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feat(agent): complete EvoAgent integration for all 6 agent roles

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Migrate all agent roles from Legacy to EvoAgent architecture:
- fundamentals_analyst, technical_analyst, sentiment_analyst, valuation_analyst
- risk_manager, portfolio_manager

Key changes:
- EvoAgent now supports Portfolio Manager compatibility methods (_make_decision,
  get_decisions, get_portfolio_state, load_portfolio_state, update_portfolio)
- Add UnifiedAgentFactory for centralized agent creation
- ToolGuard with batch approval API and WebSocket broadcast
- Legacy agents marked deprecated (AnalystAgent, RiskAgent, PMAgent)
- Remove backend/agents/compat.py migration shim
- Add run_id alongside workspace_id for semantic clarity
- Complete integration test coverage (13 tests)
- All smoke tests passing for 6 agent roles

Constraint: Must maintain backward compatibility with existing run configs
Constraint: Memory support must work with EvoAgent (no fallback to Legacy)
Rejected: Separate PM implementation for EvoAgent | unified approach cleaner
Confidence: high
Scope-risk: broad
Directive: EVO_AGENT_IDS env var still respected but defaults to all roles
Not-tested: Kubernetes sandbox mode for skill execution
This commit is contained in:
cillin
2026-04-02 00:55:08 +08:00
parent 0fa413380c
commit 16b54d5ccc
73 changed files with 9454 additions and 904 deletions
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18
.env.example
View File

@@ -26,6 +26,10 @@ EXPLAIN_RANGE_USE_LLM=
# Memory module
MEMORY_API_KEY=
# Experimental EvoAgent rollout for selected analysts only.
# Example: EVO_AGENT_IDS=fundamentals_analyst,risk_manager,portfolio_manager
EVO_AGENT_IDS=
# ================== Agent-Specific Model Configuration | Agent特定模型配置 ==================
AGENT_SENTIMENT_ANALYST_MODEL_NAME=deepseek-v3.2-exp
AGENT_TECHNICAL_ANALYST_MODEL_NAME=glm-4.6
@@ -35,6 +39,20 @@ AGENT_RISK_MANAGER_MODEL_NAME=qwen3-max-preview
AGENT_PORTFOLIO_MANAGER_MODEL_NAME=qwen3-max-preview
# ================== Advanced Configuration | 高阶配置 ==================
# Skill Sandbox Mode | 技能沙盒执行模式
# none = direct execution (default, development only) | 直接执行（默认，仅开发环境）
# docker = Docker container isolation | Docker 容器隔离
# kubernetes = Kubernetes Pod isolation (reserved) | Kubernetes Pod 隔离（预留）
SKILL_SANDBOX_MODE=none
# Docker Sandbox Settings (only used when SKILL_SANDBOX_MODE=docker) | Docker 沙盒配置
SKILL_SANDBOX_IMAGE=python:3.11-slim
SKILL_SANDBOX_MEMORY_LIMIT=512m
SKILL_SANDBOX_CPU_LIMIT=1.0
SKILL_SANDBOX_NETWORK=none
SKILL_SANDBOX_TIMEOUT=60
MAX_COMM_CYCLES=2
MARGIN_REQUIREMENT=0.5
DATA_START_DATE=2022-01-01

101
README.md
View File

@@ -39,22 +39,41 @@ The frontend exposes the trading room, runtime controls, logs, approvals, agent
## Current Architecture
The repository is currently in a transition from a modular monolith to split service surfaces. The split-service path is the default local development mode.
The repository uses a **split-service runtime model** for local development and is the default supported path.
