You will get a LangChain and LangGraph AI agent with tools and human approval
Project details
I will build a LangChain or LangGraph AI agent that can use tools, manage workflow state, and support human approval where required. The solution can connect to a compatible hosted or local language model and can include memory, conditional routing, API tools, database access, multimodal input, or tokenizer configuration depending on the selected package.
You will receive organized Python source code, setup instructions, configuration examples, and clear usage documentation. Standard and Advanced packages can include testing, stateful workflows, detailed code comments, more complex integrations, and deployment-ready setup.
This service is designed for clients who need a focused AI agent workflow rather than custom model training. Model training, fine-tuning, complex frontends, large multi-agent systems, and full production cloud infrastructure are not included unless agreed as a separate scope.
Before development starts, I will review the agent goal, selected model, required tools, expected inputs and outputs, memory needs, approval steps, and deployment requirements to confirm compatibility and scope.
You will receive organized Python source code, setup instructions, configuration examples, and clear usage documentation. Standard and Advanced packages can include testing, stateful workflows, detailed code comments, more complex integrations, and deployment-ready setup.
This service is designed for clients who need a focused AI agent workflow rather than custom model training. Model training, fine-tuning, complex frontends, large multi-agent systems, and full production cloud infrastructure are not included unless agreed as a separate scope.
Before development starts, I will review the agent goal, selected model, required tools, expected inputs and outputs, memory needs, approval steps, and deployment requirements to confirm compatibility and scope.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Image Analysis, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
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.
Additional Revision
+$25
Additional Tool Integration
(+ 2 Days)
+$50
Docker Setup
(+ 2 Days)
+$60
Multimodal Input Support
(+ 3 Days)
+$100Frequently asked questions
About Chathuranga
AI/ML Engineer | RAG, AI Agents, FastAPI, MLOps & AWS
Nugegoda, Sri Lanka - 1:24 pm local time
Need a RAG application, AI agent, machine learning API, or help improving an existing AI project? I help businesses turn AI ideas, datasets, and prototypes into tested, maintainable, and deployment-ready systems.
I can support you with:
• RAG and LLM applications using LangChain, LangGraph, OpenAI, Hugging Face, and vector databases
• AI agents with tools, memory, conditional workflows, structured outputs, and human-in-the-loop approval
• Machine learning models using scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, and PyTorch
• FastAPI model services, backend integrations, and REST APIs
• Docker, automated testing, CI/CD, monitoring, model versioning, and AWS deployment
• Debugging, improving, and productionizing existing AI/ML projects
My project experience includes enterprise fraud detection, RAG-driven decision intelligence, multimodal recommendation, NLP inference APIs, persistent tool agents, and cloud-native AI/ML deployment.
You will receive maintainable code, clear documentation, honest technical communication, and an implementation aligned with your business requirements.
Steps for completing your project
After purchasing the project, send requirements so Chathuranga can start the project.
Delivery time starts when Chathuranga receives requirements from you.
Chathuranga works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and define workflow
I will review the agent goal, tools, model preferences, inputs, outputs, and approval needs to confirm the workflow and implementation scope.
Build the agent workflow
I will implement the LangChain or LangGraph workflow, connect the selected model and tools, and add routing, state, or memory based on the package.