Build a Hybrid-Memory Autonomous Agent with Modular Architecture and Tool Dispatch using OpenAI

class MemoryStoreTool(Tool):
   name = "memory_store"
   description = "Save an important fact or piece of information to long-term memory."


   def __init__(self, memory: MemoryBackend):
       self._mem = memory


   def run(self, text: str, category: str = "general") -> str:
       chunk_id = self._mem.store(text, {"category": category})
       return f"Stored as {chunk_id}."


   def schema(self) -> Dict:
       return {
           "type": "function",
           "function": {
               "name": self.name,
               "description": self.description,
               "parameters": {
                   "type": "object",
                   "properties": {
                       "text":     {"type": "string", "description": "The fact to remember."},
                       "category": {"type": "string", "description": "Category tag, e.g. 'user_pref', 'task', 'fact'."},
                   },
                   "required": ["text"],
               },
           },
       }




class MemorySearchTool(Tool):
   name = "memory_search"
   description = "Search long-term memory for information relevant to a query."


   def __init__(self, memory: MemoryBackend):
       self._mem = memory


   def run(self, query: str, top_k: int = 3) -> str:
       results = self._mem.search(query, top_k=top_k)
       if not results:
           return "No relevant memories found."
       lines = [f"[{r['id']}] (score={r['rrf_score']}) {r['text']}" for r in results]
       return "Relevant memories:n" + "n".join(lines)


   def schema(self) -> Dict:
       return {
           "type": "function",
           "function": {
               "name": self.name,
               "description": self.description,
               "parameters": {
                   "type": "object",
                   "properties": {
                       "query": {"type": "string", "description": "What to look for."},
                       "top_k": {"type": "integer", "description": "Max results (default 3)."},
                   },
                   "required": ["query"],
               },
           },
       }




class CalculatorTool(Tool):
   name = "calculator"
   description = "Evaluate a safe mathematical expression, e.g. '2 ** 10 + sqrt(144)'."


   def run(self, expression: str) -> str:
       allowed = {k: getattr(math, k) for k in dir(math) if not k.startswith("_")}
       allowed.update({"abs": abs, "round": round})
       try:
           result = eval(expression, {"__builtins__": {}}, allowed)
           return str(result)
       except Exception as exc:
           return f"Error: {exc}"


   def schema(self) -> Dict:
       return {
           "type": "function",
           "function": {
               "name": self.name,
               "description": self.description,
               "parameters": {
                   "type": "object",
                   "properties": {
                       "expression": {"type": "string", "description": "Math expression to evaluate."},
                   },
                   "required": ["expression"],
               },
           },
       }




class WebSnippetTool(Tool):
   name = "web_search"
   description = "Search the web for current information on a topic (simulated)."


   _KB = {
       "openai": "OpenAI is an AI safety company that develops the GPT family of models.",
       "rag": "Retrieval-Augmented Generation (RAG) combines a retrieval system with an LLM to ground answers in external documents.",
       "bm25": "BM25 (Best Match 25) is a probabilistic keyword ranking function used in search engines.",
   }


   def run(self, query: str) -> str:
       q = query.lower()
       for kw, snippet in self._KB.items():
           if kw in q:
               return f"Web snippet for '{query}': {snippet}"
       return f"No snippet found for '{query}'. (Mock tool — integrate a real search API here.)"


   def schema(self) -> Dict:
       return {
           "type": "function",
           "function": {
               "name": self.name,
               "description": self.description,
               "parameters": {
                   "type": "object",
                   "properties": {
                       "query": {"type": "string", "description": "Search query."},
                   },
                   "required": ["query"],
               },
           },
       }




@dataclass
class AgentPersona:
   name: str
   role: str
   traits: List[str]
   forbidden_phrases: List[str] = field(default_factory=list)
   goals: List[str] = field(default_factory=list)


   def compile_system_prompt(self, extra_context: str = "") -> str:
       lines = [
           f"You are {self.name}, {self.role}.",
           "",
           "## Core Traits",
           *[f"- {t}" for t in self.traits],
       ]
       if self.goals:
           lines += ["", "## Goals", *[f"- {g}" for g in self.goals]]
       if self.forbidden_phrases:
           lines += ["", "## Forbidden Phrases (never say these)", *[f"- "{p}"" for p in self.forbidden_phrases]]
       if extra_context:
           lines += ["", "## Live Context", extra_context]
       lines += [
           "",
           "## Behaviour",
           "- Always reason step-by-step before answering.",
           "- Use available tools proactively; never guess when you can look up.",
           "- After using memory_search, quote the retrieved ID in your answer.",
           "- Keep answers concise unless depth is explicitly requested.",
       ]
       return "n".join(lines)




ARIA = AgentPersona(
   name="Aria",
   role="a precise, helpful research assistant with a hybrid memory system",
   traits=["Methodical", "Curious", "Transparent about uncertainty", "Concise"],
   goals=[
       "Remember and connect information across conversations",
       "Use tools whenever they can improve accuracy",
   ],
   forbidden_phrases=["I cannot", "As an AI language model"],
)


print("✅  Tools and AgentPersona ready.")