Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
A Model Context Protocol (MCP) TypeScript template with multi agents that can send Emails, schedule meetings and answer knowledge-base related questions.
FenceFlow replaces this manual chaos with an AI-powered operations platform that automates scoring, coordinates referees, and delivers real-time results to every stakeholder.
🤖 Define and execute multi-agent AI workflows declaratively using YAML, simplifying orchestration and enhancing collaboration through automatic context handling.
This repository contains the official implementation of BLEND, a novel framework that advances distributed intelligence through the synergy of blockchain technology and Large Language Models (LLMs) for long-horizon time-series forecasting in Internet of Vehicles (IoV) environments.
A conversational AI platform to help users explore, filter, and get insights about vehicles, including electric cars, MOT status, and specifications. The platform uses MongoDB for structured vehicle data, ChromaDB for semantic search, and OpenAI LLM for generating conversational summaries.
Multi-agent AI pipeline that turns a hiring brief into an evaluated, export-ready candidate slate using Claude, with built-in AI output evaluation and schema-validated agent contracts.
a multi-agent robotic assembly simulation that integrates A* path finding, task allocation, collision avoidance for parallel construction
Parking management system using IoT devices in conjunction with an integrated multi-agent system. The objective was to monitor parking space availability in real time, identifying occupancy. Intelligent system capable of analyzing and managing available spaces.
A YAML-driven engine for declaratively defining and executing multi-agent workflows with automatic orchestration, parallelism and context passing.
Build intelligent Java agents for the Embabel Hub with this powerful, annotation-driven framework offering seamless integration and production-ready tooling.
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