Current app surfaces:
### Runtime vs Design-Time
- `backend.apps.agent_service` on `:8000`: control plane for workspaces, agents, skills, and guard/approval APIs
- `backend.apps.trading_service` on `:8001`: read-only trading data APIs
- `backend.apps.news_service` on `:8002`: read-only explain/news APIs
- `backend.apps.runtime_service` on `:8003`: runtime lifecycle APIs
- `backend.apps.openclaw_service` on `:8004`: read-only OpenClaw facade
- WebSocket gateway on `:8765`: live event/feed channel for the frontend
- **runtime** — the active execution layer (scheduler, gateway, pipeline, approvals during a live run)
- **run** — one concrete execution instance (`runs/<run_id>/`)
- **design-time** — configuration and control-plane concepts before a specific runtime is launched
- **workspace** — the design-time registry exposed by `agent_service` (`workspaces/`)
The most important runtime path today is:
### Service Surfaces
`frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage`
| Service | Port | Responsibility |
|---------|------|----------------|
| `backend.apps.agent_service` | `:8000` | Control plane for workspaces, agents, skills, and guard/approval APIs |
| `backend.apps.trading_service` | `:8001` | Read-only trading data APIs |
| `backend.apps.news_service` | `:8002` | Read-only explain/news APIs |
| `backend.apps.runtime_service` | `:8003` | Runtime lifecycle APIs |
| `backend.apps.openclaw_service` | `:8004` | Read-only OpenClaw facade |
| WebSocket gateway | `:8765` | Live event/feed channel for the frontend |
Reference notes for the migration live in [services/README.md](./services/README.md).
### Active Runtime Path
```
frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage
```
Runtime state is stored in `runs/<run_id>/` — this is the **runtime source of truth**. The `workspaces/` directory is the **design-time registry**, not the runtime execution path.
### Documentation
- [docs/current-architecture.md](./docs/current-architecture.md) — canonical architecture facts
- [services/README.md](./services/README.md) — service boundaries and migration details
- [docs/current-architecture.excalidraw](./docs/current-architecture.excalidraw) — visual diagram
- [docs/development-roadmap.md](./docs/development-roadmap.md) — next-step execution plan
- [docs/terminology.md](./docs/terminology.md) — consistent terminology guide
---
@@ -114,6 +133,9 @@ MODEL_NAME=qwen3-max-preview
# memory (optional unless --enable-memory is used)
MEMORY_API_KEY=
# experimental: switch selected analyst / risk roles to EvoAgent
EVO_AGENT_IDS=
```
Notes:
@@ -121,6 +143,52 @@ Notes:
- `FINNHUB_API_KEY` is required for live mode.
- `POLYGON_API_KEY` enables long-lived market-store ingestion and refresh helpers.
- `MEMORY_API_KEY` is only required when long-term memory is enabled.
- `EVO_AGENT_IDS` currently supports analyst roles plus `risk_manager` and `portfolio_manager`, and is intended for staged rollout.
### Skill Sandbox Security | 技能沙盒安全
Skill scripts can be executed in multiple sandbox modes controlled by `SKILL_SANDBOX_MODE`:
| Mode | Description | Use Case |
|------|-------------|----------|
| `none` | Direct execution, no isolation | Development only (default) |
| `docker` | Docker container isolation | Production with Docker |
| `kubernetes` | Kubernetes Pod isolation | Enterprise (reserved) |
Default configuration (development):
```bash
SKILL_SANDBOX_MODE=none
```
For production with Docker isolation:
```bash
SKILL_SANDBOX_MODE=docker
SKILL_SANDBOX_MEMORY_LIMIT=512m
SKILL_SANDBOX_CPU_LIMIT=1.0
SKILL_SANDBOX_NETWORK=none
```
When running in `none` mode, a runtime security warning is displayed on first skill execution as a reminder that scripts execute directly without isolation.
Smoke test for a specific staged EvoAgent rollout target:
```bash
python3 scripts/smoke_evo_runtime.py --agent-id fundamentals_analyst
```
This script starts a temporary runtime, verifies the gateway log contains the
selected `EvoAgent`, checks `runtime_state.json`, validates the approval wake-up
path, and then stops the runtime.
You can also include it in the local release check:
```bash
./scripts/check-prod-env.sh --smoke-evo
```
Without `EVO_AGENT_IDS`, this release check now runs
`fundamentals_analyst`, `risk_manager`, and `portfolio_manager`
smoke paths by default.
For a production-style local start flow, you can also use:
@@ -128,6 +196,9 @@ For a production-style local start flow, you can also use:
./start.sh
```
The checked-in `production` label in the deploy scripts is only an example run
label. It should not be treated as a canonical root-level runtime directory.
