From c16e76fb937a25a97545018a4dd3df21c34bf0e5 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 13:19:06 +0000
Subject: [PATCH] Add DeepAgent implementation

---
 src/agent.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 src/agent.py

diff --git a/src/agent.py b/src/agent.py
new file mode 100644
index 0000000..74a695c
--- /dev/null
+++ b/src/agent.py
@@ -0,0 +1,70 @@
+"""
+DeepAgent that searches the web and creates virtual files.
+
+The agent uses langchain's Perplexity wrapper to query the internet.
+It writes results into a temporary directory and finally copies them
+to the real filesystem when finished.
+"""
+
+import os
+import shutil
+from pathlib import Path
+from typing import List, Dict
+
+from langchain_community.utilities.perplexity import PerplexityAPIWrapper
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+# Configuration
+BASE_DIR = Path("/tmp/deepagent")
+OUTPUT_DIR = Path("./output_files")
+
+class DeepAgent:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.llm = PerplexityAPIWrapper(api_key=api_key)
+        self.chain = LLMChain(llm=self.llm, prompt=self._build_prompt())
+        BASE_DIR.mkdir(parents=True, exist_ok=True)
+        OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    def _build_prompt(self) -> PromptTemplate:
+        template = (
+            "You are a web search assistant. Given the query: {query}\n"
+            "Return a JSON object with keys:\n"
+            "- title: short title\n"
+            "- content: full text of the page (max 500 words)\n"
+            "- url: source URL\n"
+        )
+        return PromptTemplate(template=template, input_variables=["query"])
+
+    def search_and_save(self, query: str, filename: str):
+        result = self.chain.run({"query": query})
+        # Parse JSON safely
+        try:
+            import json
+            data = json.loads(result)
+        except Exception as e:
+            raise ValueError(f"Failed to parse LLM output: {e}\n{result}")
+        file_path = BASE_DIR / filename
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(json.dumps(data, ensure_ascii=False, indent=2))
+        return file_path
+
+    def export(self):
+        """Copy all virtual files to OUTPUT_DIR."""
+        for src in BASE_DIR.iterdir():
+            dst = OUTPUT_DIR / src.name
+            shutil.copy(src, dst)
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--api-key", required=True)
+    args = parser.parse_args()
+    agent = DeepAgent(api_key=args.api_key)
+    # Example queries
+    queries = ["Python async programming", "LangChain deep agents"]
+    for i, q in enumerate(queries, 1):
+        agent.search_and_save(q, f"result_{i}.json")
+    agent.export()
+    print("Exported files to", OUTPUT_DIR)