feat: 新增 core/agents 模块和 nanobot
- 新增 agents 模块，包含 agent、api、skills 等子模块 - 新增 nanobot 项目，支持多渠道集成 - 添加启动脚本 start-all.bat 和 start-all.sh Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
7
core/agents/providers/__init__.py
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7
core/agents/providers/__init__.py
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"""LLM Provider abstraction for X-Agents."""
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from agents.providers.base import LLMProvider, LLMResponse, ToolCallRequest
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from agents.providers.openai_provider import OpenAIProvider
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from agents.providers.anthropic_provider import AnthropicProvider
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__all__ = ["LLMProvider", "LLMResponse", "ToolCallRequest", "OpenAIProvider", "AnthropicProvider"]
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241
core/agents/providers/anthropic_provider.py
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241
core/agents/providers/anthropic_provider.py
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"""Anthropic LLM provider implementation."""
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import json
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import secrets
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import string
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from typing import Any
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import aiohttp
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from loguru import logger
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from agents.providers.base import LLMProvider, LLMResponse, ToolCallRequest
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_ALNUM = string.ascii_letters + string.digits
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def _short_tool_id() -> str:
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"""Generate a 9-char alphanumeric ID for tool calls."""
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return "".join(secrets.choice(_ALNUM) for _ in range(9))
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class AnthropicProvider(LLMProvider):
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"""Anthropic LLM provider using Claude API."""
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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default_model: str = "claude-sonnet-4-20250514",
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):
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super().__init__(api_key, api_base)
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self.default_model = default_model
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self._session: aiohttp.ClientSession | None = None
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async def _get_session(self) -> aiohttp.ClientSession:
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"""Get or create aiohttp session."""
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if self._session is None or self._session.closed:
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self._session = aiohttp.ClientSession()
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return self._session
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async def close(self):
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"""Close the HTTP session."""
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if self._session and not self._session.closed:
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await self._session.close()
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def _convert_messages_to_anthropic(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert messages to Anthropic API format."""
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converted = []
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for msg in messages:
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role = msg.get("role")
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content = msg.get("content")
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# Handle tool calls in assistant messages
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if role == "assistant" and msg.get("tool_calls"):
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# Anthropic doesn't support tool_calls in the same way, convert to text
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tool_calls_text = "\n".join([
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f"Tool call: {tc.get('name')}({json.dumps(tc.get('arguments', {}))})"
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for tc in msg["tool_calls"]
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])
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if content:
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content = f"{content}\n\n{tool_calls_text}"
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else:
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content = tool_calls_text
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# Handle tool results
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if role == "tool":
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# Convert tool result to Anthropic format
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tool_use_id = msg.get("tool_call_id", _short_tool_id())
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converted.append({
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"type": "tool_result",
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"tool_use_id": tool_use_id,
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"content": content or "(empty)",
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})
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continue
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# Skip system messages - they'll be handled separately
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if role == "system":
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continue
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# Convert content to Anthropic format
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if isinstance(content, str):
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converted.append({
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"role": role,
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"content": content,
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})
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elif isinstance(content, list):
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# Handle list content
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text_parts = []
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for item in content:
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if isinstance(item, dict):
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if item.get("type") == "text":
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text_parts.append(item.get("text", ""))
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elif item.get("type") == "tool_use":
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# This shouldn't happen in input, but handle it
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text_parts.append(f"[tool_use: {item.get('name')}]")
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elif item.get("type") == "tool_result":
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text_parts.append(item.get("content", ""))
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converted.append({
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"role": role,
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"content": "\n".join(text_parts),
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})
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else:
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converted.append({
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"role": role,
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"content": str(content) if content else "(empty)",
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})
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return converted
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def _get_system_message(self, messages: list[dict[str, Any]]) -> str | None:
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"""Extract system message from messages."""
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for msg in messages:
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if msg.get("role") == "system":
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return msg.get("content")
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return None
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async def chat(
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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 = 4096,
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temperature: float = 0.7,
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) -> LLMResponse:
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"""Send a chat completion request to Anthropic API."""