### 3. Start the stack
Recommended local development flow:
@@ -159,6 +230,7 @@ python -m uvicorn backend.apps.agent_service:app --host 0.0.0.0 --port 8000 --re
python -m uvicorn backend.apps.trading_service:app --host 0.0.0.0 --port 8001 --reload
python -m uvicorn backend.apps.news_service:app --host 0.0.0.0 --port 8002 --reload
python -m uvicorn backend.apps.runtime_service:app --host 0.0.0.0 --port 8003 --reload
# compatibility gateway path, not the recommended primary dev entrypoint
python -m backend.main --mode live --host 0.0.0.0 --port 8765
```
@@ -208,6 +280,11 @@ unzip ret_data.zip -d backend/data
- `runs/<run_id>/BOOTSTRAP.md` stores run-specific bootstrap values and prompt body
- `runs/<run_id>/state/runtime_state.json` stores runtime snapshot state
- `runs/<run_id>/team_dashboard/*.json` is a compatibility/export layer for dashboard consumers, not the primary runtime source of truth
- `ENABLE_DASHBOARD_COMPAT_EXPORTS=false` can disable those compatibility JSON exports in controlled environments while keeping runtime state persistence intact
Legacy root-level directories such as `live/`, `production/`, and `backtest/`
should be treated as historical compatibility artifacts, not the default runtime
location for new work.
Optional retention control:
@@ -304,7 +381,7 @@ trigger_time: "09:30"
enable_memory: false
```
Initialize a run workspace with:
Initialize run-scoped assets with:
```bash
evotraders init-workspace --config-name my_run

76
README_zh.md
View File

@@ -37,22 +37,41 @@
## 当前架构
仓库目前处于“模块化单体 -> 拆分服务”的迁移阶段，本地开发默认走 split-service 路径。
仓库目前使用 **split-service 运行时模型** 进行本地开发，这是默认支持的运行路径。
当前 app surface：
### 运行时 vs 设计时
- `backend.apps.agent_service`，端口 `8000`：控制面，负责 workspaces、agents、skills、审批接口
- `backend.apps.trading_service`，端口 `8001`：只读交易数据接口
- `backend.apps.news_service`，端口 `8002`：只读 explain/news 接口
- `backend.apps.runtime_service`，端口 `8003`：运行时生命周期接口
- `backend.apps.openclaw_service`，端口 `8004`：只读 OpenClaw facade
- WebSocket gateway，端口 `8765`：前端实时事件和 feed 通道
- **runtime** — 活跃的执行层（scheduler、gateway、pipeline、实盘运行期间的审批）
- **run** — 一次具体的执行实例（`runs/<run_id>/`）
- **design-time** — 启动特定 runtime 之前的配置和控制面概念
- **workspace** — `agent_service` 暴露的设计时注册表（`workspaces/`）
当前最关键的主链路是：
### 服务表面
`frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage`
| 服务 | 端口 | 职责 |
|------|------|------|
| `backend.apps.agent_service` | `:8000` | workspaces、agents、skills 和 guard/approval API 的控制面 |
| `backend.apps.trading_service` | `:8001` | 只读交易数据 API |
| `backend.apps.news_service` | `:8002` | 只读 explain/news API |
| `backend.apps.runtime_service` | `:8003` | 运行时生命周期 API |
| `backend.apps.openclaw_service` | `:8004` | 只读 OpenClaw facade |
| WebSocket gateway | `:8765` | 前端实时事件/feed 通道 |
迁移背景可参考 [services/README.md](./services/README.md)。
### 活跃运行时路径
```
frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage
```
运行时状态存储在 `runs/<run_id>/` — 这是 **运行时唯一真相源**。`workspaces/` 目录是 **设计时注册表**，不是运行时执行路径。
