You will get I will build a multi agent AI system with LangChain and LangGraph
Project details
I build production-ready multi-agent AI systems using LangChain
and LangGraph — where multiple specialized AI agents work
together to handle complex tasks automatically.
What makes my work different:
— I built HerAI, a 5-agent system with Mood Detection, RAG
Memory, Content Generation, Surprise Planning, and Safety
Check agents — all orchestrated with LangGraph routing.
— I implement FAISS-based semantic memory so agents remember
and retrieve past context intelligently.
— I design conditional routing logic — the system decides
which agent handles each request based on context, not just
keywords.
— Every agent is modular, documented and easy to extend with
new capabilities after delivery.
What you get:
✅ Custom specialized agents for your use case
✅ LangGraph workflow with intelligent routing
✅ FAISS RAG memory for context retention
✅ Streamlit web interface for interaction
✅ FastAPI endpoint for integration
✅ Full documented source code
I am a Computer Engineering student at IOE Purwanchal Campus
Nepal, interning at Planto AI, building production AI systems.
Tell me your use case and I will build your agent system.
and LangGraph — where multiple specialized AI agents work
together to handle complex tasks automatically.
What makes my work different:
— I built HerAI, a 5-agent system with Mood Detection, RAG
Memory, Content Generation, Surprise Planning, and Safety
Check agents — all orchestrated with LangGraph routing.
— I implement FAISS-based semantic memory so agents remember
and retrieve past context intelligently.
— I design conditional routing logic — the system decides
which agent handles each request based on context, not just
keywords.
— Every agent is modular, documented and easy to extend with
new capabilities after delivery.
What you get:
✅ Custom specialized agents for your use case
✅ LangGraph workflow with intelligent routing
✅ FAISS RAG memory for context retention
✅ Streamlit web interface for interaction
✅ FastAPI endpoint for integration
✅ Full documented source code
I am a Computer Engineering student at IOE Purwanchal Campus
Nepal, interning at Planto AI, building production AI systems.
Tell me your use case and I will build your agent system.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Deeplearning4j, Keras, MLflow, Open Neural Network Exchange, OpenCV, PyTorch, TensorFlowAI Development Language
C++What's included
| Service Tiers |
Starter
$120
|
Standard
$250
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 16 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | - | ||
Taxonomy | - |
Optional add-ons
You can add these on the next page.
RAG Memory Integration
(+ 4 Days)
+$100
Streamlit Web UI
(+ 3 Days)
+$75
API Deployment
(+ 3 Days)
+$80About Yamraj
Machine Learning & AI Enthusiast
Dharan, Nepal - 5:41 am local time
I build production ML and AI systems — not just notebooks.
I'm a Computer Engineering student at IOE Purwanchal Campus (Nepal) specializing in Machine Learning, LLMs, Computer Vision, and RAG pipelines. My work has been adopted by the open-source community and presented at international AI conferences.
WHAT I'VE BUILT
— Fine-tuned Mistral-7B on Nepal's National Penal Code → adopted by community maintainer within 24 hours and redistributed in Q2–Q8 GGUF formats for CPU-only inference across the llama.cpp ecosystem
— Built U-Net from scratch in TensorFlow for satellite land cover segmentation (Mean IoU: 0.674 across 7 classes) → presented at ICRTAI 2025 international conference
— Built multi-agent AI system using LangChain + LangGraph with 5 specialized agents: Mood Detection, FAISS-based Memory (RAG), Content Generation, Surprise Planning, Safety Check
— Deployed 4 live AI web apps + 2 production FastAPI endpoints + Android app (React Native/Expo) on Hugging Face Spaces
— Implemented RNN, GRU, LSTM, and BRNN architectures from scratch in PyTorch
— Built MLOps pipeline: MLflow experiment tracking, Optuna hyperparameter search, model registry
WHAT I CAN DO FOR YOU
1.Fine-tune LLMs on your domain-specific data
2. Build RAG pipelines for your documents or knowledge base
3.Train computer vision models (classification, segmentation, detection)
4. Build and deploy ML APIs with FastAPI on Hugging Face or cloud
5. MLOps: experiment tracking, reproducible pipelines, model registry
6. Multi-agent AI workflows with LangChain and LangGraph
TECH STACK
Python | TensorFlow | PyTorch | Hugging Face | FAISS | FastAPI | Streamlit | LangChain | LangGraph | MLflow | GGUF / llama.cpp | React Native
Steps for completing your project
After purchasing the project, send requirements so Yamraj can start the project.
Delivery time starts when Yamraj receives requirements from you.
Yamraj works on your project following the steps below.
Revisions may occur after the delivery date.
Define agents and routing logic
I review your requirements and design the agent architecture — how many agents, what each does, and how LangGraph routes between them based on context and intent.
Build individual agents
I build each specialized agent with its own prompt, tools, and logic using LangChain. Each agent is modular and independently testable before integration.