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>
This commit is contained in:
Kaloyan Danchev
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"""Anthropic/Claude LLM provider."""
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from __future__ import annotations
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from typing import Any
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import anthropic
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from xtrm_agent.llm.provider import LLMProvider, LLMResponse, ToolCallRequest
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def _openai_tools_to_anthropic(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert OpenAI function-calling tool schema to Anthropic format."""
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result = []
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for tool in tools:
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func = tool.get("function", tool)
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result.append(
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{
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"name": func["name"],
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"description": func.get("description", ""),
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"input_schema": func.get("parameters", {"type": "object", "properties": {}}),
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}
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)
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return result
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class AnthropicProvider(LLMProvider):
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"""Claude via the Anthropic SDK."""
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def __init__(self, model: str = "claude-sonnet-4-5-20250929") -> None:
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self.client = anthropic.AsyncAnthropic()
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self.model = model
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async def complete(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 8192,
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temperature: float = 0.3,
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) -> LLMResponse:
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model = model or self.model
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# Extract system message
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system_text = ""
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api_messages = []
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for msg in messages:
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if msg["role"] == "system":
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system_text = msg["content"] if isinstance(msg["content"], str) else str(msg["content"])
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else:
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api_messages.append(self._convert_message(msg))
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kwargs: dict[str, Any] = {
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"model": model,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"messages": api_messages,
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}
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if system_text:
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kwargs["system"] = system_text
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if tools:
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kwargs["tools"] = _openai_tools_to_anthropic(tools)
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response = await self.client.messages.create(**kwargs)
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return self._parse_response(response)
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def get_default_model(self) -> str:
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return self.model
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def _convert_message(self, msg: dict[str, Any]) -> dict[str, Any]:
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"""Convert a message to Anthropic format."""
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role = msg["role"]
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# Tool results → user message with tool_result blocks
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if role == "tool":
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return {
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": msg["tool_call_id"],
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"content": msg.get("content", ""),
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}
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],
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}
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# Assistant messages with tool_calls → content blocks
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if role == "assistant" and "tool_calls" in msg:
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blocks: list[dict[str, Any]] = []
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if msg.get("content"):
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blocks.append({"type": "text", "text": msg["content"]})
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for tc in msg["tool_calls"]:
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func = tc.get("function", tc)
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blocks.append(
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{
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"type": "tool_use",
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"id": tc.get("id", func.get("id", "")),
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"name": func["name"],
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"input": func.get("arguments", {}),
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}
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)
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return {"role": "assistant", "content": blocks}
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return {"role": role, "content": msg.get("content", "")}
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def _parse_response(self, response: anthropic.types.Message) -> LLMResponse:
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"""Parse Anthropic response into standardized LLMResponse."""
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text_parts: list[str] = []
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tool_calls: list[ToolCallRequest] = []
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for block in response.content:
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if block.type == "text":
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text_parts.append(block.text)
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elif block.type == "tool_use":
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tool_calls.append(
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ToolCallRequest(
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id=block.id,
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name=block.name,
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arguments=block.input if isinstance(block.input, dict) else {},
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)
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)
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return LLMResponse(
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content="\n".join(text_parts),
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tool_calls=tool_calls,
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finish_reason=response.stop_reason or "",
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usage={
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"input_tokens": response.usage.input_tokens,
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"output_tokens": response.usage.output_tokens,
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},
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)
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"""LiteLLM provider — DeepSeek, Kimi, MiniMax, and more."""
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from __future__ import annotations
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import json
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from typing import Any
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import litellm
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from json_repair import repair_json
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from xtrm_agent.llm.provider import LLMProvider, LLMResponse, ToolCallRequest
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class LiteLLMProvider(LLMProvider):
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"""Multi-provider via LiteLLM."""
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def __init__(self, model: str = "deepseek/deepseek-chat-v3.1") -> None:
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self.model = model
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litellm.drop_params = True
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async def complete(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 8192,
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temperature: float = 0.3,
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) -> LLMResponse:
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model = model or self.model
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kwargs: dict[str, Any] = {
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"model": model,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = "auto"
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response = await litellm.acompletion(**kwargs)
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return self._parse_response(response)
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def get_default_model(self) -> str:
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return self.model
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def _parse_response(self, response: Any) -> LLMResponse:
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"""Parse LiteLLM (OpenAI-format) response."""
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choice = response.choices[0]
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message = choice.message
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content = message.content or ""
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tool_calls: list[ToolCallRequest] = []
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if message.tool_calls:
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for tc in message.tool_calls:
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args = self._parse_arguments(tc.function.arguments)
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tool_calls.append(
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ToolCallRequest(
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id=tc.id,
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name=tc.function.name,
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arguments=args,
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)
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)
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usage_data = {}
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if hasattr(response, "usage") and response.usage:
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usage_data = {
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"input_tokens": getattr(response.usage, "prompt_tokens", 0),
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"output_tokens": getattr(response.usage, "completion_tokens", 0),
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}
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return LLMResponse(
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content=content,
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tool_calls=tool_calls,
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finish_reason=choice.finish_reason or "",
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usage=usage_data,
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)
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def _parse_arguments(self, raw: str | dict) -> dict[str, Any]:
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"""Parse tool call arguments, using json-repair for malformed JSON."""
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if isinstance(raw, dict):
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return raw
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try:
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return json.loads(raw)
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except (json.JSONDecodeError, TypeError):
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try:
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repaired = repair_json(raw)
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result = json.loads(repaired)
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return result if isinstance(result, dict) else {}
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except Exception:
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return {}
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"""LLM provider abstract base class."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any
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@dataclass
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class ToolCallRequest:
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"""A single tool call from the LLM."""
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id: str
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name: str
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arguments: dict[str, Any]
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@dataclass
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class LLMResponse:
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"""Standardized response from any LLM provider."""
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content: str = ""
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tool_calls: list[ToolCallRequest] = field(default_factory=list)
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finish_reason: str = ""
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usage: dict[str, int] = field(default_factory=dict)
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@property
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def has_tool_calls(self) -> bool:
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return len(self.tool_calls) > 0
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class LLMProvider(ABC):
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"""Abstract base for LLM providers."""
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@abstractmethod
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async def complete(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 8192,
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temperature: float = 0.3,
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) -> LLMResponse:
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"""Send messages to the LLM and get a response."""
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@abstractmethod
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def get_default_model(self) -> str:
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"""Return the default model string for this provider."""
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