You will get a Production-Ready LangGraph Multi-Agent System in Python & FastAPI
Project details
Stop relying on fragile prompt chains that lose state or hallucinate tool calls. I build production-ready, stateful multi-agent systems using LangGraph, Python, and FastAPI designed for complex multi-step execution.
What you get with this service:
• Custom LangGraph Multi-Agent Architecture (Supervisor, Worker, & Reviewer nodes)
• Stateful Workflow Persistence with explicit checkpointers and memory
• Human-in-the-Loop Routing for critical approvals and fallback handling
• Asynchronous FastAPI REST API endpoints with strict Pydantic validation
• Observability setup with LangSmith tracing to track execution, latency, and cost
Ideal for founders and engineering teams looking to deploy reliable AI agents into enterprise workflows, SaaS platforms, or internal tools. Clean code, clear architecture, and zero surprises.
What you get with this service:
• Custom LangGraph Multi-Agent Architecture (Supervisor, Worker, & Reviewer nodes)
• Stateful Workflow Persistence with explicit checkpointers and memory
• Human-in-the-Loop Routing for critical approvals and fallback handling
• Asynchronous FastAPI REST API endpoints with strict Pydantic validation
• Observability setup with LangSmith tracing to track execution, latency, and cost
Ideal for founders and engineering teams looking to deploy reliable AI agents into enterprise workflows, SaaS platforms, or internal tools. Clean code, clear architecture, and zero surprises.
AI Development Type
Knowledge RepresentationAI Tools
Open Neural Network Exchange, SonnetAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$349
|
Standard
$749
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
About Muhammad
LangGraph Multi-Agent Engineer | Enterprise RAG | Voice AI & MCP
Lahore, Pakistan - 4:47 pm local time
I engineer production-ready, stateful multi-agent systems and enterprise RAG pipelines using LangGraph, LangChain, and FastAPI built for multi-step execution without hallucinations.
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⚙️ CORE TECHNICAL CAPABILITIES
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• Stateful Multi-Agent Workflows: Designing graph architectures in LangGraph with explicit state checkpointers, human-in-the-loop nodes, and conditional tool routing.
• Enterprise RAG Architecture: Building hybrid retrieval pipelines (dense + sparse search), metadata filtering, reranking, and citation guardrails using Pinecone, Qdrant, and pgvector.
• Scalable AI Backends: Asynchronous FastAPI microservices with strict Pydantic schema validation, rate-limiting, and WebSockets for real-time streaming.
• Evaluation & Observability: Implementing LangSmith tracing, prompt benchmarking, and hallucination evaluations to optimize latency and token costs.
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⚡ SELECTED PRODUCTION BUILDS
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• Automotive AI Valuation Engine: Reduced vehicle inspection manual overhead by 32% via automated multi-modal analysis & pricing logic.
• EdTech Exam Intelligence System: Automated curriculum-aligned question generation, cutting test creation time from hours to seconds.
• Clinical Support RAG Agent: Deployed a zero-hallucination document agent handling 100+ daily patient query workflows without manual oversight.
• Real-Time Business Analytics Engine: Built an enterprise reporting suite running on FastAPI + Next.js for sub-second data synthesis.
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🛠️ CORE TECH STACK
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• Orchestration: LangGraph, LangChain
• Backends & Frameworks: Python, FastAPI, REST APIs, WebSockets, Next.js
• Vector DBs & Search: Pinecone, Qdrant, Weaviate, pgvector, FAISS
• Foundation Models: Claude API (Anthropic), OpenAI GPT-4o, Gemini API
• Infra & DBs: PostgreSQL, Supabase, Redis, Docker, AWS
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📩 NEXT STEPS
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Have an agent workflow breaking in production, or need a non-hallucinating RAG engine built from scratch?
Message me with a brief summary of your architecture or business requirement. I will provide a technical breakdown, feasibility review, and clear delivery timeline.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Architecture Scoping
review your workflow requirements, API documentation, and tool requirements to design the optimal LangGraph state graph.
Graph Development & Tool Integration
I construct the agent nodes, implement state checkpointers, write Pydantic schemas, and integrate external tool APIs.