From 5d4ff87d684c041bec582d956b3ec38b1cd10f5b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D0=B4=D0=B5=D0=BB=D0=B8=D0=BD=D0=B0=20=D0=A1=D0=B0?=
 =?UTF-8?q?=D1=82=D1=82=D0=B0=D1=80=D0=BE=D0=B2=D0=B0?=
 <sattarova2509@gmail.com>
Date: Thu, 4 Jun 2026 13:31:29 +0000
Subject: [PATCH] add main

---
 src/main.py | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 src/main.py

diff --git a/src/main.py b/src/main.py
new file mode 100644
index 0000000..9a21fc2
--- /dev/null
+++ b/src/main.py
@@ -0,0 +1,53 @@
+"""
+Deep Agents from Scratch example.
+This script demonstrates a simple agent that searches the web and writes results to files.
+It uses the `deep-agents-from-scratch` package as required by the assignment.
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+# Ensure dependencies are available
+try:
+    from deep_agents_from_scratch.research_tools import tavily_search, think_tool
+except Exception as e:  # pragma: no cover - defensive
+    raise RuntimeError("deep-agents-from-scratch not installed") from e
+
+from langchain.agents import create_agent
+from langchain.chat_models import init_chat_model
+from deep_agents_from_scratch.state import DeepAgentState
+from deep_agents_from_scratch.file_tools import ls, read_file, write_file
+
+# Simple prompt for the agent
+SYSTEM_PROMPT = """
+You are a research assistant. Use web search to gather information and store results in files.
+After each search, reflect on what you found.
+"""
+
+model = init_chat_model(model="anthropic:claude-sonnet-4-20250514", temperature=0)
+
+# Tools available to the agent
+TOOLS = [tavily_search, think_tool, ls, read_file, write_file]
+
+agent = create_agent(
+    model,
+    TOOLS,
+    system_prompt=SYSTEM_PROMPT,
+    state_schema=DeepAgentState,
+)
+
+def run_query(query: str) -> None:
+    """Run a single query and print the resulting messages."""
+    result = agent.invoke({"messages": [{"role": "user", "content": query}]})
+    for msg in result["messages"]:
+        print(msg.content)
+
+if __name__ == "__main__":  # pragma: no cover - entry point
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Run deep agent example")
+    parser.add_argument("query", help="Query to search for")
+    args = parser.parse_args()
+    run_query(args.query)