I will build a production ready ai agent with langgraph and tool calling

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akashlp
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akashlp
Akash L

About this gig

I build AI agents that survive contact with real users.


Most agent demos break the moment the model returns a malformed tool call. I build the layer that stops that: strict tool schemas, enum validation, cross-field correction, and graceful degradation instead of a stack trace.


What I have shipped: sole author of a multi-agent LangGraph assistant running in production at a US supply-chain platform. Natural language in, validated calls across tracking, geo and address APIs out, over a 125-parameter tool surface. It survived a full re-platform and still runs today.


WHAT YOU GET

A working agent in your repo, not a notebook. Tool schemas designed around your actual API. Structured output validation and fallback handling. FastAPI endpoint, Dockerfile and README. A call to walk you through the code.


STACK: Python, LangGraph, LangChain, OpenAI and open models, Pydantic, FastAPI, Qdrant, Docker, AWS.


Message me before ordering with what you are building. If it is not a fit, I will tell you.

Get to know Akash L

Akash L

AI Engineer, LLM Agents, RAG and MLOps

  • FromIndia
  • Member sinceMar 2020
  • Languages

    Tamil, English
AI engineer with 4 years building LLM systems that run in production, not just demos. I was sole author of a multi-agent LangGraph assistant for a US supply-chain platform. Plain-English questions turned into validated calls across a 125-parameter tool surface. It survived a re-platform and still runs today. I build the unglamorous parts too: RAG pipelines, tool schemas, validation that degrades gracefully instead of crashing, and eval harnesses that catch regressions before your users do. Python, LangGraph, LangChain, RAG, FastAPI, AWS. Tell me what you are building.