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Fiverr Pro
English
$
USD
I will build ai agent with rag, langgraph, langchain, llamaindex and n8n
Ilyas N
About this gig
Most AI chatbots just wrap a model: they sound confident, hallucinate, and can't do anything. Businesses need an agent that reasons over real data, takes action, and holds up in production.
I build custom AI agents with Python, FastAPI, LangChain, LangGraph and LlamaIndex:
Grounded RAG answers come from your documents and cite sources. If the answer isn't in your data, the agent says so instead of inventing one.
Tool use it acts, not just talks: searches your knowledge base, calls APIs, parses web data, updates your CRM.
Memory & control multi-turn memory and LangGraph state machines make behavior predictable and debuggable, not fragile prompt chains.
Automation & voice n8n workflows connect the agent to Telegram, WhatsApp, email, Sheets and your CRM; STT/TTS for voice agents.
You get clean, documented code you fully own.
Message me before ordering and tell me what you're building I'll honestly tell you if it's a good fit.
Get to know Ilyas N
Ilyas N
AI Engineer, LLM Agents, RAG and n8n Automation
- FromUzbekistan
- Member sinceApr 2025
- Last delivery6 months
Languages
Russian, English
I build AI agents, RAG systems and n8n automations that replace manual work in real businesses.
• AI agents & RAG: LangChain, LangGraph, LlamaIndex, pgvector
• n8n workflows: CRM, email, e-commerce, self-hosted setup
• Telegram/WhatsApp bots with AI, quizzes, lead funnels
• Scrapers, Python/FastAPI backends, API integrations
Clear scope first, working version early, then iterations. Clean, documented solutions, no black box.
Send me a short description of your task and I'll suggest an approach.
My Portfolio
FAQ
What’s the difference between a RAG agent and a normal chatbot?
A normal chatbot answers from what the model memorized, so it can invent facts. A RAG agent first searches your documents, answers only from what it finds, cites the source, and says “I don’t know” when the answer isn’t in your data.
My documents are confidential. Is that safe?
Yes. Your data lives in your own database, and I can self-host everything on your server. For strict privacy I can use a local open-source model, so nothing leaves your infrastructure. With cloud LLMs, only the text needed for each answer is sent to the provider’s API.
Can the agent connect to my existing tools: CRM, APIs, spreadsheets?
Yes. With n8n and Python I connect it to anything that has an API or webhook: CRMs, Google Sheets, databases, email, Telegram, WhatsApp. The agent doesn’t just chat, it can read data, create records and trigger actions.
Why LangChain and LangGraph specifically?
LangChain gives proven building blocks (retrievers, tools, memory). LangGraph adds explicit state and control flow, so the agent’s behavior is predictable and debuggable, not a fragile prompt chain. If your task needs only a simple n8n workflow, I’ll tell you and save you money.
Which channels can the agent work in?
Telegram, WhatsApp, a chat widget on your website, Slack or email. Voice (speech-to-text and text-to-speech) is possible in the Premium package. Tell me where your customers are, and I’ll suggest the best fit.
