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I will build custom ai agents and workflows using langgraph
Muhammad Zain
Informazioni su questo servizio
Are you tired of basic OpenAI wrappers that fail to follow business rules, hallucinate, or lose track of conversation context?
To run a business smoothly, your AI needs structure, precise control flow, and persistent memory. I will build production-grade, stateful AI applications using LangGraph and LangChain in Python.
By designing explicit state graphs with robust nodes and conditional edges, I ensure your AI follows strict business logic, auto-corrects its errors, and handles complex multi-step reasoning perfectly.
What I Can Build For You:
- Stateful AI Agents: Systems with reliable short-term and long-term memory that maintain context across multiple interactions.
- Self-Correcting LLM Loops: Workflows where the agent automatically validates its own code or output against your criteria and loops back to retry if it fails.
- Structured Data Pipelines: Agents specialized in parsing, routing, and formatting raw information using explicit conditional routing edges.
My Technical Stack:
- Core Orchestration: LangGraph, LangChain, Python
Why Choose Me?
As an AI and Data Science engineer, I focus on building reliable, deterministic workflows that don't break in production.
Scopri di più su Muhammad Zain
Muhammad Zain
Data Scientist, AI Solutions Engineer, Agentic AI Specialist
- DaPakistan
- Membro dagiu 2024
- Tempo di risposta medio1 ora
Lingue
Urdu, Inglese
Hi, I'm a Data Scientist and Machine Learning Engineer specializing in building predictive models, end-to-end data pipelines, and intelligent AI automation. I focus on turning complex datasets into clean, deployment-ready production code.
𝐌𝐲 𝐂𝐨𝐫𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞:
𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: Data cleaning, preprocessing, and predictive modeling using Python.
𝐀𝐈 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Building reliable, stateful workflows and custom logic loops using LangGraph and LangChain.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Turning backend scripts into interactive Python web applications via Streamlit, flask
Il mio portfolio
FAQ
Why use LangGraph instead of standard LangChain or basic API calls?
Standard APIs run linearly and struggle with cycles (loops). LangGraph allows us to define cycles, loops, and explicit state management, meaning the agent can execute a task, evaluate its own work, and loop back to fix errors until it gets the right result.
Do I need to provide the API keys?
Yes, you will need to provide your own API keys (OpenAI, Anthropic, Gemini, etc.) for testing and deployment. I will show you exactly how to set them up securely as environmental variables.
Can you integrate this into an existing application?
Yes! The graph logic is written in modular Python code, making it highly flexible to integrate into backend systems like Flask or FastAPI frameworks.
