langgraph/how-tos/human_in_the_loop/breakpoints/

[giscus[bot]]

langgraph/how-tos/human_in_the_loop/breakpoints/

Build language agents as graphs

https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/

[hasnainali659]

from langgraph_state import GraphState
from langgraph.graph import END, StateGraph
from langgraph.checkpoint.memory import MemorySaver

from nodes import build_strategy, human_feedback, decision_node, rag_node, database_search, web_search_node, sql_search_node, state_printer
from edges import rag_database_websearch_sqlseqrch, strategy_decision

workflow = StateGraph(GraphState)

workflow.add_node("build_strategy", build_strategy)
workflow.add_node("human_feedback", human_feedback)
workflow.add_node("decision_node", decision_node)

workflow.add_node("rag_node", rag_node)
workflow.add_node("database_search", database_search)
workflow.add_node("web_search_node", web_search_node)
workflow.add_node("sql_search_node", sql_search_node)
workflow.add_node("state_printer", state_printer)

workflow.set_entry_point("build_strategy")

workflow.add_edge("build_strategy", "human_feedback")

workflow.add_conditional_edges(
"human_feedback",
strategy_decision,
{
"yes": "decision_node",
"no": "build_strategy"
},
)

workflow.add_conditional_edges(
"decision_node",
rag_database_websearch_sqlseqrch,
{
"rag": "rag_node",
"database_search": "database_search",
"web_search": "web_search_node",
"sql_search": "sql_search_node",
"state_printer": "state_printer"
},
)

workflow.add_conditional_edges(
"rag_node",
rag_database_websearch_sqlseqrch,
{
"rag": "rag_node",
"database_search": "database_search",
"web_search": "web_search_node",
"sql_search": "sql_search_node",
"state_printer": "state_printer"
},
)

workflow.add_conditional_edges(
"database_search",
rag_database_websearch_sqlseqrch,
{
"rag": "rag_node",
"database_search": "database_search",
"web_search": "web_search_node",
"sql_search": "sql_search_node",
"state_printer": "state_printer"
},
)

workflow.add_conditional_edges(
"web_search_node",
rag_database_websearch_sqlseqrch,
{
"rag": "rag_node",
"database_search": "database_search",
"web_search": "web_search_node",
"sql_search": "sql_search_node",
"state_printer": "state_printer"
},
)

workflow.add_conditional_edges(
"sql_search_node",
rag_database_websearch_sqlseqrch,
{
"rag": "rag_node",
"database_search": "database_search",
"web_search": "web_search_node",
"sql_search": "sql_search_node",
"state_printer": "state_printer"
},
)

workflow.add_edge("state_printer", END)

memory = MemorySaver()

app = workflow.compile(checkpointer=memory, interrupt_before=["human_feedback"])

I have created a langgraph with human feedback. After strategy node is executed. It should interrupt before human_feedback node and ask from the user that if the strategy made by the agent is correct or wrong in case if it is correct it will proceed to the next nodes and if not then it will go to strategy node again.

For my first condition in case it is correct it is working fine. But when it is wrong the strategy node executes but donot go to the next nodes and the program terminates.

This is the issue if anyone can help. Thanks

[hwchase17]

how are you updating state from the user to say if the strategy is good or bad?

[TillSimon]

Does the process have to remain active? Or can I also persist the state, terminate the agent and then, once human approval is given, restart it and continue where it left off?