You will get an agentic AI agent or multi-agent system with LangGraph
Project details
I build agentic AI systems — single agents and multi-agent workflows — that do real work across your tools, not just answer questions. Using LangGraph, the agent can research, decide, call tools, and complete multi-step tasks, with human approval on anything sensitive.
Many tasks need several steps and different tools, and a basic chatbot can't handle them. An agent can follow a plan, use tools, and finish the job while keeping you in control.
You'll get a working agent with a clear task flow, tool integrations (search, your APIs, or a database), human-in-the-loop approval steps, conversation memory, and a backend API to run it. I design the agent graph, define safe tool access, and add logging so you can see what it did.
I work with Python, LangGraph, LangChain, OpenAI or Claude, MCP for tool access, and a FastAPI or Django backend. I add error handling, retries, and safe execution rules so the agent behaves predictably, and sensitive actions always wait for human approval.
If you have a repetitive multi-step task you want an agent to handle, describe the steps and tools involved, and I'll show you how I'd build it.
Many tasks need several steps and different tools, and a basic chatbot can't handle them. An agent can follow a plan, use tools, and finish the job while keeping you in control.
You'll get a working agent with a clear task flow, tool integrations (search, your APIs, or a database), human-in-the-loop approval steps, conversation memory, and a backend API to run it. I design the agent graph, define safe tool access, and add logging so you can see what it did.
I work with Python, LangGraph, LangChain, OpenAI or Claude, MCP for tool access, and a FastAPI or Django backend. I add error handling, retries, and safe execution rules so the agent behaves predictably, and sensitive actions always wait for human approval.
If you have a repetitive multi-step task you want an agent to handle, describe the steps and tools involved, and I'll show you how I'd build it.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$800
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 10 days | 21 days | 35 days |
Number of Revisions | 2 | 3 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$250 - $900
Additional Revision
+$150
Additional tool integration
(+ 3 Days)
+$300
MCP server setup
(+ 4 Days)
+$450
Evaluation and safety testing
(+ 5 Days)
+$550About Dhruv
RAG, Agentic AI & Multi-Agent Developer | LangChain, LangGraph, LLMs
Ahmedabad, India - 10:10 am local time
I'm a Python and AI backend developer who works on real, live LLM systems, not just demos. On a production RAG SaaS product, I built the document ingestion pipeline, set up embeddings, and implemented real-time question answering with source citations so users could verify every response. I also added multi-provider LLM support, allowing conversations to continue even if one provider becomes unavailable or costs increase. My focus is on building AI systems that are accurate, reliable, and ready for production.
𝐖𝐇𝐀𝐓 𝐈 𝐂𝐀𝐍 𝐇𝐄𝐋𝐏 𝐘𝐎𝐔 𝐖𝐈𝐓𝐇
1. RAG Chatbots & Knowledge Assistants
Answer from your own documents with citations using hybrid retrieval (BM25 + dense embeddings) and reranking. I also build structured data extraction pipelines for invoices, contracts, forms, and other business documents.
2. Agentic AI & Multi-Agent Systems
Build LangGraph-based agents with tool calling, human approval workflows, and multi-step task automation for complex business processes.
3. LLM Integration for Your App or Product
Integrate OpenAI, Claude, and Gemini with streaming responses, multi-provider fallback, and production-ready backend architecture.
𝐓𝐎𝐎𝐋𝐒 𝐀𝐍𝐃 𝐒𝐓𝐀𝐂𝐊 𝐈 𝐔𝐒𝐄
• Backend: Python, Django, Django REST Framework, FastAPI, Node.js
• Frontend: React.js
• AI / Agent Frameworks: LangChain, LangGraph, n8n
• Async & Data: Celery, Redis, PostgreSQL
• Vector Databases: Milvus, Qdrant, pgvector
𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊
Every successful AI project starts with understanding the business problem, not choosing the latest framework or model.
I first understand your data, users, and business goals before deciding on the right retrieval or agent architecture. Then I build the solution step by step, validate it with real-world scenarios, and prepare it for production with proper logging, monitoring, and error handling.
Security is part of the implementation from day one, including API key management, document access control, and protection against prompt injection. Throughout the project, I communicate clearly, share regular progress updates, and raise potential risks early so there are no surprises later.
You get clean, documented code, an architecture that's easy to maintain, and AI systems that behave predictably when real users arrive.
If you're building a RAG assistant, an agentic AI agent, or a multi-agent system. Send me a short note about what you're building and the outcome you want. I'll tell you how I'd approach it, what challenges I see, and what's realistically achievable.
Steps for completing your project
After purchasing the project, send requirements so Dhruv can start the project.
Delivery time starts when Dhruv receives requirements from you.
Dhruv works on your project following the steps below.
Revisions may occur after the delivery date.
Map the task and tools
I map out the task, its steps, and the tools or APIs involved, so the agent design fits how the work actually needs to get done.
Design the agent flow and approvals
I design the agent's decision flow and identify which actions need human approval before the agent can proceed with them.