Build a Multi-Agent Data Pipeline in 50 Lines of Neam

In this tutorial, you'll build a working multi-agent data pipeline using Neam, an agentic AI programming language. By the end, you'll have a DIO orchestrating five agents through a churn prediction workflow.

Step 1: Define Your Infrastructure Profile. This tells every agent where data lives and what compliance rules apply:

infrastructure_profile MyInfra {  data_warehouse: {  platform: "postgres",  connection: env("DB_URL")  },  governance: { regulations: ["GDPR"] } }

infrastructure_profile MyInfra {  data_warehouse: {  platform: "postgres",  connection: env("DB_URL")  },  governance: { regulations: ["GDPR"] } }

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Step 2: Declare Your Agents. Each agent is a specialist. Note the budget constraints:

budget B { cost: 50.00, tokens: 500000 }

databa agent MyBA { provider: "openai", model: "gpt-4o", budget: B } datascientist agent MyDS { provider: "openai", model: "gpt-4o", budget: B } datatest agent MyDT { provider: "openai", model: "gpt-4o", budget: B }`

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Step 3: Wire Up the DIO. The orchestrator coordinates everything:

budget DioBudget { cost: 500.00, tokens: 2000000 }

dio agent MyDIO { mode: "hybrid", task: "Predict customer churn, identify drivers", infrastructure: MyInfra, agent_md: "./my_domain.agent.md", provider: "openai", model: "gpt-4o", budget: DioBudget }

let result = dio_solve(MyDIO, task) print(result)`

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Step 4: Create Your Agent.MD. This is the secret weapon — encode domain knowledge:

## @organization-context Company: My E-Commerce Co Scale: 500K customers, 5M orders

## @organization-context Company: My E-Commerce Co Scale: 500K customers, 5M orders

@known-data-issues

• signup_date timezone drift before 2024-03
• Product ratings skew positive (self-reported)

@agent-preferences

DataScientist: XGBoost for tabular, AUC-ROC metric`

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Run it: neam-cli run my_pipeline.neam

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