### 文档
- [docs/current-architecture.md](./docs/current-architecture.md) — 权威架构事实
- [services/README.md](./services/README.md) — 服务边界和迁移详情
- [docs/current-architecture.excalidraw](./docs/current-architecture.excalidraw) — 架构图
- [docs/development-roadmap.md](./docs/development-roadmap.md) — 下一步执行计划
- [docs/terminology.md](./docs/terminology.md) — 术语规范指南
---
@@ -112,6 +131,9 @@ MODEL_NAME=qwen3-max-preview
# 长期记忆（只有启用 --enable-memory 才需要）
MEMORY_API_KEY=
# 实验性：将选定的 analyst / risk 角色切换到 EvoAgent
EVO_AGENT_IDS=
```
说明：
@@ -119,6 +141,23 @@ MEMORY_API_KEY=
- live 模式必须配置 `FINNHUB_API_KEY`
- `POLYGON_API_KEY` 用于长期 market store 的补数和刷新
- `MEMORY_API_KEY` 仅在启用长期记忆时需要
- `EVO_AGENT_IDS` 目前支持 analyst 角色以及 `risk_manager` 和 `portfolio_manager`，用于分阶段灰度发布
特定 EvoAgent 灰度目标的冒烟测试：
```bash
python3 scripts/smoke_evo_runtime.py --agent-id fundamentals_analyst
```
该脚本启动临时运行时，验证 gateway 日志包含选定的 `EvoAgent`，检查 `runtime_state.json`，验证审批唤醒路径，然后停止运行时。
你也可以将其包含在本地发布检查中：
```bash
./scripts/check-prod-env.sh --smoke-evo
```
未设置 `EVO_AGENT_IDS` 时，此发布检查默认运行 `fundamentals_analyst`、`risk_manager` 和 `portfolio_manager` 的冒烟路径。
如果要用更接近生产的本地启动方式，也可以直接执行：
@@ -157,9 +196,13 @@ python -m uvicorn backend.apps.agent_service:app --host 0.0.0.0 --port 8000 --re
python -m uvicorn backend.apps.trading_service:app --host 0.0.0.0 --port 8001 --reload
python -m uvicorn backend.apps.news_service:app --host 0.0.0.0 --port 8002 --reload
python -m uvicorn backend.apps.runtime_service:app --host 0.0.0.0 --port 8003 --reload
# 兼容性 gateway 路径，不是推荐的主要开发入口
python -m backend.main --mode live --host 0.0.0.0 --port 8765
```
仓库里部署脚本使用的 `production` 只是一个示例 run label，不应再把它理解成
系统规定的根目录运行目录名。
### 4. 使用 CLI 运行回测或实盘
回测：
@@ -205,7 +248,10 @@ unzip ret_data.zip -d backend/data
- 每次 run 的状态写入 `runs/<run_id>/`
- `runs/<run_id>/BOOTSTRAP.md` 保存该 run 的 bootstrap 值和 prompt body
- `runs/<run_id>/state/runtime_state.json` 保存运行时快照
- `runs/<run_id>/team_dashboard/*.json` 主要是给 dashboard 用的兼容导出层，不是唯一真相源
- `runs/<run_id>/team_dashboard/*.json` 主要是给 dashboard 用的兼容导出层，不是运行时唯一真相源
- 在受控环境里可通过 `ENABLE_DASHBOARD_COMPAT_EXPORTS=false` 关闭这层兼容 JSON 导出，而不影响 runtime state 持久化
遗留的根级目录如 `live/`、`production/` 和 `backtest/` 应被视为历史兼容性产物，不是新工作的默认运行时位置。
可选保留策略：
@@ -231,7 +277,7 @@ VITE_TRADING_SERVICE_URL=http://localhost:8001
VITE_WS_URL=ws://localhost:8765
```
如果不配置，前端会按本地默认值和兼容回退逻辑运行。
如果未设置这些变量，前端会回退到本地默认值和兼容性路径。
---
@@ -302,7 +348,7 @@ trigger_time: "09:30"
enable_memory: false
```
初始化一个 run 工作区：
初始化一个 run 运行资产目录：
```bash
evotraders init-workspace --config-name my_run
@@ -324,7 +370,7 @@ evotraders/
│ └── cli.py # Typer CLI 入口
├── frontend/ # React + Vite 前端
├── shared/ # 拆分服务共用 client 和 schema
├── runs/ # run 级状态和 dashboard 导出
├── runs/ # run-scoped 状态和 dashboards
├── data/ # 长期研究数据
└── services/README.md
```

16
backend/agents/__init__.py
View File

@@ -1,14 +1,14 @@
# -*- coding: utf-8 -*-
"""
Agents package - EvoAgent architecture for trading system.
