ajarbot/agent.py

"""AI Agent with Memory and LLM Integration."""

import threading
from typing import List, Optional

from hooks import HooksSystem
from llm_interface import LLMInterface
from memory_system import MemorySystem
from self_healing import SelfHealingSystem
from tools import TOOL_DEFINITIONS, execute_tool

# Maximum number of recent messages to include in LLM context
MAX_CONTEXT_MESSAGES = 20  # Optimized for Agent SDK flat-rate subscription
# Maximum characters of agent response to store in memory
MEMORY_RESPONSE_PREVIEW_LENGTH = 500  # Store more context for better memory retrieval
# Maximum conversation history entries before pruning
MAX_CONVERSATION_HISTORY = 100  # Higher limit with flat-rate subscription
# Maximum tool execution iterations (generous limit for complex operations like zettelkasten)
MAX_TOOL_ITERATIONS = 30  # Allows complex multi-step workflows with auto-linking, hybrid search, etc.


class Agent:
    """AI Agent with memory, LLM, and hooks."""

    def __init__(
        self,
        provider: str = "claude",
        workspace_dir: str = "./memory_workspace",
        is_sub_agent: bool = False,
        specialist_prompt: Optional[str] = None,
    ) -> None:
        self.memory = MemorySystem(workspace_dir)
        self.llm = LLMInterface(provider)
        self.hooks = HooksSystem()
        self.conversation_history: List[dict] = []
        self._chat_lock = threading.Lock()
        self.healing_system = SelfHealingSystem(self.memory, self)

        # Sub-agent orchestration
        self.is_sub_agent = is_sub_agent
        self.specialist_prompt = specialist_prompt
        self.sub_agents: dict = {}  # Cache for spawned sub-agents

        self.memory.sync()
        if not is_sub_agent:  # Only trigger hooks for main agent
            self.hooks.trigger("agent", "startup", {"workspace_dir": workspace_dir})

    def spawn_sub_agent(
        self,
        specialist_prompt: str,
        agent_id: Optional[str] = None,
        share_memory: bool = True,
    ) -> 'Agent':
        """Spawn a sub-agent with specialized system prompt.

        Args:
            specialist_prompt: Custom system prompt for the specialist
            agent_id: Optional ID for caching (reuse same specialist)
            share_memory: Whether to share memory workspace with main agent

        Returns:
            Agent instance configured as a specialist

        Example:
            sub = agent.spawn_sub_agent(
                specialist_prompt="You are a zettelkasten expert. Focus ONLY on note-taking.",
                agent_id="zettelkasten_processor"
            )
            result = sub.chat("Process my fleeting notes", username="jordan")
        """
        # Check cache if agent_id provided
        if agent_id and agent_id in self.sub_agents:
            return self.sub_agents[agent_id]

        # Create new sub-agent
        workspace = self.memory.workspace_dir if share_memory else f"{self.memory.workspace_dir}/sub_agents/{agent_id}"
        sub_agent = Agent(
            provider=self.llm.provider,
            workspace_dir=workspace,
            is_sub_agent=True,
            specialist_prompt=specialist_prompt,
        )

        # Cache if ID provided
        if agent_id:
            self.sub_agents[agent_id] = sub_agent

        return sub_agent

    def delegate(
        self,
        task: str,
        specialist_prompt: str,
        username: str = "default",
        agent_id: Optional[str] = None,
    ) -> str:
        """Delegate a task to a specialist sub-agent (convenience method).

        Args:
            task: The task/message to send to the specialist
            specialist_prompt: System prompt defining the specialist's role
            username: Username for context
            agent_id: Optional ID for caching the specialist

        Returns:
            Response from the specialist

        Example:
            # One-off delegation
            result = agent.delegate(
                task="Process my fleeting notes and find connections",
                specialist_prompt="You are a zettelkasten expert. Focus on note organization and linking.",
                username="jordan"
            )

            # Cached specialist (reused across multiple calls)
            result = agent.delegate(
                task="Summarize my emails from today",
                specialist_prompt="You are an email analyst. Focus on extracting key information.",
                username="jordan",
                agent_id="email_analyst"
            )
        """
        sub_agent = self.spawn_sub_agent(specialist_prompt, agent_id=agent_id)
        return sub_agent.chat(task, username=username)

    def _get_context_messages(self, max_messages: int) -> List[dict]:
        """Get recent messages without breaking tool_use/tool_result pairs.