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model = model or self.default_model
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api_base = self.api_base or "https://api.anthropic.com"
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url = f"{api_base}/v1/messages"
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headers = {
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"Content-Type": "application/json",
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"anthropic-version": "2023-06-01",
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}
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if self.api_key:
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headers["x-api-key"] = self.api_key
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# Get system message and convert other messages
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system = self._get_system_message(messages)
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anthropic_messages = self._convert_messages_to_anthropic(messages)
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payload: dict[str, Any] = {
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"model": model,
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"messages": anthropic_messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if system:
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payload["system"] = system
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# Convert tools to Anthropic format if provided
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if tools:
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anthropic_tools = self._convert_tools(tools)
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payload["tools"] = anthropic_tools
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try:
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session = await self._get_session()
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async with session.post(url, json=payload, headers=headers) as resp:
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if resp.status != 200:
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error_text = await resp.text()
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try:
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error_json = json.loads(error_text)
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error_msg = error_json.get("error", {}).get("message", error_text)
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except json.JSONDecodeError:
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error_msg = error_text
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return LLMResponse(
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content=f"Anthropic API error (status {resp.status}): {error_msg}",
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finish_reason="error",
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)
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data = await resp.json()
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return self._parse_response(data, tools is not None)
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except aiohttp.ClientError as e:
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return LLMResponse(
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content=f"Anthropic API connection error: {str(e)}",
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finish_reason="error",
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)
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except Exception as e:
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return LLMResponse(
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content=f"Error calling Anthropic: {str(e)}",
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finish_reason="error",
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)
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def _convert_tools(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert OpenAI-style tools to Anthropic format."""
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anthropic_tools = []
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for tool in tools:
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func = tool.get("function", {})
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anthropic_tools.append({
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"name": func.get("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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return anthropic_tools
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def _parse_response(self, data: dict[str, Any], has_tools: bool = False) -> LLMResponse:
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"""Parse Anthropic API response into our standard format."""
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content = data.get("content", [])
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# Extract text content
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text_content = ""
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tool_calls = []
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for block in content:
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if block.get("type") == "text":
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text_content += block.get("text", "")
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elif block.get("type") == "tool_use" and has_tools:
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# Convert Anthropic tool_use to our format
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args = block.get("input", {})
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tool_calls.append(ToolCallRequest(
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id=block.get("id", _short_tool_id()),
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name=block.get("name", ""),
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arguments=args,
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))
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# Determine finish reason
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stop_reason = data.get("stop_reason", "end_turn")
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if stop_reason == "tool_use":
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finish_reason = "tool_calls"
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elif stop_reason == "max_tokens":
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finish_reason = "length"
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else:
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finish_reason = "stop"
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# Parse usage
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usage = data.get("usage", {})
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usage_dict = {
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"prompt_tokens": usage.get("input_tokens", 0),
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"completion_tokens": usage.get("output_tokens", 0),
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"total_tokens": usage.get("input_tokens", 0) + usage.get("output_tokens", 0),
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}
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return LLMResponse(
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content=text_content if text_content else None,
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tool_calls=tool_calls,
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finish_reason=finish_reason,
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usage=usage_dict,
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)
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def get_default_model(self) -> str:
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"""Get the default model."""
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return self.default_model
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225
core/agents/providers/base.py
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225
core/agents/providers/base.py
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"""Base LLM provider interface."""
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import asyncio
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import json
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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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from loguru import logger
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@dataclass
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class ToolCallRequest:
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"""A tool call request 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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provider_specific_fields: dict[str, Any] | None = None
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def to_openai_tool_call(self) -> dict[str, Any]:
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"""Serialize to an OpenAI-style tool_call payload."""
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tool_call = {
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"id": self.id,
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"type": "function",
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"function": {
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"name": self.name,
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"arguments": json.dumps(self.arguments, ensure_ascii=False),
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},
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}
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if self.provider_specific_fields:
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tool_call["provider_specific_fields"] = self.provider_specific_fields
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return tool_call
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@dataclass
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class LLMResponse:
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"""Response from an LLM provider."""
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content: str | None
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tool_calls: list[ToolCallRequest] = field(default_factory=list)
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finish_reason: str = "stop"
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usage: dict[str, int] = field(default_factory=dict)
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reasoning_content: str | None = None # For reasoning models
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@property
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def has_tool_calls(self) -> bool:
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"""Check if response contains tool calls."""
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return len(self.tool_calls) > 0
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@dataclass(frozen=True)
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class GenerationSettings:
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"""Default generation parameters for LLM calls."""
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temperature: float = 0.7
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max_tokens: int = 4096
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class LLMProvider(ABC):
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"""
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Abstract base class for LLM providers.
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Implementations should handle the specifics of each provider's API
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while maintaining a consistent interface.
