update agent.py

agent.py

@@ -1,11 +1,9 @@
 import os
-from typing import Dict, Any
+import json
 import requests
-from langchain.llms.openai import OpenAI
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
+from typing import Dict, Any
 
-# Simple web search tool using DuckDuckGo instant answer API
+# Simple web search using DuckDuckGo instant answer API
 class WebSearch:
     def __init__(self):
         self.base = "https://api.duckduckgo.com/"
@@ -20,32 +18,47 @@ class WebSearch:
         }
         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", "")
+        return data.get("AbstractText") or (data.get("RelatedTopics", [])[0].get("Text") if data.get("RelatedTopics") else "")
 
-# Agent that searches and writes a virtual file
+# DeepAgent without external libraries
 class DeepAgent:
-    def __init__(self, llm: Any):
-        self.llm = llm
+    def __init__(self, api_key: str):
+        self.api_key = api_key
         self.search = WebSearch()
         self.virtual_fs: Dict[str, str] = {}
 
+    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 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)
+        # 1. Search web for context
+        query = f"{task_description} example"
+        context = self.search.run(query)
         if not context:
-            context = "No context found."
+            context = "No relevant information 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"
+        # 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."
         )
-        chain = LLMChain(llm=self.llm, prompt=prompt)
-        result = chain.run(context=context, task_description=task_description)
-
-        # Step 3: Store in virtual FS
+        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:
@@ -56,11 +69,12 @@ class DeepAgent:
 
 # Example usage
 if __name__ == "__main__":
-    llm = OpenAI(temperature=0.2, model_name="gpt-4o-mini")
-    agent = DeepAgent(llm)
+    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)
-    # Export to repository directory
     repo_dir = os.path.abspath(".")
     agent.export_to_real_fs(repo_dir)
-    print("Files written:", list(agent.virtual_fs.keys()))
+    print("Generated files:", list(agent.virtual_fs.keys()))