        Ensures that:
        1. A tool_result message always has its preceding tool_use message
        2. A tool_use message always has its following tool_result message
        3. The first message is never a tool_result without its tool_use
        """
        if len(self.conversation_history) <= max_messages:
            return list(self.conversation_history)

        # Start with the most recent messages
        start_idx = len(self.conversation_history) - max_messages
        # Track original start_idx before adjustments for end-of-list check
        original_start_idx = start_idx

        # Check if we split a tool pair at the start
        if start_idx > 0:
            candidate = self.conversation_history[start_idx]
            # If first message is a tool_result, include the tool_use before it
            if candidate["role"] == "user" and isinstance(candidate.get("content"), list):
                if any(isinstance(block, dict) and block.get("type") == "tool_result"
                       for block in candidate["content"]):
                    start_idx -= 1

        # Build result slice using adjusted start
        result = list(self.conversation_history[start_idx:])

        # Check if we split a tool pair at the end
        # Use original_start_idx + max_messages to find end of original slice
        original_end_idx = original_start_idx + max_messages
        if original_end_idx < len(self.conversation_history):
            end_msg = self.conversation_history[original_end_idx - 1]
            if end_msg["role"] == "assistant" and isinstance(end_msg.get("content"), list):
                has_tool_use = any(
                    (hasattr(block, "type") and block.type == "tool_use") or
                    (isinstance(block, dict) and block.get("type") == "tool_use")
                    for block in end_msg["content"]
                )
                if has_tool_use:
                    # The tool_result at original_end_idx is already in result
                    # if start_idx was adjusted, so only add if it's not there
                    next_msg = self.conversation_history[original_end_idx]
                    if next_msg not in result:
                        result.append(next_msg)

        return result

    def _prune_conversation_history(self) -> None:
        """Prune conversation history to prevent unbounded growth.

        Removes oldest messages while preserving tool_use/tool_result pairs.
        """
        if len(self.conversation_history) <= MAX_CONVERSATION_HISTORY:
            return

        # Keep the most recent half
        keep_count = MAX_CONVERSATION_HISTORY // 2
        start_idx = len(self.conversation_history) - keep_count

        # Ensure we don't split a tool pair
        if start_idx > 0:
            candidate = self.conversation_history[start_idx]
            if candidate["role"] == "user" and isinstance(candidate.get("content"), list):
                if any(isinstance(block, dict) and block.get("type") == "tool_result"
                       for block in candidate["content"]):
                    start_idx -= 1

        self.conversation_history = self.conversation_history[start_idx:]

    def chat(self, user_message: str, username: str = "default") -> str:
        """Chat with context from memory and tool use.

        Thread-safe: uses a lock to prevent concurrent modification of
        conversation history from multiple threads (e.g., scheduled tasks
        and live messages).
        """
        # Handle model switching commands (no lock needed, read-only on history)
        if user_message.lower().startswith("/model "):
            model_name = user_message[7:].strip()
            self.llm.set_model(model_name)
            return f"Switched to model: {model_name}"
        elif user_message.lower() == "/sonnet":
            self.llm.set_model("claude-sonnet-4-5-20250929")
            return "Switched to Claude Sonnet 4.5 (more capable, higher cost)"
        elif user_message.lower() == "/haiku":
            self.llm.set_model("claude-haiku-4-5-20251001")
            return "Switched to Claude Haiku 4.5 (faster, cheaper)"
        elif user_message.lower() == "/status":
            current_model = self.llm.model
            is_sonnet = "sonnet" in current_model.lower()
            cache_status = "enabled" if is_sonnet else "disabled (Haiku active)"
            return (
                f"Current model: {current_model}\n"
                f"Prompt caching: {cache_status}\n"
                f"Context messages: {MAX_CONTEXT_MESSAGES}\n"
                f"Memory results: 2\n\n"
                f"Commands: /sonnet, /haiku, /status"
            )

        with self._chat_lock:
            return self._chat_inner(user_message, username)

    def _chat_inner(self, user_message: str, username: str) -> str:
        """Inner chat logic, called while holding _chat_lock."""
        # Use specialist prompt if this is a sub-agent, otherwise use full context
        if self.specialist_prompt:
            # Sub-agent: Use focused specialist prompt
            system = (
                f"{self.specialist_prompt}\n\n"
                f"You have access to {len(TOOL_DEFINITIONS)} tools. Use them to accomplish your specialized task. "
                f"Stay focused on your specialty and complete the task efficiently."
            )
        else:
            # Main agent: Use full SOUL, user profile, and memory context
            soul = self.memory.get_soul()
            user_profile = self.memory.get_user(username)
            relevant_memory = self.memory.search_hybrid(user_message, max_results=5)

