Initial implementation of xtrm-agent multi-agent system

Multi-agent AI automation system with shared message bus, specialized
roles (coder/researcher/reviewer), and deny-by-default security.

- Config system with Pydantic validation and YAML loading
- Async message bus with inter-agent delegation
- LLM providers: Anthropic (Claude) and LiteLLM (DeepSeek/Kimi/MiniMax)
- Tool system: registry, builtins (file/bash/web), approval engine, MCP client
- Agent engine with tool-calling loop and orchestrator for multi-agent management
- CLI channel (REPL) and Discord channel
- Docker + Dockge deployment config
- Typer CLI: chat, serve, status, agents commands

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

xtrm_agent/llm/provider.py

@@ -0,0 +1,49 @@
+"""LLM provider abstract base class."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class ToolCallRequest:
+    """A single tool call from the LLM."""
+
+    id: str
+    name: str
+    arguments: dict[str, Any]
+
+
+@dataclass
+class LLMResponse:
+    """Standardized response from any LLM provider."""
+
+    content: str = ""
+    tool_calls: list[ToolCallRequest] = field(default_factory=list)
+    finish_reason: str = ""
+    usage: dict[str, int] = field(default_factory=dict)
+
+    @property
+    def has_tool_calls(self) -> bool:
+        return len(self.tool_calls) > 0
+
+
+class LLMProvider(ABC):
+    """Abstract base for LLM providers."""
+
+    @abstractmethod
+    async def complete(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 8192,
+        temperature: float = 0.3,
+    ) -> LLMResponse:
+        """Send messages to the LLM and get a response."""
+
+    @abstractmethod
+    def get_default_model(self) -> str:
+        """Return the default model string for this provider."""