#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

#jupyter_notebook #agent #agentic_ai #agents #authentication #bedrock #core #gateway #identity_management #memory_management #production_code #runtime

Amazon Bedrock AgentCore lets you build, deploy, and run AI agents securely at scale with any framework like CrewAI or LangGraph and any model, without managing complex infrastructure. It offers serverless runtime for long tasks up to 8 hours, gateway to connect tools like Slack or APIs easily, memory for personalized experiences, identity management, built-in code interpreter and browser tools, plus observability. This saves time by skipping heavy setup, speeds prototypes to production, cuts costs with pay-per-use, and boosts security—helping you create powerful agents faster for real business needs.

https://github.com/awslabs/amazon-bedrock-agentcore-samples

#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

#javascript #agentic_ai #agentic_engineering #agentic_framework #agentic_rag #agentic_workflow #ai_assistant #ai_tools #anthropic_claude #autonomous_agents #claude_code #codex #huggingface #jules #mcp_server #model_context_protocol #multi_agent #multi_agent_systems #npx #swarm #swarm_intelligence

Claude-Flow v2.7 is an enterprise AI platform with hive-mind swarms, 25 natural language skills, 100+ tools, and AgentDB integration for 96x-164x faster semantic search and 4-32x less memory use. Install via `npx claude-flow@alpha init` after Claude Code, then use commands like `swarm "build API"` for quick tasks or hive-mind for projects. It boosts your coding speed with 84.8% problem-solving rate, automation, GitHub tools, and persistent memory—saving you hours on complex development.

https://github.com/ruvnet/claude-flow

#javascript #agent #agentic #agentic_ai #ai #ai_agents #automation #cursor #design #figma #generative_ai #llm #llms #mcp #model_context_protocol

Cursor Talk to Figma MCP lets Cursor AI read and edit your Figma designs directly, using tools like `get_selection` for info, `set_text_content` for bulk text changes, `create_rectangle` for shapes, and `set_instance_overrides` for components. Setup is quick: install Bun, run `bun setup` and `bun socket`, add the Figma plugin. This saves you hours by skipping context switches, automating repetitive tasks like text replacement or override propagation, speeding up design-to-code workflows, and keeping everything in sync for faster, precise builds.

https://github.com/grab/cursor-talk-to-figma-mcp

#python #agentic_ai #agents #ai #ai_agents #realtime #stt #tts #video_agents #video_ai #vision_ai #voice_ai

Vision Agents is an open-source Python framework by Stream to build real-time AI agents that watch video, listen to audio, and respond instantly with low latency under 30ms. It integrates YOLO, Roboflow, OpenAI, Gemini, and 25+ tools for apps like golf coaching, security cameras detecting theft, or phone assistants. Install easily with `uv add vision-agents`, use free Stream credits, and deploy on any video network. You benefit by quickly creating smart video AI for gaming, safety, or coaching without vendor lock-in, saving time and costs on custom builds.

https://github.com/GetStream/Vision-Agents

#typescript #agentic_ai #ai_agents #claude_code #cli #codex #coding_agents #cursor_agent #desktop_app #developer_tools #electron #git_worktree #llm #mcp #opencode #orchestration #parallel_agents #terminal #tui #vibe_coding #worktrees

Superset is a turbocharged macOS terminal for running 10+ CLI coding agents like Claude Code, Cursor, and GitHub Copilot in parallel. It isolates tasks in separate Git worktrees to avoid interference, lets you monitor progress from one dashboard, review changes with a built-in diff viewer, and switch contexts quickly. You benefit by coding 10x faster, shipping more without context-switching delays or conflicts, saving time on development workflows.

https://github.com/superset-sh/superset

#python #agentic_ai #agentic_coding #ai_coding_agent #ai_plugins #anthropic_claude #claude_ai #claude_ai_skills #claude_code #claude_code_plugins #claude_code_skills #claude_skills #claudecode_subagents #developer_tools #devtools #mcp_tools #openai_codex #prompt_engineering

Claude Code Skills offers 169 free, ready-to-use plugins that turn AI coding agents like Claude Code, OpenAI Codex, and OpenClaw into experts in engineering, marketing, product, compliance, and more. Install easily via simple commands to add skills like security auditing, test automation, or C-level advice, with 160+ Python tools included. This saves you time by automating complex tasks, boosting code quality, and handling grunt work so you focus on creative problem-solving and faster results.

