This repository is a production-grade application for building real-time voice agents, primarily designed as an educational course but structured for actual deployment. It implements a complete voice call center system capable of receiving and making phone calls via Twilio, transcribing speech using various STT models (Moonshine, Whisper, Faster Whisper), reasoning with LLMs (Groq), searching property data using Superlinked and Qdrant, and generating speech using TTS models (Kokoro, Orpheus). The architecture is a modular monolith built with FastAPI, utilizing Docker for containerization and RunPod for GPU-accelerated model inference. While the repository includes Jupyter notebooks for educational purposes (Lesson 1-4), the core value lies in the `src/realtime_phone_agents` package, which encapsulates the agent logic, avatar system, and infrastructure integrations. The code demonstrates high craftsmanship in handling async audio streaming, managing external API dependencies, and implementing a flexible avatar registry system. It is suitable for engineers looking to build voice AI applications or understand the integration of LLMs with real-time audio pipelines.
AgentScope is a full-stack, production-grade multi-agent orchestration platform. The core library (`src/agentscope/`) provides a FastAPI-based agent service with CRUD endpoints for agents, sessions, credentials, channels, knowledge bases, schedules, and SOPs. It features a modular middleware onion pattern for agent lifecycle hooks (reply, reasoning, acting, tool execution), a pluggable storage abstraction (Redis, SQL with Alembic migrations, S3 blob store), and a message bus (in-memory or Redis) for inter-session communication and distributed coordination. The system supports real-time voice (WebRTC, DashScope/OpenAI/Gemini/xAI), RAG pipelines with document parsing and vector stores (Qdrant, Milvus, MongoDB, Elasticsearch), channel integrations (Feishu, DingTalk, Discord), and sandboxed workspace backends (Docker, Kubernetes, E2B, Daytona, Apple Containers, Bubblewrap, OpenSandbox). A full React/TypeScript web UI (`examples/web_ui/frontend`) provides chat, channel management, schedule calendar, knowledge base browsing, and MCP hub browsing. Additionally, a terminal console and a goal-oriented pipeline are provided for developer workflows. The framework emphasizes transparency, controllability, and separation between agent logic and infrastructure, supporting multi-agent collaboration, human-in-the-loop permissions, and long-term memory via ReMe and mem0 middleware.
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.