You will get LangGraph Multi-Agent AI Workflows with OpenAI & LLMs
Project details
I build advanced multi-agent AI workflows using LangGraph and LLMs like OpenAI, DeepSeek, and Llama 3. If you need AI systems that can handle complex tasks, automate decisions, and work step-by-step like real agents, I can create reliable and scalable solutions.
I focus on building structured AI workflows where multiple agents collaborate, use tools, and deliver accurate results for real business use cases.
➡ What you will get
✅ LangGraph multi-agent workflow development
✅ OpenAI GPT & LLM integration
✅ AI agents with tool/function calling
✅ Prompt chaining & reasoning workflows
✅ API integrations for real-world tasks
✅ RAG support with knowledge-based responses
✅ Scalable and production-ready architecture
➡ Use Cases
✅ AI task automation systems
✅ Multi-step decision-making agents
✅ AI copilots & assistants
✅ Business workflow automation
I focus on building structured AI workflows where multiple agents collaborate, use tools, and deliver accurate results for real business use cases.
➡ What you will get
✅ LangGraph multi-agent workflow development
✅ OpenAI GPT & LLM integration
✅ AI agents with tool/function calling
✅ Prompt chaining & reasoning workflows
✅ API integrations for real-world tasks
✅ RAG support with knowledge-based responses
✅ Scalable and production-ready architecture
➡ Use Cases
✅ AI task automation systems
✅ Multi-step decision-making agents
✅ AI copilots & assistants
✅ Business workflow automation
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included $150
These options are included with the project scope.
$150
- Delivery Time 3 days
- Number of Revisions 3
About Muhammad Maaz
AI Engineer | AI Agents, LLMs, Automations,RAG Chatbots|Python/FastAPI
Sargodha, Pakistan - 2:55 am local time
With 5+ years of production engineering experience, I help startups and established teams turn AI ideas, repetitive workflows, and disconnected data into reliable systems that are ready for real users.
𝟭. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
• LangGraph state machines
• Supervisor-worker architectures
• Tool and API calling
• Structured outputs and permissions
• Checkpoints, retries, and fallbacks
• Duplicate prevention and audit logs
• Human-in-the-loop approvals
For an operations automation platform, I designed a six-agent workflow:
Parser → Supervisor → Lookup → Generator → Guardrail → Execution
The system:
• Processes manual requests and bulk CSV/Excel files
• Validates records and detects duplicates
• Prepares actions for human approval
• Executes authenticated API actions after review
Stack: LangGraph, async Python, FastAPI, PostgreSQL, Streamlit, Docker, and AWS.
𝟮. 𝗥𝗔𝗚 𝗖𝗵𝗮𝘁𝗯𝗼𝘁𝘀 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀
I build RAG systems using company documents, websites, databases, policies, product information, and internal knowledge.
Capabilities include:
• Document ingestion and chunking
• Embeddings and semantic search
• Hybrid retrieval and re-ranking
• Vector databases
• Citations and conversation memory
• Response validation
• Retrieval evaluation
• Access control
I developed a medical knowledge assistant using LangGraph, LangChain, FastAPI, and ChromaDB.
The system supports:
• Clinic FAQs
• Document retrieval
• Doctor matching
• Scheduling
• Structured appointment summaries
• Grounded answers using approved information
𝟯. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻
I build n8n, Make, and API-based automations connecting:
• CRMs
• Calendars
• Slack
• Spreadsheets
• Databases
• Telephony platforms
• Internal business tools
Typical workflows include:
• Lead capture and qualification
• Follow-up sequences
• CRM updates
• Document processing
• Approval workflows
• Reporting and alerts
• Task creation and routing
I also handle retries, failed records, duplicate events, logging, permissions, fallbacks, and documentation.
𝟰. 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜
I develop inbound and outbound voice agents using Retell AI, Twilio, ElevenLabs, Whisper, FastAPI, and automation tools.
These agents can:
• Answer FAQs
• Qualify leads
• Collect customer information
• Schedule appointments
• Update CRMs
• Generate call summaries
• Trigger follow-ups
• Transfer calls to human staff
For a hotel voice receptionist, I built a Dockerised FastAPI backend for Retell AI webhooks.
The system handles:
• Interruptions
• Corrections
• Spelled information
• Booking details
• Structured JSON extraction
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗠𝗟𝗢𝗽𝘀
I build and deploy ML workflows for:
• Classification
• Regression
• Forecasting
• Recommendations
• Anomaly detection
• NLP
• Structured data analysis
My work includes:
• Data preparation and feature engineering
• Model training and evaluation
• Experiment tracking
• Model comparison and versioning
• API-based inference
• Dockerised deployment
• CI/CD, monitoring, and rollback
Tools: Scikit-learn, PyTorch, TensorFlow, MLflow, FastAPI, AWS, GCP, and Azure.
𝟲. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴
I design ETL and ELT pipelines that:
• Ingest data from APIs, databases, files, and SaaS tools
• Clean and validate records
• Transform data into usable models
• Deliver data to dashboards and applications
• Monitor failures and retries
Stack:
• Python and SQL
• PostgreSQL
• Airflow and dbt
• BigQuery and Snowflake
• Kafka
• Power BI
I have built reporting pipelines for operational, client, vendor, appointment, and financial data.
𝟳. 𝗙𝘂𝗹𝗹-𝗦𝘁𝗮𝗰𝗸 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
I can build complete AI products with:
• React or Next.js frontends
• FastAPI backends
• Authentication
• Databases and APIs
• Document ingestion
• Dashboards and subscriptions
• Vector search
• Cloud deployment
𝟴. 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀
Python, FastAPI, LangGraph, LangChain, OpenAI, Claude, RAG, AI agents, Retell AI, Twilio, ElevenLabs, Whisper, n8n, Make, PostgreSQL, Neo4j, Pinecone, ChromaDB, Airflow, dbt, Kafka, BigQuery, Snowflake, Power BI, MLflow, PyTorch, TensorFlow, React, Next.js, Docker, AWS, GCP, Azure, REST APIs, GraphQL, and CI/CD.
✅ 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸
• Understand the workflow, data, integrations, and success criteria
• Identify failure conditions, security needs, and cost limits
• Design an architecture suited to the current project stage
• Build with validation, retries, monitoring, and documentation
• Keep the solution practical and avoid unnecessary complexity
Send me the AI product, workflow, or data problem you want to solve, and I’ll help define a practical path to production.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Maaz can start the project.
Delivery time starts when Muhammad Maaz receives requirements from you.
Muhammad Maaz works on your project following the steps below.
Revisions may occur after the delivery date.
Client purchases the project and sends requirements.