Agent Virtual Machine
  • Getting Started
    • Introduction
    • Roadmap
  • Use Cases
    • Code Generation Evaluations
    • Data Analysis
    • Data Extraction
    • Data Transformation
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  • Data Transformation
  • ​Use Cases
  • ​Scenario: Custom Schema Delivery
  • ​Implementation: Schema-Driven Codegen
  1. Use Cases

Data Transformation

Agent-led data transformation using AVM

PreviousData Extraction

Last updated 5 days ago

Objective: Enable agent-led data transformation through LLM-generated scripts executed on AVM.

Data Transformation

Automate conversion pipelines by having an LLM produce transformation functions, then execute safely via AVM.

Use Cases

Web2: CSV → JSON → Notion

Transform business data across different formats and integrate with productivity tools.

Web3: Normalize Wallet Data → Subgraphs

Process blockchain wallet data and prepare it for indexing in decentralized subgraphs.

Scenario: Custom Schema Delivery

Clients request data in bespoke JSON structures without manual coding.

Implementation: Schema-Driven Codegen

  1. Define Schema Provide a JSON Schema template.

  2. Generate Code Prompt the LLM to write an execute(input) function matching the schema.

  3. Secure Run Execute the function in AVM’s sandbox.

  4. Deliver Output Return parsed JSON conforming to the schema.

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