Agents package for the current mixed runtime.
Exports:
- EvoAgent: Next-generation agent with workspace support
- ToolGuardMixin: Tool call approval/denial flow
- CommandHandler: System command handling
- AgentFactory: Dynamic agent creation and management
- WorkspaceManager: Legacy name for the persistent workspace registry
- WorkspaceRegistry: Explicit run-time-agnostic workspace registry
- AgentFactory: Design-time agent creation under `workspaces/`
- WorkspaceManager: Legacy alias for the persistent `workspaces/` registry
- WorkspaceRegistry: Explicit design-time `workspaces/` registry
- RunWorkspaceManager: Run-scoped workspace asset manager
- AgentRegistry: Central agent registry
- Legacy compatibility: AnalystAgent, PMAgent, RiskAgent
@@ -26,9 +26,6 @@ from .analyst import AnalystAgent
from .portfolio_manager import PMAgent
from .risk_manager import RiskAgent
# Compatibility layer
from .compat import LegacyAgentAdapter, adapt_agent, adapt_agents, is_legacy_agent
__all__ = [
# New architecture
"EvoAgent",
@@ -48,9 +45,4 @@ __all__ = [
"AnalystAgent",
"PMAgent",
"RiskAgent",
# Compatibility layer
"LegacyAgentAdapter",
"adapt_agent",
"adapt_agents",
"is_legacy_agent",
]

30
backend/agents/analyst.py
View File

@@ -2,7 +2,13 @@
"""
Analyst Agent - Based on AgentScope ReActAgent
Performs analysis using tools and LLM
.. deprecated:: 0.2.0
AnalystAgent is deprecated and will be removed in a future version.
Use :class:`backend.agents.base.evo_agent.EvoAgent` instead.
See docs/CRITICAL_FIXES.md for migration guide.
"""
import warnings
from typing import Any, Dict, Optional
from agentscope.agent import ReActAgent
@@ -13,11 +19,23 @@ from ..config.constants import ANALYST_TYPES
from ..utils.progress import progress
from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
# Emit deprecation warning on module import
warnings.warn(
"AnalystAgent is deprecated. Use EvoAgent instead. "
"See docs/CRITICAL_FIXES.md for migration guide.",
DeprecationWarning,
stacklevel=2,
)
class AnalystAgent(ReActAgent):
"""
Analyst Agent - Uses LLM for tool selection and analysis
Inherits from AgentScope's ReActAgent
.. deprecated:: 0.2.0
Use :class:`backend.agents.base.evo_agent.EvoAgent` with
workspace-driven configuration instead.
"""
def __init__(
@@ -33,6 +51,10 @@ class AnalystAgent(ReActAgent):
"""
Initialize Analyst Agent
.. deprecated:: 0.2.0
Use :class:`backend.agents.unified_factory.UnifiedAgentFactory`
or :class:`backend.agents.base.evo_agent.EvoAgent` instead.
Args:
analyst_type: Type of analyst (e.g., "fundamentals", etc.)
toolkit: AgentScope Toolkit instance
@@ -42,6 +64,14 @@ class AnalystAgent(ReActAgent):
config: Configuration dictionary
long_term_memory: Optional ReMeTaskLongTermMemory instance
"""
# Emit runtime deprecation warning
warnings.warn(
f"AnalystAgent('{analyst_type}') is deprecated. "
"Use EvoAgent via UnifiedAgentFactory instead.",
DeprecationWarning,
stacklevel=2,
)
if analyst_type not in ANALYST_TYPES:
raise ValueError(
f"Unknown analyst type: {analyst_type}. "

135
backend/agents/base/evo_agent.py
View File

@@ -90,6 +90,8 @@ class EvoAgent(ToolGuardMixin, ReActAgent):
sys_prompt: Optional[str] = None,
max_iters: int = 10,
memory: Optional[Any] = None,
long_term_memory: Optional[Any] = None,
long_term_memory_mode: str = "static_control",
enable_tool_guard: bool = True,
enable_bootstrap_hook: bool = True,
enable_memory_compaction: bool = False,
@@ -97,6 +99,9 @@ class EvoAgent(ToolGuardMixin, ReActAgent):
memory_compact_threshold: Optional[int] = None,
env_context: Optional[str] = None,
prompt_files: Optional[List[str]] = None,
# Portfolio manager specific parameters
initial_cash: Optional[float] = None,
margin_requirement: Optional[float] = None,
):
"""Initialize EvoAgent.
