#jupyter_notebook #agent_based_framework #agent_oriented_programming #agentic #agentic_agi #chat #chat_application #chatbot #chatgpt #gpt #gpt_35_turbo #gpt_4 #llm_agent #llm_framework #llm_inference #llmops

AutoGen is a tool that helps you build AI systems where agents can work together and perform tasks on their own or with human help. It makes it easier to create scalable, distributed, and resilient AI applications. Here are the key benefits Agents can talk to each other using asynchronous messages.
- **Scalable** You can add your own agents, tools, and models to the system.
- **Multi-Language Support** It includes features to track and debug how the agents interact.

Using AutoGen, you can develop and test your AI systems locally and then move them to a cloud environment as needed. This makes it simpler to build and manage advanced AI projects.

https://github.com/microsoft/autogen

#jupyter_notebook #agentic_ai #agentic_framework #agentic_rag #ai_agents #ai_agents_framework #autogen #generative_ai #semantic_kernel

This course helps you learn about AI Agents from the basics to advanced levels. AI Agents are systems that use large language models to perform tasks by accessing tools and knowledge. The course includes 10 lessons covering topics like agent fundamentals, frameworks, and use cases. It provides code examples and supports multiple languages. By completing this course, you can build your own AI Agents and apply them in various applications, such as customer support or event planning, making complex tasks easier and more efficient.

https://github.com/microsoft/ai-agents-for-beginners

#jupyter_notebook #agentic_ai #agents #course #huggingface #langchain #llamaindex #smolagents

The Hugging Face Agents Course is a free, interactive course that teaches you how to build and deploy AI agents. It's divided into four units, starting with the basics of agents and ending with a final project where you create and test your own agent. You'll learn about frameworks like `smolagents`, `LangGraph`, and `LlamaIndex`, and how to use large language models (LLMs) in your agents. The course benefits you by providing hands-on experience and practical skills in AI agent development, helping you become proficient in creating and deploying AI agents.

https://github.com/huggingface/agents-course

#jupyter_notebook #a2a #agentic_ai #dapr #dapr_pub_sub #dapr_service_invocation #dapr_sidecar #dapr_workflow #docker #kafka #kubernetes #langmem #mcp #openai #openai_agents_sdk #openai_api #postgresql_database #rabbitmq #rancher_desktop #redis #serverless_containers

The Dapr Agentic Cloud Ascent (DACA) design pattern helps you build powerful, scalable AI systems that can handle millions of AI agents working together without crashing. It uses Dapr technology with Kubernetes to efficiently manage many AI agents as lightweight virtual actors, ensuring fast response, reliability, and easy scaling. You can start small using free or low-cost cloud tools and grow to planet-scale systems. The OpenAI Agents SDK is recommended for beginners because it is simple, flexible, and gives you good control to develop AI agents quickly. This approach saves costs, avoids vendor lock-in, and supports resilient, event-driven AI workflows, making it ideal for developers aiming to create advanced, cloud-native AI applications[1][2][3][4].

https://github.com/panaversity/learn-agentic-ai

#python #agentic_ai #agents #ai #autonomous_agents #deepseek_r1 #llm #llm_agents #voice_assistant

AgenticSeek is a free, fully local AI assistant that runs entirely on your own computer, ensuring your data stays private with no cloud or API use. It can autonomously browse the web, write and debug code in many languages, plan and execute complex tasks, and even respond to voice commands. It smartly chooses the best AI agent for each task, making it like having a personal team of experts. This local setup avoids monthly fees and protects your privacy while giving you powerful AI help for coding, research, and task management all on your device[1][2].

https://github.com/Fosowl/agenticSeek

#python #agent #agentic_ai #grpo #kimi_ai #llms #lora #qwen #qwen3 #reinforcement_learning #rl

ART is a tool that helps you train smart agents for real-world tasks using reinforcement learning, especially with the GRPO method. The standout feature is RULER, which lets you skip the hard work of designing reward functions by using a large language model to automatically score how well your agent is doing—just describe your task, and RULER takes care of the rest. This makes building and improving agents much faster and easier, works for any task, and often performs as well as or better than hand-crafted rewards. You can install ART with a simple command and start training agents right away, even on your own computer or with cloud resources.

https://github.com/OpenPipe/ART

#typescript #agent_workflow #agentic_workflow #agents #ai #aiagents #anthropic #artificial_intelligence #automation #chatbot #deepseek #gemini #low_code #nextjs #no_code #openai #rag #react #typescript

Sim Studio is an easy-to-use, open-source platform that lets you build AI workflows visually without coding by dragging and connecting blocks on a canvas. It supports many AI models and integrates with over 60 popular tools like Gmail, Slack, and Google Sheets. You can run workflows via chat, APIs, or scheduled jobs and deploy them as APIs or plugins. It also offers real-time collaboration and built-in monitoring. This helps you quickly create, test, and deploy AI-powered applications or automation, saving time and effort while allowing flexibility and control over your AI projects[1][2][3][4].

https://github.com/simstudioai/sim

#python #agentic_code #agentic_coding #ai_workflow_optimization #ai_workflows #anthropic #anthropic_claude #awesome #awesome_list #awesome_lists #awesome_resources #claude #claude_code #coding_agent #coding_agents #coding_assistant

This repository is a collection of resources to enhance your workflow with Claude Code, a powerful coding assistant. It includes **slash-commands**, **tooling**, **hooks**, and **CLAUDE.md files** that help you manage projects, automate tasks, and improve code quality. The repository is community-driven, allowing users to share and discover new ways to use Claude Code effectively. By contributing to this list, you can help others and improve your own productivity with Claude Code.

https://github.com/hesreallyhim/awesome-claude-code

#typescript #agentic_ai #ai #flow_based_programming #visual_ai #visual_programming #visual_programming_editor #visual_programming_language #vscode #vscode_extension

Flyde is a free, open-source tool that lets you build and manage AI workflows visually inside your existing TypeScript codebase using VS Code. It helps you create, test, and improve complex backend AI logic like AI agents and prompt chains with a clear visual interface, making it easier for both developers and non-developers to collaborate. Flyde integrates directly with your code and tools, so you keep full control while simplifying development and debugging. This saves time, reduces errors, and improves teamwork on AI-powered backend projects.

https://github.com/flydelabs/flyde

#python #agent #agentic #agentic_ai #agents #agents_sdk #ai #ai_agents #aiagentframework #genai #genai_chatbot #llm #llms #multi_agent #multi_agent_systems #multi_agents #multi_agents_collaboration

The Agent Development Kit (ADK) is an open-source Python toolkit that helps you easily build, test, and deploy smart AI agents, from simple helpers to complex multi-agent systems. It lets you write agent logic in Python, use many built-in or custom tools, and organize multiple agents to work together. You can deploy agents anywhere, including Google Cloud, and evaluate their performance with built-in tools. ADK supports flexible workflows and works with various AI models, not just Google’s. This means you get full control and flexibility to create powerful AI applications that fit your needs, speeding up development and making it easier to manage AI projects.

https://github.com/google/adk-python