From 48f3d0e167de2915b3630905cc9d4c4985e69401 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, 28 May 2026 13:41:28 +0000
Subject: [PATCH] add agent.py

---
 agent.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 agent.py

diff --git a/agent.py b/agent.py
new file mode 100644
index 0000000..a8429b5
--- /dev/null
+++ b/agent.py
@@ -0,0 +1,66 @@
+import os
+from typing import Dict, Any
+import requests
+from langchain.llms.openai import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+# Simple web search tool using DuckDuckGo instant answer API
+class WebSearch:
+    def __init__(self):
+        self.base = "https://api.duckduckgo.com/"
+
+    def run(self, query: str) -> str:
+        params = {
+            "q": query,
+            "format": "json",
+            "no_redirect": 1,
+            "no_html": 1,
+            "skip_disambig": 1,
+        }
+        r = requests.get(self.base, params=params)
+        data = r.json()
+        # Return the abstract text if available
+        return data.get("AbstractText", "") or data.get("RelatedTopics", [])[0].get("Text", "")
+
+# Agent that searches and writes a virtual file
+class DeepAgent:
+    def __init__(self, llm: Any):
+        self.llm = llm
+        self.search = WebSearch()
+        self.virtual_fs: Dict[str, str] = {}
+
+    def run(self, task_description: str) -> None:
+        # Step 1: Search the web for relevant info
+        search_query = f"{task_description} example"
+        context = self.search.run(search_query)
+        if not context:
+            context = "No context found."
+
+        # Step 2: Ask LLM to generate file content based on context
+        prompt = PromptTemplate(
+            input_variables=["context", "task_description"],
+            template="You are a developer. Based on the following context, write a Python file named output.py that demonstrates the concept described in the task: {task_description}\nContext: {context}\nOutput:\n"
+        )
+        chain = LLMChain(llm=self.llm, prompt=prompt)
+        result = chain.run(context=context, task_description=task_description)
+
+        # Step 3: Store in virtual FS
+        self.virtual_fs["output.py"] = result.strip()
+
+    def export_to_real_fs(self, repo_path: str) -> None:
+        for filename, content in self.virtual_fs.items():
+            full_path = os.path.join(repo_path, filename)
+            with open(full_path, "w", encoding="utf-8") as f:
+                f.write(content)
+
+# Example usage
+if __name__ == "__main__":
+    llm = OpenAI(temperature=0.2, model_name="gpt-4o-mini")
+    agent = DeepAgent(llm)
+    task_desc = "Create a simple Python script that prints 'Hello World'"
+    agent.run(task_desc)
+    # Export to repository directory
+    repo_dir = os.path.abspath(".")
+    agent.export_to_real_fs(repo_dir)
+    print("Files written:", list(agent.virtual_fs.keys()))