Replio Messaging CoPilot

Managing digital conversations can quickly become overwhelming, especially when users need to respond thoughtfully, maintain the right tone, and keep track of conversation context. This project introduces an AI-powered messaging copilot that makes communication faster and more efficient while keeping humans in control. When a message is received, a stateful agentic workflow built with LangGraph analyzes the conversation context and tone, generates a relevant draft response, and provides alternative replies. Users can review, edit, request AI-powered revisions using natural-language instructions, or approve the final response. The human-in-the-loop architecture ensures that AI assists communication without automatically making decisions on the user’s behalf. The backend is built with FastAPI, while Fireworks AI provides LLM inference for generating context-aware responses. Conversation history is maintained to improve reply relevance, and the model layer is designed to be swappable, allowing different OpenAI-compatible inference providers or self-hosted models to be integrated without changing the core application architecture. The project is demonstrated through a WhatsApp-inspired messaging interface, showing how agentic AI can integrate naturally into everyday communication. By combining stateful agent orchestration, contextual understanding, response generation, alternative suggestions, and human oversight, the project creates a practical foundation for AI-assisted communication. The system can potentially extend beyond personal messaging into customer support, workplace collaboration, and other communication platforms where speed, context, personalization, and human accountability are essential.