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EnterpriseBench is a comprehensive framework designed to evaluate Large Language Model (LLM) agents within realistic enterprise environments. It provides a complete ecosystem for generating, simula…
Training and Evaluating LLM Agents in Enterprise Environments
AI agents running research on single-GPU nanochat training automatically
Asynchronous LLM Agent playing games of Mafia against human players
Fast, small, and fully autonomous AI personal assistant infrastructure, ANY OS, ANY PLATFORM — deploy anywhere, swap anything 🦀
Assignments for CS146S: The Modern Software Dev (Stanford University Fall 2025)
Harbor is a framework for running agent evaluations and creating and using RL environments.
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
🚀 The fast, Pythonic way to build MCP servers and clients.
The official Python SDK for Model Context Protocol servers and clients
MCP Server for interacting with Salesforce instances
Model Context Protocol (MCP) server that enables AI assistants to securely interact with Odoo ERP systems through standardized resources and tools for data retrieval and manipulation.
Video+code lecture on building nanoGPT from scratch
Build effective agents using Model Context Protocol and simple workflow patterns
Agent2Agent (A2A) is an open protocol enabling communication and interoperability between opaque agentic applications.
MCP-Universe is a comprehensive framework designed for RL training, benchmarking, and developing AI agents for general tool-use.
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
Free and Open Source Enterprise Resource Planning (ERP)
Official repo of Toucan: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments
[ICLR 2026] The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
Crack LeetCode, not only how, but also why.