ZeroGraph supports a wide range of complex AI workflows, with typical scenarios including:
- Multi-agent collaboration::
- Define multiple
Nodeclass handles different subtasks bynextmethod to connect the node logic. - Example:
NodeAAfter checking the input, jump to the result based on theNodeB(Successful) orNodeC(Failure).
- Define multiple
- Retrieval Augmentation Generation (RAG)::
- combining
AsyncNodeRealize external data retrieval (e.g. API query), and then output the final result by generating nodes. - Example: Search the database first, then generate a summary to answer user questions.
- combining
- batch file task::
- utilization
BatchNodeEfficiently process arrays of data, such as batch cleaning text or parallel computing. - Example: Sentiment analysis of 1000 product descriptions.
- utilization
- asynchronous workflow::
AsyncNodeSupports asynchronous operations such as network requests, file reading and writing to avoid blocking the main thread.- Example: extracting keywords after crawling web content.
The official example catalog (examples/) provides full implementations such as multi-agent Demonstrating multi-agent dialog.rag Demonstrate retrieval-based generation.
This answer comes from the articleZeroGraph: a lightweight AI agent programming frameworkThe
May not be reproduced without permission:AI productivity tools " What complex AI workflows does ZeroGraph support? Can you give examples?
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