Open sreenivasanm6 opened 1 month ago
I wasn't able to find specific information comparing LlamaIndex and LangGraph for agents in the repository. However, LlamaIndex offers a comprehensive framework for building agentic systems with features like breaking down complex questions, choosing external tools, planning and executing tasks, and storing completed tasks in a memory module. It supports various use cases, provides prebuilt agent architectures, and allows for custom workflows. Additionally, LlamaIndex can be integrated as a tool within other agent frameworks, enhancing its versatility [1][2].
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@dosu not clear in specific to langgraph
@sreenivasanm6 any agentic implementation in llama-index should probably be using workflows
While we do have prebuilts like FunctionCallingAgent to get started quickly, in most cases I see, users need so much customization that it makes sense to build more from-scratch with workflows Workflow Docs Function Calling Agent w/ Streaming, Dynamic Context
I can say that in my own observations/notes
The feedback on workflows has been positive from basically everyone who's tried it. I would say that the only negative that comes to mind is that not every developer is familiar with async+python
Thanks for the info. How about the memory consumption, token limit usage of api calls etc. Will it be less or more when compared to langraph?
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Why should one prefer llama index for agents when compared to langgraph? Please help in providing detail.