Repository History
18 repositories tagged with agents
text-to-cad: AI Agent Skills for CAD, CAE, and CAM Workflows
text-to-cad is a comprehensive Python library developed by earthtojake, providing a rich set of agent skills for Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and Computer-Aided Manufacturing (CAM). It enables AI agents to generate, inspect, source, slice, and manage CAD and robot-description artifacts, streamlining complex engineering processes.
AgentFS: The Filesystem Designed for AI Agents and Their State Management
AgentFS is an innovative filesystem specifically engineered for AI agents, providing robust storage abstractions. It leverages SQLite to offer auditability, reproducibility, and portability for agent states, tool calls, and file operations. This solution simplifies debugging, analysis, and deployment of AI agents by encapsulating their entire runtime into a single, queryable database file.
CQ: An Open Standard for Shared Agent Learning by Mozilla.ai
CQ is an open standard designed to prevent AI agents from repeatedly making the same mistakes by enabling them to persist, share, and query collective knowledge. It facilitates a structured exchange of ideas, allowing agents to learn from each other's experiences and accelerate development. This system helps agents avoid redundant debugging and discover solutions more efficiently.
Agent Sandbox: Secure Local Development for AI Coding Agents
Agent Sandbox provides a robust and secure local development environment specifically designed for collaborating with AI coding agents. It ensures minimal filesystem access, configurable network egress policies, and secure secret injection, protecting your local machine from potentially risky agent operations. This project supports various AI agents and integrates seamlessly with both CLI and popular IDE devcontainer setups.
FastMCP: The Pythonic Framework for Model Context Protocol Applications
FastMCP is a robust, Pythonic framework developed by PrefectHQ, designed to simplify the creation of Model Context Protocol (MCP) servers and clients. It provides a comprehensive application framework for connecting Large Language Models (LLMs) to tools and data, handling complexities like schema generation, validation, and protocol lifecycle. As the standard framework for MCP, FastMCP empowers developers to build powerful LLM-integrated applications efficiently.
CubeSandbox: Instant, Concurrent, and Secure Sandbox for AI Agents
CubeSandbox, developed by TencentCloud, is a high-performance, secure sandbox service built on RustVMM and KVM, designed specifically for AI agents. It offers ultra-fast startup times, hardware-level isolation, and high-density deployment, making it ideal for scalable and secure agent execution environments. The service is also fully compatible with the E2B SDK for seamless integration.
DeepFabric: High-Quality Synthetic Data for Agentic AI Systems
DeepFabric is an open-source Python library designed to generate high-quality synthetic training data for language models and agent evaluations. It excels at creating domain-specific datasets that teach models to think, plan, and act effectively, including correct tool usage and adherence to schema structures. This comprehensive pipeline also integrates training and evaluation capabilities, ensuring robust model development.
agentmemory: Persistent Memory for AI Coding Agents
agentmemory provides persistent memory for AI coding agents, ensuring they remember past interactions and project context across sessions. This eliminates the need for re-explaining, significantly boosting agent efficiency and reducing token costs. Built on the `iii engine`, it offers high retrieval accuracy and multi-agent support without external databases.
agent-service-toolkit: A Comprehensive Toolkit for AI Agent Services with LangGraph
The agent-service-toolkit is a full-featured repository for building and running AI agent services. It leverages LangGraph for sophisticated agent logic, FastAPI for a robust service API, and Streamlit for an interactive chat interface. This toolkit provides a comprehensive and robust template for developing and deploying custom AI agents with ease.
Deep Agents: The Batteries-Included Agent Harness for Complex AI Tasks
Deep Agents is an agent harness built on LangChain and LangGraph, designed to simplify the creation of complex AI agents. It comes equipped with essential tools like planning, filesystem access, and the ability to spawn sub-agents, enabling it to handle sophisticated agentic tasks out of the box. This framework provides a ready-to-run agent that can be easily customized with additional tools, models, and prompts.
Company Research Agent: Deep Diligence with Multi-Agent AI and LangGraph
The Company Research Agent is an advanced tool designed for in-depth company diligence, leveraging a multi-agent framework built with LangGraph and Tavily. It efficiently gathers, filters, and synthesizes information from various sources. The system utilizes Google's Gemini 2.5 Flash for high-context synthesis and OpenAI's GPT-5.1 for precise formatting, delivering comprehensive research reports.
Memary: The Open Source Memory Layer for Autonomous Agents
Memary is an innovative open-source memory layer designed to enhance autonomous agents by emulating human memory. It integrates knowledge graphs and memory modules to provide agents with advanced capabilities for reasoning and learning. This project aims to make agents more intelligent and capable of self-improvement.