supports 200+ models across 75+ LLM providers including Codex, Claude, and OpenCode. It has accumulated 1,189 stars with 56 added today, suggesting growing community interest.
Agent-Reach: open-source CLI tool giving AI agents multi-platform web access without API fees
Agent-Reach is an open-source Python CLI tool that enables AI agents to read and search across Twitter, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu without requiring API keys or fees. The project has accumulated over 21,000 GitHub stars with 127 added today, indicating significant community traction. It addresses a common friction point in agent development: accessing real-time web content across multiple platforms.
phodal/routa: Workspace-First Multi-Agent Coordination Platform with MCP/ACP/A2A Support
Routa is an open-source TypeScript project providing a workspace-first multi-agent coordination platform for AI development. It features shared Specs, Kanban-style orchestration, and support for multiple agent communication protocols including MCP, ACP, and A2A across web and desktop environments. The repository has gained significant traction with 1,136 total stars and 141 stars added today, signaling community interest in multi-agent tooling.
Agents-K1: End-to-end knowledge orchestration pipeline for agent-native scientific knowledge graphs
Agents-K1 is a new pipeline that converts raw scientific documents into structured knowledge graphs for use by LLM-based research agents, addressing the gap where existing systems reduce papers to abstracts and flat citation edges. The system integrates a multimodal parser, a 4B information-extraction model trained with GRPO, and a tri-source agent interface combining web search, graph retrieval, and cross-document traversal. The authors process 2.46 million scientific papers to produce Scholar-KG, releasing a one-million-paper subset. Experiments show improvements in scientific information extraction, knowledge graph construction, and multi-hop reasoning.
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
Alibaba's Qwen team describes an agent built on Qwen2 (8k native context) that processes documents up to 1M tokens by decomposing retrieval and reasoning tasks, reportedly outperforming both RAG pipelines and native long-context models. The agent framework was also used to generate synthetic training data for fine-tuning new long-context Qwen models, creating a self-improvement loop. This positions agent-based context extension as a practical alternative to architectural long-context training.
