Whatsapp LangGraph Agent Integration is an example implementation showcasing the deployment of LangGraph-based AI agents on WhatsApp messaging. It uses Python and FastAPI to expose webhook endpoints for Twilio’s WhatsApp API, automatically parsing incoming messages into the agent’s graph workflow. The agent supports context preservation across sessions with built-in memory nodes, tool invocation for specific tasks, and dynamic decision-making via LangGraph’s modular nodes. Developers can customize graph definitions, integrate additional external APIs, and manage conversational state seamlessly. This integration acts as a template, illustrating message routing, response generation, error handling, and easy scalability to build complex interactive chatbots on WhatsApp.

AI_RAG delivers a modular retrieval-augmented generation solution that combines document indexing, vector search, embedding generation, and LLM-driven response composition. Users prepare corpora of text documents, connect a vector store like FAISS or Pinecone, configure embedding and LLM endpoints, and run the indexing process. When a query arrives, AI_RAG retrieves the most relevant passages, feeds them alongside the prompt into the chosen language model, and returns a contextually grounded answer. Its extensible design allows custom connectors, multi-model support, and fine-grained control over retrieval and generation parameters, ideal for knowledge bases and advanced conversational agents.