https://github.com/alirezarezvani/claude-skills

#python #agentic_ai #agents #memory

Hindsight is a top agent memory system that helps AI agents learn over time by storing facts, experiences, and mental models like human memory, beating rivals on LongMemEval benchmarks with 91.4% accuracy. Add it easily with 2 lines of code via Python or Node.js clients, using simple retain, recall, and reflect operations for Docker or embedded setups. You benefit by building smarter, consistent agents that reduce errors, cut hallucinations, handle long-term tasks, and personalize chats—saving time and boosting performance in production.

https://github.com/vectorize-io/hindsight

#python #agentic_ai #agentic_workflow #agents #function_calling #llama_cpp #llamafile #llm #ollama #python #self_hosted #tool_calling

Forge is a Python tool that makes self-hosted LLM tool-calling more reliable. It helps local models handle multi-step tasks with guardrails, better context control, and support for Ollama, llama-server, Llamafile, and Anthropic. You can use it as a workflow runner, middleware, or proxy server with OpenAI-style clients. The benefit is fewer broken tool calls, better results on small models, and easier setup for agent apps, chat tools, and long-running sessions.

https://github.com/antoinezambelli/forge

#javascript #agent_orchestration #agent_skills #agentic_ai #agentic_workflow #ai_agents #ai_automation #babysitter #claude_code #claude_code_skills #claude_code_workflows #claude_skills #claude_workflows #codex_plugin #codex_skills #codex_workflow #hermes_plugin #pi_extension #trustworthy_ai #vibe_coding

Babysitter is a tool that helps you run AI coding work in a strict, step-by-step way, with checks, approvals, and a full log of every decision. It can save you time, reduce mistakes, and make complex tasks easier to control because it keeps the process deterministic, resumable, and easier to audit.

https://github.com/a5c-ai/babysitter

#python #agent #agentic_ai #ai #claude #copilot #cursor #elevenlabs #ffmpeg #flux #image_generation #open_source #openai #python #remotion #stable_diffusion #text_to_speech #text_to_video #video_generation #video_production

OpenMontage turns a plain idea or even a reference video into a full video production workflow, handling research, script writing, asset creation, editing, captions, and final rendering. Your benefit is faster video creation with lower cost, more control, and fewer surprises, because it can use free/open footage or AI tools, estimate cost first, and check quality before showing you the result.

https://github.com/calesthio/OpenMontage

#other #agentic_ai #coding #llm #long_horizon

GLM-5.2 is the latest open-source model built for long, complex tasks, featuring a stable 1 million-token context that lets you handle huge projects without losing track of details. It also offers stronger coding skills with flexible thinking modes to balance speed and quality, plus faster performance. You benefit by getting reliable, high-quality results for big software engineering jobs, automated research, or long planning sessions, all while using an open model that saves costs compared to closed alternatives.

https://github.com/zai-org/GLM-5

#python #agent_skills #agentic_ai #ai_agents #autonomous_agents #claude #claude_code #claude_skills #codex #coding_agent #context_engineering #copilot #cursor #developer_tools #github_copilot #llm_agents #long_running_agents #manus #multi_agent_systems #pi #planning

This tool makes your AI coding agent keep a plan in files like `task_plan.md` so it never loses its goals when context fills up, you run `/clear`, or it crashes. You get a working memory on disk that auto-recovers past sessions and holds the agent until the plan is truly done, letting complex, multi-step tasks finish reliably without goal drift or repeated errors.

https://github.com/OthmanAdi/planning-with-files

#kotlin #agent #agentic_ai #agents #ai #ai_agents #aiagentframework #genai #generative_ai #java #kotlin #llms #multi_agents #multi_agents_orchestration #multi_agents_system #spring

Embabel is a Java and Kotlin framework for building AI agents on the JVM with Spring, using typed goals, actions, and conditions instead of hard-coded prompt chains. It plans and replans at runtime, which helps you build more flexible, testable, and reusable agent flows while still fitting into existing enterprise code. The benefit to you is faster AI app development with stronger type safety, easier testing, and simpler use of LLMs, tools, and domain models in one system.

https://github.com/embabel/embabel-agent