378d599125
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>
108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
"""Single agent engine — one LLM loop per agent."""
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from __future__ import annotations
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from typing import Any
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from loguru import logger
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from xtrm_agent.config import AgentFileConfig
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from xtrm_agent.llm.provider import LLMProvider, LLMResponse
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from xtrm_agent.tools.approval import ApprovalEngine
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from xtrm_agent.tools.registry import ToolRegistry
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class Engine:
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"""Runs one agent's LLM loop: messages → LLM → tool calls → loop → response."""
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def __init__(
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self,
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agent_config: AgentFileConfig,
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provider: LLMProvider,
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tools: ToolRegistry,
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approval: ApprovalEngine,
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) -> None:
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self.config = agent_config
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self.provider = provider
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self.tools = tools
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self.approval = approval
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async def run(self, user_message: str) -> str:
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"""Process a single user message through the agent loop."""
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messages = self._build_initial_messages(user_message)
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return await self._agent_loop(messages)
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async def run_delegation(self, task: str) -> str:
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"""Process a delegation task (no system prompt changes)."""
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messages = self._build_initial_messages(task)
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return await self._agent_loop(messages)
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def _build_initial_messages(self, user_message: str) -> list[dict[str, Any]]:
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messages: list[dict[str, Any]] = []
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if self.config.instructions:
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messages.append({"role": "system", "content": self.config.instructions})
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messages.append({"role": "user", "content": user_message})
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return messages
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async def _agent_loop(self, messages: list[dict[str, Any]]) -> str:
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"""Core agent iteration loop."""
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for iteration in range(self.config.max_iterations):
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model = self.config.model or self.provider.get_default_model()
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tool_defs = self.tools.get_definitions() if self.tools.names() else None
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response = await self.provider.complete(
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messages=messages,
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tools=tool_defs,
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model=model,
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max_tokens=8192,
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temperature=self.config.temperature,
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)
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if not response.has_tool_calls:
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return response.content or "(no response)"
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# Add assistant message with tool calls
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messages.append(self._assistant_message(response))
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# Execute each tool call
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for tc in response.tool_calls:
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result = await self._execute_tool(tc.name, tc.arguments)
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messages.append(
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{
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"role": "tool",
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"tool_call_id": tc.id,
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"name": tc.name,
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"content": result,
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}
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)
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logger.debug(
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f"[{self.config.name}] Iteration {iteration + 1}: "
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f"{len(response.tool_calls)} tool call(s)"
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)
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return "(max iterations reached)"
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async def _execute_tool(self, name: str, arguments: dict[str, Any]) -> str:
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"""Execute a tool with approval check."""
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approved = await self.approval.check(name, arguments)
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if not approved:
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return f"Tool '{name}' was denied by approval policy."
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return await self.tools.execute(name, arguments)
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def _assistant_message(self, response: LLMResponse) -> dict[str, Any]:
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"""Build assistant message dict from LLMResponse."""
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msg: dict[str, Any] = {"role": "assistant"}
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if response.content:
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msg["content"] = response.content
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if response.tool_calls:
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msg["tool_calls"] = [
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{
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"id": tc.id,
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"type": "function",
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"function": {"name": tc.name, "arguments": tc.arguments},
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}
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for tc in response.tool_calls
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]
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return msg
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