agentic-app

ModelCraft is an agentic application designed for MacOS. It aims to make it easier to use LLM to complete task locally.

The universal interface layer between AI agents and humans. Generate rich UIs on demand via MCP.

Flutter framework for multi-agent AI orchestration. Build agents that use tools, chain tasks, and talk to each other. Works with any LLM provider & no backend is needed.

Move from a rough brief to distinct, playable prototypes—then test and revise precisely without losing lineage.

A web component based AI agentic chat UI element which can be added in any website to turn it into an agentic app

A learning system that researches source anchors, generates typed lessons, and adapts from durable attempts.

Turn your own tag rules into continuous triage, concise summaries, and reviewable replies in your voice.

Background agents leave anchored comments and exact, reviewable edits instead of silently rewriting your work.

Assign real work, share context, review output, and keep every human-agent handoff visible.

Turn a shared Gmail mailbox into an operated support system where every risky action remains human-controlled.

Mobile-first agentic matching MVP with GGUI, persona exploration, tobl.ai simulation, and Cocoun council reports

A capture stream with a background curator that titles, summarizes, tags, and files what you send.

A food journal that turns messy meal language into daily totals, confidence signals, and one useful next move.

A user-named assistant that does recurring work, learns preferences, and proposes outside actions for approval.

Keep the strategy, audience, narrative, assets, decisions, and execution of a campaign in one durable system.

Production-ready template for building agentic apps with FastAPI, Pydantic AI, PostgreSQL, and full tracing via Jaeger.

Build LLM-powered AI agents with a minimal FastAPI and Pydantic template featuring async SQLAlchemy, uv for dependencies, and Alembic migrations.

Forked agentic app demo for learning CRUD workflows and AI-assisted full-stack application patterns.

A customer service chatbot for a restaurant built with React frontend and Python backend using Retrieval-Augmented Generation (RAG) for intelligent responses.

no code ml-studio