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refactor: clean up agent architecture and coordination loop

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Squash merged refactoring of agent architecture.
This commit is contained in:
michael
2026-06-29 22:44:06 +02:00
parent 7f66d09d9e
commit edc8415571
8 changed files with 760 additions and 812 deletions
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core/agent.py
+20 -11
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@@ -2,9 +2,10 @@ import asyncio
import logging
import lmstudio as lms
from typing import Any, Callable
from core.interfaces import Agent
from .prompt import CAVEMAN_PROMPT
logger: logging.Logger = logging.getLogger("agent-caveman")
logger: logging.Logger = logging.getLogger("agent-base")
class _ActResponseCapture:
@@ -53,17 +54,17 @@ class _ActResponseCapture:
return '\n'.join(self.responses) if self.responses else "No response captured."
class CavemanAgent:
"""Caveman AI agent - minimal token usage variant."""
class BaseAgent(Agent):
"""Base AI agent implementing common LMStudio interaction patterns."""
def __init__(self, model_name: str) -> None:
self.model_name: str = model_name
self.model: Any | None = None
self.system_prompt: str = CAVEMAN_PROMPT
self.system_prompt: str = ""
async def initialize(self) -> None:
"""Initialize the LM Studio model."""
logger.info(f"Initializing CavemanAgent with model: {self.model_name}")
logger.info(f"Initializing agent with model: {self.model_name}")
self.model = lms.llm(self.model_name)
async def run(self, user_input: str) -> str:
@@ -78,12 +79,12 @@ class CavemanAgent:
]
try:
logger.info(f"Running CavemanAgent interactively (input length: {len(user_input)})")
logger.info(f"Running agent interactively (input length: {len(user_input)})")
response = await self.model.respond(user_input, messages=messages)
logger.info(f"CavemanAgent responded successfully (response length: {len(response)})")
logger.info(f"Agent responded successfully (response length: {len(response)})")
return response
except Exception as e:
logger.error(f"CavemanAgent execution error: {e}")
logger.error(f"Agent execution error: {e}")
return f"Error in agent execution: {str(e)}"
async def run_with_tools(self, user_input: str, tools: list[Any]) -> str:
@@ -94,14 +95,22 @@ class CavemanAgent:
try:
capture = _ActResponseCapture()
logger.info(f"Calling LMStudio act() on CavemanAgent with {len(tools)} tools...")
logger.info(f"Calling LMStudio act() on agent with {len(tools)} tools...")
result: lms.ActResult = self.model.act(user_input, tools=tools, on_message=capture)
logger.info(f"act() on CavemanAgent returned: {result}")
logger.info(f"act() on agent returned: {result}")
response: str = capture.full_response
if not response or response == "No response captured.":
logger.warning(f"Act completed with {result.rounds} rounds but no response was captured.")
return f"Act completed with {result.rounds} rounds but no response captured."
return response
except Exception as e:
logger.error(f"CavemanAgent tool execution error: {e}")
logger.error(f"Agent tool execution error: {e}")
return f"Error in agent tool execution: {str(e)}"
class CavemanAgent(BaseAgent):
"""Caveman AI agent - minimal token usage variant."""
def __init__(self, model_name: str) -> None:
super().__init__(model_name)
self.system_prompt = CAVEMAN_PROMPT