LangGraph create_react_agent example (#279)

An example conversational agent built with LangGraph.

Author: DevonFulcher

.changes/unreleased/Under the Hood-20250811-163302.yaml

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+kind: Under the Hood
+body: LangGraph create_react_agent example
+time: 2025-08-11T16:33:02.306539-05:00

examples/langgraph_agent/.python-version

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+3.13

examples/langgraph_agent/README.md

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+# LangGraph Agent Example
+
+This is a simple example of how to create conversational agent with remote dbt MCP & LangGraph.
+
+## Usage
+
+1. Set an `ANTHROPIC_API_KEY` environment variable with your Anthropic API key.
+2. Run `uv run main.py`.

examples/langgraph_agent/__init__.py

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+# mypy: ignore-errors
+
+import asyncio
+import os
+
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.prebuilt import create_react_agent
+
+
+def print_stream_item(item):
+    if "agent" in item:
+        content = [
+            part
+            for message in item["agent"]["messages"]
+            for part in (
+                message.content
+                if isinstance(message.content, list)
+                else [message.content]
+            )
+        ]
+        for c in content:
+            if isinstance(c, str):
+                print(f"Agent > {c}")
+            elif "text" in c:
+                print(f"Agent > {c['text']}")
+            elif c["type"] == "tool_use":
+                print(f"    using tool: {c['name']}")
+
+
+async def main():
+    url = f"https://{os.environ.get('DBT_HOST')}/api/ai/v1/mcp/"
+    headers = {
+        "x-dbt-user-id": os.environ.get("DBT_USER_ID"),
+        "x-dbt-prod-environment-id": os.environ.get("DBT_PROD_ENV_ID"),
+        "x-dbt-dev-environment-id": os.environ.get("DBT_DEV_ENV_ID"),
+        "Authorization": f"token {os.environ.get('DBT_TOKEN')}",
+    }
+    client = MultiServerMCPClient(
+        {
+            "dbt": {
+                "url": url,
+                "headers": headers,
+                "transport": "streamable_http",
+            }
+        }
+    )
+    tools = await client.get_tools()
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tools,
+        # This allows the agent to have conversational memory.
+        checkpointer=InMemorySaver(),
+    )
+    # This config maintains the conversation thread.
+    config = {"configurable": {"thread_id": "1"}}
+    while True:
+        user_input = input("User > ")
+        async for item in agent.astream(
+            {"messages": {"role": "user", "content": user_input}},
+            config,
+        ):
+            print_stream_item(item)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/langgraph_agent/main.py

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+[project]
+name = "langgraph-agent"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "langchain-mcp-adapters>=0.1.9",
+    "langchain[anthropic]>=0.3.27",
+    "langgraph>=0.6.4",
+]