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$
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I will architect a langgraph multi agent ai system for enterprise using fastapi
Awais Hyder
About this gig
Stop shipping brittle LLM wrappers. I architect stateful LangGraph Multi-Agent Compound AI systems that survive production.
Most LLM wrappers fail at scale: linear prompt chains with no memory, no recovery, no control. One timeout or bad tool call collapses the workflow. Enterprise work needs cyclical reasoning - agents that plan, act, check, retry, and route - with state at every step.
What you get:
- LangGraph - deterministic state machines, supervisor node routing, cyclical graphs, checkpointing
- FastAPI - asynchronous endpoints, typed contracts, background orchestration
- Model Context Protocol (MCP) - isolated servers over Streamable HTTP / JSON-RPC, schema-validated tools
- Vector Databases - Pinecone/Milvus hybrid retrieval, reranking, citation-grounded RAG
Risk is engineered out: Human-in-the-Loop approvals before financial writes, sandboxed tool execution, per-agent tracing, crash-safe resume from checkpoints.
Message me before ordering so I can map your workflow, state schema, and tool contracts - and tell you what should be agentic and what should stay deterministic.
I build production LangGraph systems with MCP tools, checkpointing, and human approvals.
Get to know Awais Hyder
Awais Hyder
AI Systems Architect LangGraph, MCP and Enterprise Agentic Workflows
- FromPakistan
- Member sinceApr 2016
- Last delivery1 year
Languages
English
I design production-grade Multi-Agent Swarms for enterprises using LangGraph and MCP. Most AI projects die as brittle demos; I build backend systems engineered to survive production.
My expertise covers state machines with checkpointing, FastAPI layers, and MCP servers over Streamable HTTP (JSON-RPC) for secure ERP/CRM integrations. I enforce Human-in-the-Loop (HITL) governance for financial transactions and critical data.
I deliver infrastructure for lead qualification, reconciliation, and automation. Message me before ordering to discuss your architecture and constraints.
My Portfolio
FAQ
How does LangGraph handle state management at scale?
Via deterministic state machines with durable checkpointing. A supervisor node routes between agents, state persists after crashes, and each thread resumes exactly from its last checkpoint.
Do you build MCP servers with JSON-RPC tool calling?
Yes. I build Model Context Protocol servers over Streamable HTTP using JSON-RPC, with strict JSON Schema validation so high-privilege tools stay isolated from the LLM.
Can FastAPI serve asynchronous endpoints for agents?
Yes. FastAPI exposes asynchronous endpoints with typed contracts and background orchestration, so agent runs, tool calls, and long workflows never block your app.
How do you prevent unsafe tool calls in production?
Through sandboxed tool execution, schema validation, per-agent tracing, and Human-in-the-Loop approvals via interrupt() before any financial or critical write executes.
