AI Agent

GitHub - karpathy/autoresearch: AI agents automatically conducting research on single-GPU nanochat training

Building AI Coding Agents for the Terminal: Framework, Integration, Context Engineering, and Lessons Learned

GitHub - NousResearch/hermes-agent: The agent that grows with you

GitHub - bytedance/deer-flow: An open-source SuperAgent framework that researches, codes, and creates. With the help of sandboxes,

GitHub - qwibitai/nanoclaw: A lightweight alternative to Clawdbot / OpenClaw that runs in Apple containers for security. Connect

GitHub - aiming-lab/SimpleMem: SimpleMem: Efficient Lifelong Memory for LLM Agents

GitHub - bolt-foundry/gambit: Agent framework for building, running, and verifying LLM workflows

Show HN: Agent-of-Empires: OpenCode and Claude Code Session Manager

You Should Write an Agent · The Fly Blog

Getting Started - SWE-agent Documentation

How to Build an Agent - Amp **Introduction** Building an agent, especially one that leverages the power of Amp, involves several key steps. Amp, which stands for Advanced Multi-Purpose Protocol, is a versatile framework designed to enhance the capabilities of agents in various domains. This guide will walk you through the process of creating an agent using Amp, from conceptualization to deployment. **1. Define the Purpose and Scope** Before diving into the technical details, it's crucial to define the purpose and scope of your agent. Ask yourself the following questions: - What specific tasks will the agent perform? - In what environments will the agent operate? - What are the key performance metrics for success? **2. Choose the Right Tools and Technologies** Selecting the appropriate tools and technologies is essential for building a robust agent. For an Amp-based agent, you might need: - **Programming Languages**: Python, Java, or C++ are commonly used. - **Development Frameworks**: TensorFlow, PyTorch, or custom frameworks compatible with Amp. - **Data Sources**: APIs, databases, or real-time data streams. - **Communication Protocols**: HTTP, WebSockets, or other protocols supported by Amp. **3. Design the Agent Architecture** The architecture of your agent will determine its efficiency and scalability. Consider the following components: - **Input Layer**: Handles data ingestion from various sources. - **Processing Layer**: Processes the data using algorithms and models. - **Output Layer**: Delivers the results to the end-users or other systems. - **Feedback Loop**: Allows the agent to learn and improve over time. **4. Develop the Core Functionality** With the architecture in place, start developing the core functionality of your agent. This includes: - **Data Ingestion**: Implementing mechanisms to collect and preprocess data. - **Algorithm Development**: Creating or integrating algorithms that will drive the agent's decision-making. - **Model Training**: Training machine learning models if applicable. - **Integration**: Ensuring seamless integration with other systems and protocols. **5. Implement Amp Protocols** Integrate Amp protocols into your agent to leverage its advanced capabilities. This might involve: - **Protocol Implementation**: Writing code to adhere to Amp standards. - **Communication**: Ensuring the agent can communicate effectively with other Amp-compatible systems. - **Security**: Implementing security measures to protect data and communications. **6. Testing and Validation** Thoroughly test

GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

Effective harnesses for long-running agents Anthropic

GitHub - GibsonAI/Memori: Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

[2511.10395] AgentEvolver: Towards Efficient Self-Evolving Agent System

Sim: Open-source platform to build and deploy AI agent workflows

Context Retrieval for AI Agents across Apps & Databases

You Should Write An Agent · The Fly Blog

"🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here"

Tongyi DeepResearch: A New Era of Open-Source AI Researchers | Tongyi DeepResearch

Syllabi – Open-source agentic AI with tools, RAG, and multi-channel deploy

OpenSkills

MiniMax-M2

Make Any App Searchable for AI Agents

ROMA: Recursive Open Meta-Agents

Cua: Open-source infrastructure for Computer-Use Agents

If you're late to the whole "memory in AI agents" topic like me, I recommend investing 43 minutes to watch this video

The RAG Obituary: Killed by Agents, Buried by Context Windows

Anthropic releases Claude Sonnet 4.5 in latest bid for AI agents and coding supremacy

Context Engineering for AI Agents: Lessons from Building Manus

AgenticSeek: Private, Local Manus Alternative

Introducing Tongyi Deep Research

AI Engineering Hub

Google just dropped an ace 64-page guide on building AI Agents

opcode - The Elegant Desktop Companion for Claude Code

Enable AI to control your browser 🤖

How to Use Claude Code Subagents to Parallelize Development

Small models are the future of agentic ai

Kimi K2: Open Agentic Intelligence

Scientific Paper Agent with LangGraph

RAGFlow

[2502.12110] A-MEM: Agentic Memory for LLM Agents

[2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

HumanLayer

Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

AI Agents for Beginners - A Course

How to build a coding agent

browser-use/web-ui

Parlant

CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents

Elysia: Agentic Framework Powered by Decision Trees

MCP-Use

Sim

Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

Agentic Design Patterns - Documenti Google

[2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Qwen3-Coder: Agentic coding in the world

FutureHouse Platform

Research Agent with Gemini 2.5 Pro and LlamaIndex  |  Gemini API  |  Google AI for Developers

Opencode: AI coding agent, built for the terminal

The new skill in AI is not prompting, it's context engineering

Judge Rules Training AI on Copyrighted Works Is Fair Use, Agentic Biology Evolves, and more...

Building Effective AI Agents

Designing Pareto-optimal GenAI workflows with syftr

Agent Development Kit (ADK)

Strands Agents

Cua is Docker for Computer-Use AI Agents

GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to deploy?