I will build custom LangGraph multi-agent AI pipelines in Python

About this Gig

Need a reliable, production-ready AI pipeline built with LangGraph and Python? I specialise in designing and developing multi-agent systems that are structured, maintainable, and built to scale. 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱: Multi-agent LangGraph pipelines with conditional routing Human-in-the-loop (HITL) workflows with interrupt and resume Stateful multi-turn conversation systems with memory Live data retrieval via web search (Tavily, SerpAPI, or custom tools) FastAPI backend to expose agents as a REST API Integration with any LLM — Groq, OpenAI, Anthropic, Gemini Clean, documented Python code you can maintain and extend 𝗪𝗵𝘆 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵? Unlike simple chatbots, LangGraph lets you build agents that make decisions, loop back when results are poor, pause for human input, and maintain context across an entire conversation — giving you far more control over how your AI behaves. 𝗖𝗼𝗺𝗺𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: → Research and data collection assistants → Automated report generation → Customer support agent pipelines → Document analysis and summarisation workflows → Any multi-step AI task that needs reliability and control 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗮𝗯𝗹𝗲𝘀: → Full Python source code → .env setup instructions → README with architecture diagram → Working demo you can run locally

Requirements

To get started, please share: 1. What should the agent do? Describe the task or workflow in plain language. 2. How many agents or steps do you need? (I can advise if unsure) 3. Which LLM do you want to use? - Groq (free tier available) - OpenAI (GPT-4o / GPT-3.5) - Anthropic Claude - Other 4. Do you need a web search tool, database connection, or any external API integrated? 5. How should the output be delivered? - Python script / CLI - REST API (FastAPI) - Connected to an existing app or frontend 6. Do you have existing code I should build on, or is this from scratch?

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