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Multi-Agent Systems with LangGraph

Advanced 2 hours English Course Certificate
SkillsMulti-agent systemsLangGraphAgentic AIAI orchestrationPythonLLM applications

Is this course right for you?

Our take
The deep end of agentic AI from DataCamp — where single agents give way to coordinated teams of them.

Good for: Building multi-agent systems with LangGraph.

Skip if: You are new to LLMs/agents or cannot code Python.

In about two hours you design and build agents with LangGraph, then progress from a single-agent system to a multi-agent architecture, ending with a working assistant. It's advanced and genuinely hands-on, which sets a clear prerequisite: you'll want LangChain and LangGraph basics already, so it's the wrong entry point if you're new to LLMs or agents, or you can't code Python.

As the top of the agentic progression it pays to arrive prepared. Beyond the DataCamp subscription (about $14/month billed annually, first chapter free), every model call your agents make is billed by the provider — and a team of agents fires off far more calls than a single one, so this is a running cost worth watching closely. LangGraph evolves rapidly, so confirm the methods match the current version (as of 2026).

Comparison · LBS

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About this course

Multi-Agent Systems with LangGraph is the deep end of agentic AI. In about two hours you design and build AI agents with LangGraph, then progress from single-agent systems to multi-agent architectures — culminating in an assistant that analyses Fortune 500 stock and company data and produces visualizations.

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Instructor

JC
James Chapman
DataCamp instructor

Created by James Chapman, DataCamp's AI curriculum manager, focused on agentic AI and the LangChain/LangGraph ecosystem.

Frequently asked questions

No. It is an advanced topic — building systems where multiple AI agents coordinate, using the LangGraph framework — so it assumes you already know Python and understand large-language-model and agent basics. It is not an entry point to AI. Without that background, the material moves too fast; you would first want foundational Python, LLM, and single-agent experience before tackling multi-agent orchestration.

Multi-agent AI applications with LangGraph: systems where several agents, each with roles and tools, collaborate to handle complex tasks — coordinating steps, passing information, and managing workflows that a single agent could not handle well. Because DataCamp is interactive, you build these in code as you go, so you finish having actually implemented multi-agent workflows rather than only reading about the concept.

Likely small ones. Agent systems call large-language-model APIs, which charge by usage, so running the projects can incur modest API costs beyond any course subscription — usually a few dollars for coursework if you are careful. You can limit spend with lighter models and careful testing, but budget a little for API credit if you want to build and run the agents fully.

Partly. DataCamp's free tier gives you the first chapter, and there is usually a full-access free-trial week, but completing the course requires a Premium subscription (around 25 US dollars a month). Note too the separate, usage-based API costs for running the agents. So you can sample it free, but finishing it and running the projects has real costs.

Yes — you get a DataCamp statement of accomplishment for the course. Treat it as a marker of effort rather than a formal credential; in AI, demonstrable projects matter far more to employers. The genuine value is being able to design and build multi-agent systems with LangGraph, which you show through what you build rather than the certificate.
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