Stars
MCPMark is a comprehensive, stress-testing MCP benchmark designed to evaluate model and agent capabilities in real-world MCP use.
Salesforce Enterprise Deep Research
MCP-Universe is a comprehensive framework designed for developing, testing, and benchmarking AI agents
A Survey of Reinforcement Learning for Large Reasoning Models
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
基于多智能体LLM的中文金融交易框架 - TradingAgents中文增强版
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Eigent: The Open Source Cowork Desktop to Unlock Your Exceptional Productivity.
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & TIS & vLLM & Ray & Async RL)
verl: Volcano Engine Reinforcement Learning for LLMs
Agent framework and applications built upon Qwen>=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
🦜🔗 The platform for reliable agents.
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
[ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
A curated list of papers in the intersection of multimodal LLMs and time series analysis. https://mllm-ts.github.io/paper/MLLMTS_Survey.pdf
Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.
Official implementation for "Mixture of In-Context Experts Enhance LLMs’ Awareness of Long Contexts" (Accepted by Neurips2024)
MoBA: Mixture of Block Attention for Long-Context LLMs
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
catch22: CAnonical Time-series CHaracteristics