GitHub user Aryankb added a comment to the discussion: [Discussion] The
selection of Agentic/Taskflow frame
> For example, suppose the user is already using `AutoGen` to orchestrate their
> Agent system, and now assuming that the data in the graph has been extracted
> (skipping the extraction step), how can they better and faster integrate it
> into their original system, while maintaining high performance and simplicity
> as much as possible
> They(Devs) can directly modify our pipeline/workflow code, instead of
> requiring us to provide a fixed "local/global" mode like Microsoft GraphRAG
> that is not easy to adjust.
@imbajin As per my understanding of 2, we need a way to integrate hugegraph
agents with any other existing framework. And we will provide an http api layer
abstraction or python SDK for hugegraph agents.
For example. Someone working on some projects involving AI agents. They thought
that they needs to use GRAPH RAG for some use case. Now for that, they will be
able to import HG agentic library say HG_agentic. Now using this library, they
can create agents using plane english (what the agent needs to do, given some
input and required output format.) Each agent will have the information about
graph from which information needs to be extracted. And it will have some
inputs - (output from previous agents, like node names , relationship names ,
similarity search results, etc), the agent will convert txt2gql according to
the given prompt and graph structure knowledge. This way (Devs) can create
agents and orchestrate them using our library HG_orchestrator by just passing
all created agents in a data structure.
HG_orchestrator will handle the order of agent execution. User can specify if
he want sequential in case next agent needs outputs from any previous agent ,
parallel otherwise.
If we need this, we need a framework having really good `WORKFLOW FEATURES`.
Because then we can be really flexible. (We can manage the flow of data between
agents, as specified by user, also we can add if-else validation condition,
deligation steps like deligate to previous agent with some modified input.)
Eg :-
`HG_orchestrate([agent1, agent2, agent3], fllow="sequential", data_flow:
{agent1 : [agent2, agent3], agent2 : [agent3]}, deligation: [{from:agent3,
to:agent1 , condition :"some_condition", modification :
"required_modification"}]`
One naive approach can be to do everything using simple python while loop, here
we don't need to manage different dependency conflicts. If (devs) use AutoGen,
and if we create HG_agentic SDK using crewai. Then there might be dependency
conflicts.
If not, then we can follow the below priority order.
Priority suggestion for workflow :-
- 1. Llamaindex (have workflow features like checkpoint, trigger, retries,
streaming, context across runs, with really good example as per our needs)
- 2. pydantic AI
- 3. CrewFlow
- 4. Agno (don't have workflow features)
GitHub link:
https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666605
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