#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
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
GitHub
GitHub - simstudioai/sim: Open-source platform to build and deploy AI agent workflows.
Open-source platform to build and deploy AI agent workflows. - simstudioai/sim
#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
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
GitHub
GitHub - humanlayer/humanlayer: The best way to get AI coding agents to solve hard problems in complex codebases.
The best way to get AI coding agents to solve hard problems in complex codebases. - humanlayer/humanlayer
#python #agent #agentic_ai #agentic_framework #agentic_workflow #ai #ai_agents #ai_companion #ai_roleplay #benchmark #framework #llm #mcp #memory #open_source #python #sandbox
MemU lets AI systems take in conversations, documents, and media, turn them into structured memories, and store them in a clear three-layer file system. It offers both fast embedding search and deeper LLM-based retrieval, works with many data types, and supports cloud or self-hosted setups with simple APIs. This helps you build AI agents that truly remember past interactions, retrieve the right context when needed, and improve over time, making your applications more accurate, personal, and efficient.
https://github.com/NevaMind-AI/memU
MemU lets AI systems take in conversations, documents, and media, turn them into structured memories, and store them in a clear three-layer file system. It offers both fast embedding search and deeper LLM-based retrieval, works with many data types, and supports cloud or self-hosted setups with simple APIs. This helps you build AI agents that truly remember past interactions, retrieve the right context when needed, and improve over time, making your applications more accurate, personal, and efficient.
https://github.com/NevaMind-AI/memU
GitHub
GitHub - NevaMind-AI/memU: Memory infrastructure for LLMs and AI agents
Memory infrastructure for LLMs and AI agents. Contribute to NevaMind-AI/memU development by creating an account on GitHub.