I will build ai agents, rag and multi agent systems with langchain langgraph

A normal chatbot invents plausible answers. My RAG systems retrieve from your real documents using vector databases (Pinecone, Chroma, FAISS), cite every source, and say "I don't know" instead of guessing.

Yes. I connect agents to HubSpot, Salesforce, Slack, Google Sheets, Shopify, and internal APIs using LangChain tool calling and n8n/Make/Zapier automations.

LangGraph gives stateful, controllable agent workflows (branching, retries, human approval). LangChain handles prompts, retrieval, embeddings, and model integrations. Together they're the industry standard for production agents.

Yes. Everything can be deployed on your own cloud or run fully offline with local LLMs (Llama, Mistral, Ollama) — your data never leaves your control.

Always. Clean, documented, production-ready code with a README covering setup and deployment.

OpenAI GPT-4, Anthropic Claude, Google Gemini, Mistral, Llama, and local/open-source models.

Instead of one agent doing everything, a multi agent system uses specialized agents (e.g. researcher + writer + critic) coordinated by LangGraph. It's the right choice for complex workflows — research pipelines, content production, data analysis — where single agents lose track.

Yes. I build fully private RAG systems and agents with local LLMs (Llama, Mistral) via Ollama or vLLM on your own hardware or cloud — no data ever leaves your environment.