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"""
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_CHAT_RETRY_DELAYS = (1, 2, 4)
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_TRANSIENT_ERROR_MARKERS = (
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"429",
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"rate limit",
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"500",
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"502",
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"503",
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"504",
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"overloaded",
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"timeout",
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"timed out",
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"connection",
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"server error",
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"temporarily unavailable",
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)
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_SENTINEL = object()
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def __init__(self, api_key: str | None = None, api_base: str | None = None):
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self.api_key = api_key
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self.api_base = api_base
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self.generation: GenerationSettings = GenerationSettings()
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@staticmethod
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def _sanitize_empty_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Replace empty text content that causes provider 400 errors."""
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result: list[dict[str, Any]] = []
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for msg in messages:
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content = msg.get("content")
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if isinstance(content, str) and not content:
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clean = dict(msg)
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clean["content"] = None if (msg.get("role") == "assistant" and msg.get("tool_calls")) else "(empty)"
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result.append(clean)
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continue
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if isinstance(content, list):
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filtered = [
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item for item in content
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if not (
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isinstance(item, dict)
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and item.get("type") in ("text", "input_text", "output_text")
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and not item.get("text")
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)
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]
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if len(filtered) != len(content):
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clean = dict(msg)
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if filtered:
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clean["content"] = filtered
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elif msg.get("role") == "assistant" and msg.get("tool_calls"):
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clean["content"] = None
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else:
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clean["content"] = "(empty)"
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result.append(clean)
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continue
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if isinstance(content, dict):
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clean = dict(msg)
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clean["content"] = [content]
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result.append(clean)
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continue
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result.append(msg)
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return result
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@abstractmethod
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async def chat(
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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 = 4096,
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temperature: float = 0.7,
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) -> LLMResponse:
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"""
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Send a chat completion request.
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Args:
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messages: List of message dicts with 'role' and 'content'.
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tools: Optional list of tool definitions.
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model: Model identifier (provider-specific).
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max_tokens: Maximum tokens in response.
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temperature: Sampling temperature.
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Returns:
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LLMResponse with content and/or tool calls.
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"""
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pass
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@classmethod
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def _is_transient_error(cls, content: str | None) -> bool:
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err = (content or "").lower()
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return any(marker in err for marker in cls._TRANSIENT_ERROR_MARKERS)
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async def chat_with_retry(
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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: object = _SENTINEL,
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temperature: object = _SENTINEL,
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) -> LLMResponse:
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"""Call chat() with retry on transient provider failures."""
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if max_tokens is self._SENTINEL:
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max_tokens = self.generation.max_tokens
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if temperature is self._SENTINEL:
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temperature = self.generation.temperature
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for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
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try:
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response = await self.chat(
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messages=messages,
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tools=tools,
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model=model,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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except asyncio.CancelledError:
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raise
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except Exception as exc:
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response = LLMResponse(
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content=f"Error calling LLM: {exc}",
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finish_reason="error",
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)
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if response.finish_reason != "error":
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return response
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if not self._is_transient_error(response.content):
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return response
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err = (response.content or "").lower()
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logger.warning(
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"LLM transient error (attempt {}/{}), retrying in {}s: {}",
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attempt,
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len(self._CHAT_RETRY_DELAYS),
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delay,
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err[:120],
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)
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await asyncio.sleep(delay)
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try:
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return await self.chat(
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messages=messages,
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tools=tools,
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||||
model=model,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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except asyncio.CancelledError:
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raise
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except Exception as exc:
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return LLMResponse(
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content=f"Error calling LLM: {exc}",
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finish_reason="error",
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)
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@abstractmethod
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def get_default_model(self) -> str:
|
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"""Get the default model for this provider."""
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pass
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150
core/agents/providers/openai_provider.py
Normal file
150
core/agents/providers/openai_provider.py
Normal file
@@ -0,0 +1,150 @@
|
||||
"""OpenAI LLM provider implementation."""
|
||||
|
||||
import json
|
||||
import secrets
|
||||
import string
|
||||
from typing import Any
|
||||
|
||||
import aiohttp
|
||||
from loguru import logger
|
||||
|
||||
from agents.providers.base import LLMProvider, LLMResponse, ToolCallRequest
|
||||
|
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_ALNUM = string.ascii_letters + string.digits
|
||||
|
||||
|
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def _short_tool_id() -> str:
|
||||
"""Generate a 9-char alphanumeric ID for tool calls."""
