Fix agent errors (#24)

* Upgraded jupyterlab_chat

* Fixed open_file tool

* Added run, select cell tools

* Updated tools, prompt to nudge agent to work with chat and active notebook

* Removed middleware

* add jupyterlab_notebook_awareness to deps

---------

Co-authored-by: David L. Qiu <david@qiu.dev>

Author: 3coins

jupyter_ai_jupyternaut/jupyternaut/jupyternaut.py

@@ -1,19 +1,12 @@
 import os
-from typing import Any, Callable
+from typing import Any
 
 import aiosqlite
 from jupyter_ai_persona_manager import BasePersona, PersonaDefaults
 from jupyter_core.paths import jupyter_data_dir
 from jupyterlab_chat.models import Message
 from langchain.agents import create_agent
-from langchain.agents.middleware import AgentMiddleware
-from langchain.agents.middleware.file_search import FilesystemFileSearchMiddleware
-from langchain.agents.middleware.shell_tool import ShellToolMiddleware
-from langchain.messages import ToolMessage
-from langchain.tools.tool_node import ToolCallRequest
-from langchain_core.messages import ToolMessage
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
-from langgraph.types import Command
 
 from .chat_models import ChatLiteLLM
 from .prompt_template import (
@@ -26,76 +19,11 @@
 
 MEMORY_STORE_PATH = os.path.join(jupyter_data_dir(), "jupyter_ai", "memory.sqlite")
 
-JUPYTERNAUT_AVATAR_PATH = str(os.path.abspath(
-    os.path.join(os.path.dirname(__file__), "../static", "jupyternaut.svg")
-))
-
-
-def format_tool_args_compact(args_dict, threshold=25):
-    """
-    Create a more compact string representation of tool call args.
-    Each key-value pair is on its own line for better readability.
-
-    Args:
-        args_dict (dict): Dictionary of tool arguments
-        threshold (int): Maximum number of lines before truncation (default: 25)
-
-    Returns:
-        str: Formatted string representation of arguments
-    """
-    if not args_dict:
-        return "{}"
-
-    formatted_pairs = []
-
-    for key, value in args_dict.items():
-        value_str = str(value)
-        lines = value_str.split('\n')
-
-        if len(lines) <= threshold:
-            if len(lines) == 1 and len(value_str) > 80:
-                # Single long line - truncate
-                truncated = value_str[:77] + "..."
-                formatted_pairs.append(f"  {key}: {truncated}")
-            else:
-                # Add indentation for multi-line values
-                if len(lines) > 1:
-                    indented_value = '\n    '.join([''] + lines)
-                    formatted_pairs.append(f"  {key}:{indented_value}")
-                else:
-                    formatted_pairs.append(f"  {key}: {value_str}")
-        else:
-            # Truncate and add summary
-            truncated_lines = lines[:threshold]
-            remaining_lines = len(lines) - threshold
-            indented_value = '\n    '.join([''] + truncated_lines)
-            formatted_pairs.append(f"  {key}:{indented_value}\n    [+{remaining_lines} more lines]")
-
-    return "{\n" + ",\n".join(formatted_pairs) + "\n}"
-
-
-class ToolMonitoringMiddleware(AgentMiddleware):
-    def __init__(self, *, persona: BasePersona):
-        self.stream_message = persona.stream_message
-        self.log = persona.log
-
-    async def awrap_tool_call(
-        self,
-        request: ToolCallRequest,
-        handler: Callable[[ToolCallRequest], ToolMessage | Command],
-    ) -> ToolMessage | Command:
-        args = format_tool_args_compact(request.tool_call['args'])
-        self.log.info(f"{request.tool_call['name']}({args})")
-
-        try:
-            result = await handler(request)
-            self.log.info(f"{request.tool_call['name']} Done!")
-            return result
-        except Exception as e:
-            self.log.info(f"{request.tool_call['name']} failed: {e}")
-            return ToolMessage(
-                tool_call_id=request.tool_call["id"], status="error", content=f"{e}"
-            )
+JUPYTERNAUT_AVATAR_PATH = str(
+    os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "../static", "jupyternaut.svg")
+    )
+)
 
 
 class JupyternautPersona(BasePersona):
@@ -115,6 +43,10 @@ def defaults(self):
             system_prompt="...",
         )
 
+    @property
+    def yroom_manager(self):
+        return self.parent.serverapp.web_app.settings["yroom_manager"]
+
     async def get_memory_store(self):
         if not hasattr(self, "_memory_store"):
             conn = await aiosqlite.connect(MEMORY_STORE_PATH, check_same_thread=False)
@@ -130,7 +62,7 @@ def get_tools(self):
     async def get_agent(self, model_id: str, model_args, system_prompt: str):
         model = ChatLiteLLM(**model_args, model=model_id, streaming=True)
         memory_store = await self.get_memory_store()
-  
+
         return create_agent(
             model,
             system_prompt=system_prompt,
@@ -158,18 +90,20 @@ async def process_message(self, message: Message) -> None:
             model_id=model_id, model_args=model_args, system_prompt=system_prompt
         )
 
+        context = {
+            "thread_id": self.ychat.get_id(),
+            "username": message.sender
+        }
+
         async def create_aiter():
             async for token, metadata in agent.astream(
                 {"messages": [{"role": "user", "content": message.body}]},
-                {"configurable": {"thread_id": self.ychat.get_id()}},
+                {"configurable": context},
                 stream_mode="messages",
             ):
                 node = metadata["langgraph_node"]
                 content_blocks = token.content_blocks
-                if (
-                    node == "model"
-                    and content_blocks
-                ):
+                if node == "model" and content_blocks:
                     if token.text:
                         yield token.text
 
@@ -182,15 +116,19 @@ def get_system_prompt(
         """
         Returns the system prompt, including attachments as a string.
         """
+
+        context = self.process_attachments(message) or ""
+        context = f"User's username is '{message.sender}'\n\n" + context
+
         system_msg_args = JupyternautSystemPromptArgs(
             model_id=model_id,
             persona_name=self.name,
-            context=self.process_attachments(message),
+            context=context,
         ).model_dump()
 
         return JUPYTERNAUT_SYSTEM_PROMPT_TEMPLATE.render(**system_msg_args)
 
     def shutdown(self):
-        if hasattr(self,"_memory_store"):
+        if hasattr(self, "_memory_store"):
             self.parent.event_loop.create_task(self._memory_store.conn.close())
         super().shutdown()