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/worker_individual/base_individual.py

@@ -0,0 +1,76 @@
+#  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, RunContext
+from pydantic import Field
+from pretor.adapter.model_adapter.agent_factory import AgentFactory
+from pretor.core.global_state_machine.model_provider.base_provider import Provider
+from pretor.utils.agent_model import ResponseModel, InputModel, DepsModel
+from pretor.utils.ray_hook import ray_actor_hook
+
+from pretor.utils.logger import get_logger
+logger = get_logger('worker_individual')
+
+class WorkerIndividualResponse(ResponseModel):
+    output: str = Field(..., description="Worker执行任务的输出结果")
+
+class WorkerIndividualDeps(DepsModel):
+    task_event: dict
+
+class WorkerIndividualInput(InputModel):
+    task_event: dict
+
+class BaseIndividual:
+    """
+    Worker Individual 的基类
+    """
+
+    def __init__(self, agent_config: dict):
+        self.agent_config = agent_config
+        self.agent_id = agent_config.get("agent_id")
+        self.agent: Agent | None = None
+
+    async def _init_agent(self, agent_name: str, system_prompt: str):
+        from pretor.utils.get_tool import load_tools_from_list
+        global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
+        provider_title = self.agent_config.get("provider_title", "openai")  # default fallback
+        model_id = self.agent_config.get("model_id", "gpt-4o")  # default fallback
+        tools_list = self.agent_config.get("tools", None)
+
+        provider: Provider = await global_state_machine.get_provider.remote( provider_title)
+        agent_factory = AgentFactory()
+
+        callables = load_tools_from_list(tools_list)
+
+        self.agent = agent_factory.create_agent(
+            provider=provider,
+            model_id=model_id,
+            output_type=WorkerIndividualResponse,
+            system_prompt=system_prompt,
+            deps_type=WorkerIndividualDeps,
+            agent_name=agent_name,
+            tools=callables
+        )
+
+        @self.agent.system_prompt
+        async def dynamic_prompt(ctx: RunContext[WorkerIndividualDeps]):
+            prompt = system_prompt + "\n\n"
+            prompt += (
+                f"=== 当前任务上下文 ===\n"
+                f"{ctx.deps.task_event}\n"
+            )
+            return prompt
+
+    async def run(self, task_event: dict) -> dict:
+        raise NotImplementedError("子类必须实现 run 方法")