Build Custom AI Agent with LangGraph and LangChain, Multi Agent Workflow Automation

We are looking for an experienced AI agent developer who has actually shipped production agents with LangGraph, not just tutorials or demos. The goal is to build a custom multi agent system that handles real business workflows end to end, things like lead qualification, document processing, CRM updates and follow ups. What the project involves: Designing and building custom AI agents using LangGraph and LangChain in Python Multi agent orchestration with proper state management, memory and human in the loop steps for edge cases RAG pipeline trained on our company data (vector DB experience needed, Pinecone, pgvector, Qdrant or similar) LLM integration with OpenAI GPT-4o and Claude via API, solid prompt engineering Connecting the agents to our existing tools through n8n, Make or Zapier where it makes sense, plus webhooks and REST APIs Basic dashboard or logging so we can see what the agents are doing What we need from you: Proven track record on Upwork building AI agents or LLM automation systems, please share 2 or 3 relevant projects in your proposal Strong Python, comfortable with FastAPI or similar for deployment You should be able to map the workflow first and then build, we don't want a pile of disconnected automations we have to babysit Clear communication and regular updates Nice to have: voice agent experience (Vapi, Twilio), Supabase or MongoDB, experience with healthcare, legal or ecommerce automation. This is a fixed price project. In your proposal, start with the word "orchestrate" so we know you read the full post, then briefly describe a LangGraph agent you built and what business problem it solved. We will shortlist quickly and hop on a short call.