I will build ai agents using langgraph, rag and fastapi

Standard chatbots follow rigid, linear lines and break easily. A LangGraph agent uses cyclic state machines and conditional routing. This lets the agent think, loop back, self-correct errors, use custom tools, and maintain strict business rules without hallucinating.

It’s an isolated, secure control panel accessible only via your secret admin key. From this dashboard, you can monitor live chat transcripts, view automatically captured sales leads, analyze usage metrics, and update the agent’s knowledge base instantly.

As the agent chats with visitors, it naturally extracts explicit details like names, emails, and project requirements. It immediately logs this data into a persistent PostgreSQL database and can trigger an automated email notification straight to your inbox.

Yes! You don't have to touch a single line of backend Python code. You simply log into your Admin Dashboard, drag and drop your updated company PDFs or .txt documents into the RAG uploader, and the agent's memory updates across the live site instantly.

Absolutely. I deliver the frontend interaction layer as a highly optimized, responsive script widget. It can be embedded natively into your corporate WordPress theme layout or cleanly integrated directly into any modern React/Next.js component workflow.

Yes. If you have strict data privacy constraints, I can configure the entire FastAPI backend to process semantic data locally using open-source models via Ollama (like Llama 3 or Mistral) running securely on your private server infrastructure.

No. For standard deployments, I utilize high-performance, lightweight vector indexing solutions like ChromaDB or serverless database structures that operate completely free within your application framework layer, saving you massive infrastructure costs.