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LangGraph: An intuitive framework for AI agents workflow. (www.linkedin.com)

LangGraph is a novel graph-based framework for AI agent orchestration that presents intuitive management of workflows but faces challenges such as dependency on specific prompts, limitations in data transfer between agents, and the need for explicit guidance for the supervisor agent. Despite these early-stage limitations, it has potential for significant applications, particularly in the domain of capital markets and finance.
Main Points
- A graph framework for agent orchestration aligns intuitively with how networks operateUsing a graph framework for agent orchestration seems a natural fit, so kudos to LangChain/LangGraph for this approach. It makes intuitive sense because, just like friends in a network, every agent with a tool is a node and those nodes need to be related in some way.
- Quality of results heavily dependent on the promptthe quality of my results was too dependent on the prompt, the supervisor agent needed "guidance" to assign the tasks to the agents and I faced limitations on passing the resulting output objects with data from one agent to another, which probably resulted in inconsistent runs.
- LangGraph faces limitations in passing data between agentsAnother limitation was the way the agents handled the data output from the DataAnalyst agent...the messages passed between agents included strings of the observations of the date (date, value), not the data dataframe object itself.
- Supervisor agent requires specific guidanceThe supervisor agent was inconsistent when trying to route the tasks to the correct agent...The supervisor agent behaved better when I changed the Human Message prompt to describe the sequence of tasks, rather than the initial 3-liner prompt describing the end result desired.
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