#typescript #12_factor #12_factor_agents #agents #ai #context_window #framework #llms #memory #orchestration #prompt_engineering #rag

The 12-Factor Agents are a set of proven principles to build reliable, scalable, and maintainable AI applications powered by large language models (LLMs). They help you combine the creativity of AI with the stability of traditional software by managing prompts, context, tool calls, error handling, and human collaboration effectively. Instead of relying solely on complex frameworks, you can apply these modular concepts to improve your existing products quickly and reach high-quality AI performance for real users. This approach makes AI software easier to develop, debug, and scale, ensuring it works well in production environments[1][3][5].

https://github.com/humanlayer/12-factor-agents

#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 #agents #ai #anthropic #llm #openai #python

You can use this Cookbook to quickly add ready-made AI code snippets to your projects, saving you time and effort in building AI systems. It offers practical tutorials and resources to help you learn AI development, start freelancing, or get expert help on your AI projects. Joining the free community can support your learning, and the GenAI Launchpad helps you build AI applications faster. This means you can develop real-world AI solutions more easily and grow your skills or business with guidance from an experienced AI engineer.

https://github.com/daveebbelaar/ai-cookbook

#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

#python #adk #agent_samples #agents

The Agent Development Kit (ADK) offers ready-made sample agents in Python and Java to help you quickly build AI-powered agents for various tasks, from simple chatbots to complex multi-agent workflows. It supports flexible design, letting you combine multiple specialized agents, use diverse tools, and create adaptable workflows. ADK also includes developer tools for easy testing, debugging, and deployment, and works well with Google’s AI models and other large language models. Using these samples can save you time and effort by providing practical examples and a strong foundation to develop your own intelligent agents efficiently. This helps you focus on your agent’s logic while ADK handles orchestration and scaling.

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

#typescript #agentic_ai #agents #ai #claude #copilot #cursor #git #llm #mcp

GitMCP is a free, open-source service that connects AI assistants to any GitHub project’s latest documentation and code using the Model Context Protocol (MCP). This means your AI can access up-to-date, accurate information directly from the source, reducing mistakes and hallucinations when coding or asking questions about libraries, even new or niche ones. You just add a GitMCP URL for your chosen GitHub repo to your AI tool, and it fetches relevant docs and code smartly without setup hassle. This helps you get reliable code examples and API usage instantly, improving your coding efficiency and accuracy. It’s private, easy to use, and works with many AI assistants.

https://github.com/idosal/git-mcp

#python #agents #ai #ai_ux #autogen #browser_use #computer_use_agent #cua #ui

Magentic-UI is a tool that helps you automate complex web tasks by working together with you. It lets you plan step-by-step actions, watch the progress, and approve sensitive steps to keep control and safety. You can interact with it through a browser, upload files, and even run multiple tasks at once. It learns from past tasks to improve future automation. This means you save time on repetitive or complicated web activities while staying in control, making your work easier and more efficient. It supports Python 3.10+ and works best with Docker or WSL2 on Windows.

https://github.com/microsoft/magentic-ui

#python #agents #ai #api_gateway #asyncio #authentication_middleware #devops #docker #fastapi #federation #gateway #generative_ai #jwt #kubernetes #llm_agents #mcp #model_context_protocol #observability #prompt_engineering #python #tools

The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure.

https://github.com/IBM/mcp-context-forge

#typescript #agent #agentic_ai #agents #ai #ai_agents #ai_tools #anthropic #automation #bytebot #computer_use #computer_use_agent #cua #desktop #desktop_automation #docker #gemini #llm #mcp #openai

Bytebot is an open-source AI desktop agent that acts like a virtual employee with its own computer, able to use real applications, browse websites, handle passwords, and process documents automatically. You just describe tasks in plain English, and Bytebot completes them by clicking, typing, downloading files, organizing data, and running complex workflows across multiple programs. It runs locally on your own infrastructure, ensuring privacy and full control, and supports many AI models. This helps you save time by automating repetitive or complex tasks without scripting, improving efficiency and accuracy in business, research, or development work.

https://github.com/bytebot-ai/bytebot

#typescript #agentic_ai #agentic_workflow #agents #ai #approval_process #escalation_policy #function_calling #human_as_tool #human_in_the_loop #humanlayer #llm #llms

HumanLayer helps you safely use AI agents to automate important tasks by ensuring a human always reviews high-risk actions, like sending emails or changing private data. This is crucial because AI can make mistakes or create wrong outputs, and some tasks are too sensitive to trust AI alone. HumanLayer’s tools guarantee human oversight in these cases, so you get the benefits of AI automation without risking errors in critical work. This makes AI more reliable and useful for automating complex workflows while keeping control and safety in your hands.

https://github.com/humanlayer/humanlayer

#kotlin #agentframework #agentic_ai #agents #ai #aiagentframework #android_ai #anthropic #generative_ai #java #jvm #kotlin #ktor #llm #mcp #ollama #openai #spring

Koog is a Kotlin-based open-source framework that helps you build AI agents fully in Kotlin, making it easy to create smart assistants that can use tools, manage complex tasks, and remember past interactions. It supports multiple AI models like OpenAI and Google, runs on many platforms (JVM, JavaScript, iOS), and offers features like real-time streaming, custom tools, and efficient memory use. Koog also provides debugging tools, flexible workflows, and scales from simple chatbots to enterprise systems. Using Koog lets you develop powerful, maintainable AI agents quickly and naturally within the Kotlin ecosystem, benefiting your projects with speed, flexibility, and strong integration options.

