Wavefront is an enterprise AI middleware platform, open-source alternative to tools like UnifyApps and n8n. It provides a full-stack system for building, managing, and deploying AI agents, chatbots, voice agents, knowledge bases (RAG), and AI-powered workflows. The monorepo includes a Python FastAPI backend with a modular, domain-driven architecture (agents, tools, knowledge base, voice, triggers, auth, API proxy), a React/TypeScript management console, background workers (Celery, stream processors), and the `flo_ai` Python agent framework (LLM wrappers, Arium graph engine, tool system). The platform supports multi-tenancy, RBAC, cloud storage abstractions (GCP/AWS/Azure), and pluggable datasource and authenticator modules. While primarily an application, it incorporates RAG pipelines, image embedding services, and model inference, giving it a hybrid character with strong data engineering elements.
This repository is a curated collection of 93+ production-ready AI engineering projects, spanning beginner to advanced levels. It serves as a practical resource for building AI applications, with a strong emphasis on agent orchestration (CrewAI, AutoGen, OpenAI Swarm), Retrieval-Augmented Generation (RAG) pipelines, Model Context Protocol (MCP) servers, and LLM evaluation frameworks. The dominant character is application development: most projects are deployable apps using Streamlit, Next.js, FastAPI, or as standalone MCP servers. Notable sub-projects include a full visual workflow builder (open-agent-builder) with Convex backend, a multi-modal RAG pipeline (notebook-lm-clone), and a comprehensive audio analysis toolkit. While primarily application-focused, the repository also contains data/research elements such as model fine-tuning scripts (GRPO, DPO, SFT), synthetic data generation (SDV), and LLM comparison dashboards with Opik/DeepEval evaluation. The codebase is heterogeneous in quality: some projects exhibit clean architecture and reusable patterns, while others are simple demos with boilerplate code and hardcoded dependencies. Overall, it is an excellent educational resource for AI engineers seeking practical, runnable examples, but not a single cohesive product.