add: main.py — Stream-режим AI-агента

main.py

@@ -0,0 +1,85 @@
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# LLM - OpenRouter
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# Simple backend - filesystem + local shell
+backend = CompositeBackend(
+    [
+        LocalShellBackend(workspace_dir="./workspace"),
+        FilesystemBackend(),
+    ]
+)
+
+# Example tool - echo (replace with real logic if needed)
+@tool
+def echo(query: str) -> str:
+    """Return the received query unchanged."""
+    return query
+
+# Create the deep agent
+agent = create_deep_agent(
+    model=llm,
+    tools=[echo],
+    backend=backend,
+    system_prompt="You are a helpful assistant that streams its answer token by token.",
+)
+
+def format_message(message) -> str:
+    """Convert a LangChain message to a printable string."""
+    if getattr(message, "content", None):
+        return message.content
+    # If the message is a tool call, show the call
+    if getattr(message, "tool_calls", None):
+        tc = message.tool_calls[0]
+        return f"{tc['name']}({tc['args']})"
+    return ""
+
+def format_chunk_message(chunk):
+    """Print a token chunk, adding a separator when the step changes."""
+    message, meta = chunk
+    global current_step
+    step = meta.get("langgraph_step", 0)
+    if step != current_step:
+        current_step = step
+        print("\n--- --- ---\n")
+    if getattr(message, "content", None):
+        print(message.content, end="", flush=True)
+
+async def main():
+    # Prepare the input
+    user_input = "Расскажи, как приготовить борщ, используя инструмент echo для демонстрации."
+    stream = agent.stream(
+        {"messages": [HumanMessage(content=user_input)]},
+        stream_mode=["messages", "updates"],
+    )
+
+    global current_step
+    current_step = 0
+
+    # Iterate over the stream
+    for chunk_type, chunk_data in stream:
+        if chunk_type == "messages":
+            format_chunk_message(chunk_data)
+        elif chunk_type == "updates":
+            # When a model update arrives, print the last model message (tool call or final answer)
+            model_info = chunk_data.get("model")
+            if model_info and "messages" in model_info:
+                last_msg = model_info["messages"][-1]
+                print("\n" + format_message(last_msg))
+    # Ensure the final newline
+    print()
+
+if __name__ == "__main__":
+    asyncio.run(main())