            memory_lines = [f"- {mem['snippet']}" for mem in relevant_memory]
            system = (
                f"{soul}\n\nUser Profile:\n{user_profile}\n\n"
                f"Relevant Memory:\n" + "\n".join(memory_lines) +
                f"\n\nYou have access to {len(TOOL_DEFINITIONS)} tools for file operations, "
                f"command execution, and Google services. Use them freely to help the user. "
                f"Note: You're running on a flat-rate Agent SDK subscription, so don't worry "
                f"about API costs when making multiple tool calls or processing large contexts."
            )

        self.conversation_history.append(
            {"role": "user", "content": user_message}
        )

        # Prune history to prevent unbounded growth
        self._prune_conversation_history()

        # Tool execution loop
        max_iterations = MAX_TOOL_ITERATIONS
        # Enable caching for Sonnet to save 90% on repeated system prompts
        use_caching = "sonnet" in self.llm.model.lower()

        for iteration in range(max_iterations):
            # Get recent messages, ensuring we don't break tool_use/tool_result pairs
            context_messages = self._get_context_messages(MAX_CONTEXT_MESSAGES)

            try:
                response = self.llm.chat_with_tools(
                    context_messages,
                    tools=TOOL_DEFINITIONS,
                    system=system,
                    use_cache=use_caching,
                )
            except Exception as e:
                error_msg = f"LLM API error: {e}"
                print(f"[Agent] {error_msg}")
                self.healing_system.capture_error(
                    error=e,
                    component="agent.py:_chat_inner",
                    intent="Calling LLM API for chat response",
                    context={
                        "model": self.llm.model,
                        "message_preview": user_message[:100],
                        "iteration": iteration,
                    },
                )
                return f"Sorry, I encountered an error communicating with the AI model. Please try again."

            # Check stop reason
            if response.stop_reason == "end_turn":
                # Extract text response
                text_content = []
                for block in response.content:
                    if block.type == "text":
                        text_content.append(block.text)

                final_response = "\n".join(text_content)

                # Handle empty response
                if not final_response.strip():
                    final_response = "(No response generated)"

                self.conversation_history.append(
                    {"role": "assistant", "content": final_response}
                )

                preview = final_response[:MEMORY_RESPONSE_PREVIEW_LENGTH]
                self.memory.write_memory(
                    f"**{username}**: {user_message}\n"
                    f"**Garvis**: {preview}...",
                    daily=True,
                )

                return final_response

            elif response.stop_reason == "tool_use":
                # Build assistant message with tool uses
                assistant_content = []
                tool_uses = []

                for block in response.content:
                    if block.type == "text":
                        assistant_content.append({
                            "type": "text",
                            "text": block.text
                        })
                    elif block.type == "tool_use":
                        assistant_content.append({
                            "type": "tool_use",
                            "id": block.id,
                            "name": block.name,
                            "input": block.input
                        })
                        tool_uses.append(block)

                self.conversation_history.append({
                    "role": "assistant",
                    "content": assistant_content
                })

                # Execute tools and build tool result message
                tool_results = []
                for tool_use in tool_uses:
                    result = execute_tool(tool_use.name, tool_use.input, healing_system=self.healing_system)
                    # Truncate large tool outputs to prevent token explosion
                    if len(result) > 5000:
                        result = result[:5000] + "\n... (output truncated)"
                    print(f"[Tool] {tool_use.name}: {result[:100]}...")
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": tool_use.id,
                        "content": result
                    })

                self.conversation_history.append({
                    "role": "user",
                    "content": tool_results
                })

            else:
                # Unexpected stop reason
                return f"Unexpected stop reason: {response.stop_reason}"

        return "Error: Maximum tool use iterations exceeded"

    def switch_model(self, provider: str) -> None:
        """Switch LLM provider."""
        self.llm = LLMInterface(provider)

    def shutdown(self) -> None:
        """Cleanup and stop background services."""
        self.memory.close()
        self.hooks.trigger("agent", "shutdown", {})


if __name__ == "__main__":
    agent = Agent(provider="claude")
    response = agent.chat("What's my current project?", username="alice")
    print(response)