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LangGraph

Integrate LangGraph's graph-based orchestration with Agent Kernel.

Installation​

pip install agentkernel[langgraph]

Basic Usage​

from typing import TypedDict
from langgraph.graph import StateGraph, END
from agentkernel.cli import CLI
from agentkernel.langgraph import LangGraphModule

class State(TypedDict):
    messages: list

def agent_node(state: State):
    # Your logic
    return {"messages": state["messages"] + ["response"]}

# Build graph
workflow = StateGraph(State)
workflow.add_node("agent", agent_node)
workflow.set_entry_point("agent")
workflow.add_edge("agent", END)

# Compile
graph = workflow.compile()
graph.name = "assistant"

LangGraphModule([graph])

if __name__ == "__main__":
    CLI.main()

Complex Graph​

from langgraph.graph import StateGraph, END

# Multi-node graph with conditional routing
workflow = StateGraph(State)
workflow.add_node("analyzer", analyze_node)
workflow.add_node("responder", respond_node)
workflow.add_node("validator", validate_node)

workflow.set_entry_point("analyzer")
workflow.add_conditional_edges(
    "analyzer",
    router_func,
    {
        "respond": "responder",
        "validate": "validator"
    }
)
workflow.add_edge("responder", END)
workflow.add_edge("validator", "responder")

graph = workflow.compile()
graph.name = "complex_agent"

Configuration​

export OPENAI_API_KEY=sk-...

Features​

  • ✅ Graph-based workflows
  • ✅ Conditional routing
  • ✅ State management
  • ✅ Checkpointing
  • ✅ Human-in-the-loop

Example​

See examples/cli/langgraph for complete examples.