chore: initial commit for Pretor v0.1.0-alpha

正式发布 Pretor 平台的首个 alpha 版本。本项目旨在构建一个基于分布式架构的多智能体协同工作流水线。

核心功能实现：
1. 建立基于 BaseIndividual 的动态插件加载机制。
2. 实现三类核心 worker_individual 子个体。
3. 集成 Ray 框架支持分布式集群调度。
4. 基于 PostgreSQL 的全量持久化存储方案。
5. 提供完整的 FastAPI 后端与 React 前端交互界面。

pretor/adapter/model_adapter/agent_factory.py

@@ -0,0 +1,77 @@
+#  Copyright 2026 zhaoxi826
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from pydantic_ai import Agent
+from pydantic_ai.models.openai import OpenAIChatModel
+from pydantic_ai.models.anthropic import AnthropicModel
+from pydantic_ai.providers.openai import OpenAIProvider
+from pydantic_ai.providers.anthropic import AnthropicProvider
+from pretor.adapter.model_adapter.deepseek_reasoner import DeepSeekReasonerAgent
+from pretor.core.global_state_machine.model_provider import Provider
+from pretor.utils.agent_model import ResponseModel, DepsModel
+from pretor.utils.error import ModelNotExistError
+
+class AgentFactory:
+    def __init__(self):
+        self._models_mapping = {"openai": (OpenAIChatModel, OpenAIProvider),
+                                "claude": (AnthropicModel, AnthropicProvider),
+                                "deepseek": (OpenAIChatModel, OpenAIProvider),}
+
+    def create_agent(self,
+                     provider: Provider,
+                     model_id: str,
+                     output_type: ResponseModel,
+                     system_prompt: str,
+                     deps_type: DepsModel,
+                     agent_name: str,
+                     tools: list = None) -> Agent:
+        """
+            create_agent方法，将输入的provider对象实例化为一个pydantic-ai的agent对象
+
+        Args:
+            provider: Provider对象，从global_state_machine中获取
+            model_id: 模型名
+            output_type: 输出格式
+            system_prompt: 系统提示词
+            deps_type: 依赖类型，在agent运行时动态输入的格式化消息
+            agent_name: agent的名字
+            tools: 工具列表
+
+        Returns:
+            返回被实例化的pydantic-ai的Agent对象
+        """
+        if model_id not in provider.provider_models:
+            raise ModelNotExistError("模型不存在")
+        if provider.provider_type not in self._models_mapping:
+            raise ValueError(f"不支持的协议类型: {provider.provider_type}")
+        model_class, provider_class = self._models_mapping[provider.provider_type]
+        model = model_class(model_id, provider=provider_class(api_key=provider.provider_apikey, base_url=provider.provider_url))
+        match provider.provider_type:
+            case "deepseek":
+                agent = DeepSeekReasonerAgent(model=model,
+                                              name=agent_name,
+                                              output_type=output_type,
+                                              deps_type=deps_type,
+                                              system_prompt=system_prompt,
+                                              tools=tools,
+                                              retries=3,
+                                              )
+            case _:
+                agent = Agent(model=model,
+                              name=agent_name,
+                              system_prompt=system_prompt,
+                              output_type=output_type,
+                              deps_type=deps_type,
+                              tools=tools)
+        return agent