create-agent

Create ReAct Agent

Generate a complete ReAct agent with tool calling, ToolNode integration, and conditional routing.

Instructions for Claude

1. Gather Requirements

Ask user for:

• Agent name (if not provided)
• Tools to include (if --tools not provided):
  • Search (web search)
  • Calculator (math operations)
  • Custom tools (user-defined)
• Purpose/domain of the agent

2. Read Settings

Check .claude/langgraph-dev.local.md for:

• llm_provider and llm_model
• async_by_default
• Code style preferences

3. Generate Agent Structure

Create:

{agent_name}/
├── agent.py        # ReAct agent implementation
├── tools.py        # Tool definitions
├── state.py        # Agent state schema
└── README.md

4. Generate state.py

from typing import TypedDict, Annotated, List
from langchain.schema import BaseMessage
import operator

class AgentState(TypedDict):
    \"\"\"State for ReAct agent.\"\"\"
    messages: Annotated[List[BaseMessage], operator.add]

5. Generate tools.py

from langchain.tools import Tool

def search_web(query: str) -> str:
    \"\"\"Search the web for information.\"\"\"
    # Implementation
    return f"Search results for: {query}"

search_tool = Tool(
    name="WebSearch",
    func=search_web,
    description="Useful for finding current information. Input: search query."
)

# Add other tools based on user selection
tools = [search_tool, ...]

6. Generate agent.py

from langgraph.prebuilt import ToolNode
from langgraph.graph import StateGraph, END
from langchain_anthropic import ChatAnthropic
from .tools import tools
from .state import AgentState

# LLM with tools
llm = ChatAnthropic(model="claude-sonnet-4-5")
llm_with_tools = llm.bind_tools(tools)

# Agent node
def agent_node(state: AgentState):
    response = llm_with_tools.invoke(state["messages"])
    return {"messages": [response]}

# Tool execution node
tool_node = ToolNode(tools)

# Routing logic
def should_continue(state: AgentState):
    last_message = state["messages"][-1]
    if last_message.tool_calls:
        return "tools"
    return END

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)

workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue, {
    "tools": "tools",
    END: END
})
workflow.add_edge("tools", "agent")  # Loop back

app = workflow.compile()