|
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return "".join(secrets.choice(_ALNUM) for _ in range(9))
|
||||
|
||||
|
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class OpenAIProvider(LLMProvider):
|
||||
"""OpenAI LLM provider using OpenAI API."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str | None = None,
|
||||
api_base: str | None = None,
|
||||
default_model: str = "gpt-4o",
|
||||
):
|
||||
super().__init__(api_key, api_base)
|
||||
self.default_model = default_model
|
||||
self._session: aiohttp.ClientSession | None = None
|
||||
|
||||
async def _get_session(self) -> aiohttp.ClientSession:
|
||||
"""Get or create aiohttp session."""
|
||||
if self._session is None or self._session.closed:
|
||||
self._session = aiohttp.ClientSession()
|
||||
return self._session
|
||||
|
||||
async def close(self):
|
||||
"""Close the HTTP session."""
|
||||
if self._session and not self._session.closed:
|
||||
await self._session.close()
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: list[dict[str, Any]],
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
model: str | None = None,
|
||||
max_tokens: int = 4096,
|
||||
temperature: float = 0.7,
|
||||
) -> LLMResponse:
|
||||
"""Send a chat completion request to OpenAI API."""
|
||||
model = model or self.default_model
|
||||
api_base = self.api_base or "https://api.openai.com/v1"
|
||||
url = f"{api_base}/chat/completions"
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
if self.api_key:
|
||||
headers["Authorization"] = f"Bearer {self.api_key}"
|
||||
|
||||
# Sanitize messages
|
||||
messages = self._sanitize_empty_content(messages)
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"max_tokens": max_tokens,
|
||||
"temperature": temperature,
|
||||
}
|
||||
|
||||
if tools:
|
||||
payload["tools"] = tools
|
||||
payload["tool_choice"] = "auto"
|
||||
|
||||
try:
|
||||
session = await self._get_session()
|
||||
async with session.post(url, json=payload, headers=headers) as resp:
|
||||
if resp.status != 200:
|
||||
error_text = await resp.text()
|
||||
return LLMResponse(
|
||||
content=f"OpenAI API error (status {resp.status}): {error_text}",
|
||||
finish_reason="error",
|
||||
)
|
||||
|
||||
data = await resp.json()
|
||||
return self._parse_response(data)
|
||||
|
||||
except aiohttp.ClientError as e:
|
||||
return LLMResponse(
|
||||
content=f"OpenAI API connection error: {str(e)}",
|
||||
finish_reason="error",
|
||||
)
|
||||
except Exception as e:
|
||||
return LLMResponse(
|
||||
content=f"Error calling OpenAI: {str(e)}",
|
||||
finish_reason="error",
|
||||
)
|
||||
|
||||
def _parse_response(self, data: dict[str, Any]) -> LLMResponse:
|
||||
"""Parse OpenAI API response into our standard format."""
|
||||
choices = data.get("choices", [])
|
||||
if not choices:
|
||||
return LLMResponse(content="", finish_reason="stop")
|
||||
|
||||
choice = choices[0]
|
||||
message = choice.get("message", {})
|
||||
content = message.get("content")
|
||||
finish_reason = choice.get("finish_reason", "stop")
|
||||
|
||||
# Parse tool calls
|
||||
tool_calls = []
|
||||
raw_tool_calls = message.get("tool_calls", [])
|
||||
for tc in raw_tool_calls:
|
||||
func = tc.get("function", {})
|
||||
args_str = func.get("arguments", "{}")
|
||||
if isinstance(args_str, str):
|
||||
try:
|
||||
args = json.loads(args_str)
|
||||
except json.JSONDecodeError:
|
||||
args = {}
|
||||
else:
|
||||
args = args_str
|
||||
|
||||
tool_calls.append(ToolCallRequest(
|
||||
id=tc.get("id", _short_tool_id()),
|
||||
name=func.get("name", ""),
|
||||
arguments=args,
|
||||
))
|
||||
|
||||
# Parse usage
|
||||
usage = data.get("usage", {})
|
||||
usage_dict = {
|
||||
"prompt_tokens": usage.get("prompt_tokens", 0),
|
||||
"completion_tokens": usage.get("completion_tokens", 0),
|
||||
"total_tokens": usage.get("total_tokens", 0),
|
||||
}
|
||||
|
||||
return LLMResponse(
|
||||
content=content,
|
||||
tool_calls=tool_calls,
|
||||
finish_reason=finish_reason,
|
||||
usage=usage_dict,
|
||||
)
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
"""Get the default model."""
|
||||
return self.default_model
|
||||
Reference in New Issue
Block a user