feat: solution for 'Untitled Task'

.gitignore

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+node_modules/
+.env
+dist/
+build/
+*.log

README.md

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+# LangGraph Streaming Agent
+
+This project demonstrates how to use LangGraph's streaming capabilities to display LLM responses token by token in real time.
+
+## Prerequisites
+
+- Python 3.10+
+- An OpenAI API key. Set it in a `.env` file or export `OPENAI_API_KEY`.
+
+## Installation
+
+bash
+git clone <repo-url>
+cd <repo-dir>
+python -m venv .venv
+source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
+pip install -r requirements.txt
+
+
+## Usage
+
+bash
+python src/main.py
+
+
+You will be prompted to enter a question. The answer will stream to the console as it is generated.
+
+## How it works
+
+The script builds a simple LangGraph agent that uses the OpenAI LLM. It calls `agent.stream()` with `stream_mode=['messages', 'updates']` and iterates over the returned chunks, printing each token as it arrives.
+
+## License
+
+MIT

requirements.txt

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+langgraph
+langchain
+langchain-openai
+python-dotenv

src/main.py

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+import os
+from dotenv import load_dotenv
+from langgraph import AgentBuilder
+from langchain_openai import ChatOpenAI
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    raise ValueError("OPENAI_API_KEY not set in environment")
+
+llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
+
+builder = AgentBuilder(llm=llm)
+agent = builder.build()
+
+def format_message(message) -> str:
+    if message.content:
+        return message.content
+    if message.tool_calls:
+        tool = message.tool_calls[0]
+        return f"{tool['name']}({tool['args']})"
+    return ""
+
+step = 1
+
+def format_chunk_message(chunk):
+    global step
+    message, meta = chunk
+    if meta.get("langgraph_step") != step:
+        step = meta.get("langgraph_step")
+        print("\n --- --- --- \n")
+    if message.content:
+        print(message.content, end="", flush=True)
+
+def main():
+    user_input = input("Enter your question: ")
+    stream = agent.stream(
+        {"messages": [{"role": "human", "content": user_input}]},
+        stream_mode=["messages", "updates"]
+    )
+    for chunk_type, chunk_data in stream:
+        if chunk_type == "messages":
+            format_chunk_message(chunk_data)
+        elif chunk_type == "updates":
+            if chunk_data.get("model"):
+                last_message = chunk_data["model"]["messages"][-1]
+                print(format_message(last_message))
+    print("\n\nDone.")
+
+if __name__ == "__main__":
+    main()