Dograh is an open-source, self-hostable voice AI platform for building, deploying, and monitoring voice agents. It provides a visual workflow builder (node-based graph editor) for designing call flows, with deep integration across telephony providers (Twilio, Plivo, Vonage, Telnyx, Cloudonix, Vobiz, Asterisk ARI), STT/TTS/LLM models (OpenAI, Deepgram, Google, Azure, Cartesia, ElevenLabs, etc.), and real-time audio pipelines via the Pipecat library. The backend is a FastAPI monolith with PostgreSQL, Redis, and object storage, complemented by separate worker services (ARQ for background tasks) and a campaign orchestrator for bulk outbound calling. The system supports multi-tenancy, call recording, knowledge base/RAG, QA analysis, embeddable chat widgets, and an MCP server for AI-assisted workflow management. SDKs for Python and TypeScript enable programmatic workflow creation and outbound call triggering. While the core is a full-stack application, it includes evaluation frameworks for STT providers and post-call QA analysis, giving it a hybrid character but dominated by production application concerns.
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.