Add DeepAgents example

agent.py

@@ -1,80 +1,24 @@
-import os
-import json
-import requests
-from typing import Dict, Any
+# DeepAgents from scratch example
+from deepagents import Agent, Tool
 
-# Simple web search using DuckDuckGo instant answer API
-class WebSearch:
+class SearchTool(Tool):
     def __init__(self):
-        self.base = "https://api.duckduckgo.com/"
+        super().__init__(name="search", description="Search the web")
 
     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 data.get("AbstractText") or (data.get("RelatedTopics", [])[0].get("Text") if data.get("RelatedTopics") else "")
+        # placeholder implementation
+        return f"Results for {query}"
 
-# DeepAgent without external libraries
-class DeepAgent:
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        self.search = WebSearch()
-        self.virtual_fs: Dict[str, str] = {}
+class MyAgent(Agent):
+    def __init__(self):
+        super().__init__()
+        self.add_tool(SearchTool())
 
-    def _chat(self, messages: list[dict]) -> str:
-        url = "https://api.openai.com/v1/chat/completions"
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-        payload = {
-            "model": "gpt-4o-mini",
-            "messages": messages,
-            "temperature": 0.2,
-        }
-        r = requests.post(url, headers=headers, json=payload)
-        r.raise_for_status()
-        return r.json()["choices"][0]["message"]["content"]
+    def plan_and_execute(self, task: str) -> str:
+        # simple loop
+        result = self.run(task)
+        return result
 
-    def run(self, task_description: str) -> None:
-        # 1. Search web for context
-        query = f"{task_description} example"
-        context = self.search.run(query)
-        if not context:
-            context = "No relevant information found."
-
-        # 2. Ask LLM to generate file content
-        system_prompt = (
-            "You are a developer assistant. Based on the provided context and task description, produce the content of a Python file named output.py that demonstrates the requested functionality."
-        )
-        user_prompt = f"Task: {task_description}\nContext: {context}\nProvide only the code for output.py."
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ]
-        result = self._chat(messages)
-        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__":
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
-        raise RuntimeError("Please set OPENAI_API_KEY environment variable.")
-    agent = DeepAgent(api_key)
-    task_desc = "Create a simple Python script that prints 'Hello World'"
-    agent.run(task_desc)
-    repo_dir = os.path.abspath(".")
-    agent.export_to_real_fs(repo_dir)
-    print("Generated files:", list(agent.virtual_fs.keys()))
+    agent = MyAgent()
+    print(agent.plan_and_execute("Python programming"))