Your example states that the agent follows the workflow LLM → call_func → LLM. However, I’d like to understand the stopping conditions—specifically, how and when the agent terminates based on is_last_step or result_is_true parameters. In my current implementation, the agent only executes once instead of looping as expected.
I'm implementing a workflow where:
The LLM generates text output
A validation function (check_result) evaluates this output
If invalid, the process repeats until either:
Validation passes, or
Maximum repetitions (max_repeat) is reached
Initially, I implemented this using manual graph construction (add_node/add_edges), but now I want to transition to a ReAct agent architecture for better maintainability and performance.
Could you advise on:
Best practices for implementing this validation loop in a ReAct agent framework?
How to properly configure the termination conditions (successful validation or max_repeat)?
Any framework-specific considerations I should be aware of?"
I'm implementing a workflow where:
The LLM generates text output
A validation function (check_result) evaluates this output
If invalid, the process repeats until either:
Validation passes, or
Maximum repetitions (max_repeat) is reached
Initially, I implemented this using manual graph construction (add_node/add_edges), but now I want to transition to a ReAct agent architecture for better maintainability and performance.
Could you advise on:
Best practices for implementing this validation loop in a ReAct agent framework?
How to properly configure the termination conditions (successful validation or max_repeat)?
Any framework-specific considerations I should be aware of?"