https://github.com/JetBrains/koog

#python #agents #ai #llm #mcp

You can access a large collection of ready-to-use AI agent projects and tutorials that help you build smart applications like chatbots, research assistants, and automation tools using popular AI frameworks such as LangChain, OpenAI Agents SDK, and Agno. This collection includes simple starter agents, advanced multi-agent workflows, and tools with memory and document understanding. It also offers step-by-step setup instructions and video tutorials to help you learn quickly. Using these resources saves you time and effort in creating powerful AI apps, making it easier to develop, test, and deploy AI solutions even if you are new to AI programming.

https://github.com/Arindam200/awesome-ai-apps

#python #agent_framework #agentic_ai #agents #ai #dotnet #multi_agent #orchestration #python #sdk #workflows

Microsoft Agent Framework is an open-source toolkit that helps you build and manage AI agents and multi-agent workflows using Python or .NET. It combines the best features of previous Microsoft AI projects to let you create simple chatbots or complex workflows where multiple agents work together. It supports many AI models, connects easily to external tools and APIs, and runs anywhere—on cloud or on-premises. The framework also includes features like human review, workflow checkpointing, and monitoring to make your AI applications reliable and adaptable. This means you can build powerful, flexible AI solutions faster and with less code.

https://github.com/microsoft/agent-framework

#python #agents #ai #framework #llm #openai #python

The OpenAI Agents SDK is a Python framework that lets you easily build and connect AI agents—smart programs that can talk, use tools, and work together to solve tasks[2][3]. You can turn any Python function into a tool an agent can use, set up safety checks to control what agents do, and automatically pass tasks between different agents when needed[2][4]. The SDK manages conversation history for you, so agents remember past interactions, and it includes tools to track and debug how agents make decisions[2]. This makes it simple to create reliable, customizable AI helpers for things like customer support, research, or automation, with clear oversight and fast development.

https://github.com/openai/openai-agents-python

#python #agents #ai #ai_agents #api #developer_tools #discord #function_calling #integration #llm #mcp #mcp_client #mcp_server #oauth2 #open_source

Klavis AI helps developers connect AI tools to other services like GitHub, Gmail, and Slack easily. It offers hosted servers that handle authentication and client code automatically, making it simpler to integrate AI with various platforms. This saves time and effort by eliminating the need for custom authentication management and client library maintenance. Users can quickly set up and scale their AI applications without worrying about complex integrations, making it easier to deploy AI-powered workflows securely and efficiently.

https://github.com/Klavis-AI/klavis

#python #agents #artificial_intelligence #cybersecurity #generative_ai #llm #penetration_testing

Strix is a free, open-source tool that uses AI agents to automatically find and fix security problems in your apps by acting like real hackers—running your code, hunting for vulnerabilities, and proving they’re real by actually exploiting them, not just guessing[1][2]. It works fast, gives clear reports, and can even suggest fixes or create pull requests to help you secure your code quickly. You can run it on your own computer, in your development pipeline, or use a cloud version for easier setup. The main benefit is that you get thorough, real-world security testing without the slow pace and high cost of manual checks, helping you catch and fix issues before they become serious problems.

https://github.com/usestrix/strix

#go #a2a #agents #agents_sdk #ai #aiagentframework #gemini #genai #go #llm #mcp #multi_agent_collaboration #multi_agent_systems #sdk #vertex_ai

The Agent Development Kit (ADK) for Go is an open-source toolkit that makes it easy to build, test, and deploy smart AI agents using the Go programming language. It lets you create simple or complex agent workflows, use ready-made or custom tools, and run your agents anywhere, especially in cloud environments. With ADK, you get full control, flexibility, and the ability to scale your applications, making it faster and simpler to develop powerful AI solutions for real-world tasks.

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

#python #agents #ai_agents #anthropic #anthropic_claude #automation #claude #claude_code #claude_code_cli #claude_code_commands #claude_code_plugin #claude_code_plugins #claude_code_subagents #claude_skills #claudecode #claudecode_config #claudecode_subagents #orchestration #sub_agents #subagents #workflows

Claude Code Plugins provide a comprehensive system of 63 focused plugins containing 85 specialized agents, 47 skills, and 44 development tools organized for intelligent automation across software development. You install only what you need, keeping token usage minimal while accessing domain experts in architecture, languages, infrastructure, quality, and operations. Each plugin loads independently with its own agents and commands, letting you compose multiple plugins for complex workflows. This granular design means faster, cleaner sessions with progressive disclosure—knowledge loads only when activated. The benefit is significant productivity gains: you get expert-level assistance tailored to your specific task without unnecessary overhead, enabling your entire team to work more efficiently on development, infrastructure, security, and automation challenges.

https://github.com/wshobson/agents

#python #agents #gcp #gemini #genai_agents #generative_ai #llmops #mlops #observability

You can quickly create and deploy AI agents using the Agent Starter Pack, a Python package with ready-made templates and full infrastructure on Google Cloud. It handles everything except your agent’s logic, including deployment, monitoring, security, and CI/CD pipelines. You can start a project in just one minute, customize agents for tasks like document search or real-time chat, and extend them as needed. This saves you time and effort by providing production-ready tools and integration with Google Cloud services, letting you focus on building smart AI agents without worrying about backend setup or deployment details.

https://github.com/GoogleCloudPlatform/agent-starter-pack

#typescript #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

Agent Development Kit (ADK) for TypeScript is an open-source toolkit to build, test, and deploy advanced AI agents with full control in code. Key features include rich tools like Google Search, custom functions, and multi-agent hierarchies for scalable apps, plus a dev UI for easy debugging. Install via npm install @google/adk. You benefit by creating flexible, versioned AI agents that integrate tightly with Google Cloud, run anywhere from laptop to cloud, and speed up development like regular software.

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