@@ -144,16 +149,24 @@ class EvoAgent(ToolGuardMixin, ReActAgent):
# Initialize hook manager
self._hook_manager = HookManager()
# Build kwargs for parent ReActAgent
kwargs = {
"name": agent_id,
"model": model,
"sys_prompt": self._sys_prompt,
"toolkit": toolkit,
"memory": memory or InMemoryMemory(),
"formatter": formatter,
"max_iters": max_iters,
}
# Add long-term memory if provided
if long_term_memory:
kwargs["long_term_memory"] = long_term_memory
kwargs["long_term_memory_mode"] = long_term_memory_mode
# Initialize parent ReActAgent
super().__init__(
name=agent_id,
model=model,
sys_prompt=self._sys_prompt,
toolkit=toolkit,
memory=memory or InMemoryMemory(),
formatter=formatter,
max_iters=max_iters,
)
super().__init__(**kwargs)
# Register hooks
self._register_hooks(
@@ -366,6 +379,110 @@ class EvoAgent(ToolGuardMixin, ReActAgent):
self.toolkit = new_toolkit
logger.info("Skills reloaded for agent: %s", self.agent_id)
def _make_decision(
self,
ticker: str,
action: str,
quantity: int,
confidence: int = 50,
reasoning: str = "",
) -> "ToolResponse":
"""Record a trading decision for a ticker (PM agent compatibility).
Args:
ticker: Stock ticker symbol (e.g., "AAPL")
action: Decision - "long", "short" or "hold"
quantity: Number of shares to trade (0 for hold)
confidence: Confidence level 0-100
reasoning: Explanation for this decision
Returns:
ToolResponse confirming decision recorded
"""
from agentscope.message import TextBlock
from agentscope.tool import ToolResponse
if action not in ["long", "short", "hold"]:
return ToolResponse(
content=[
TextBlock(
type="text",
text=f"Invalid action: {action}. Must be 'long', 'short', or 'hold'.",
),
],
)
# Store decision in metadata for retrieval
if not hasattr(self, "_decisions"):
self._decisions = {}
self._decisions[ticker] = {
"action": action,
"quantity": quantity if action != "hold" else 0,
"confidence": confidence,
"reasoning": reasoning,
}
return ToolResponse(
content=[
TextBlock(
type="text",
text=f"Decision recorded: {action} {quantity} shares of {ticker} "
f"(confidence: {confidence}%)",
),
],
)
def get_decisions(self) -> Dict[str, Dict]:
"""Get decisions from current cycle (PM compatibility)."""
return getattr(self, "_decisions", {}).copy()
def get_portfolio_state(self) -> Dict[str, Any]:
"""Get current portfolio state (PM compatibility)."""
return getattr(self, "_portfolio", {}).copy()
def load_portfolio_state(self, portfolio: Dict[str, Any]) -> None:
"""Load portfolio state (PM compatibility).
Args:
portfolio: Portfolio state dict with cash, positions, margin_used
"""
if not portfolio:
return
if not hasattr(self, "_portfolio"):
self._portfolio = {
"cash": 100000.0,
"positions": {},
"margin_used": 0.0,
"margin_requirement": 0.25,
}
self._portfolio = {
"cash": portfolio.get("cash", self._portfolio["cash"]),
"positions": portfolio.get("positions", {}).copy(),
"margin_used": portfolio.get("margin_used", 0.0),
"margin_requirement": portfolio.get(
"margin_requirement",
self._portfolio["margin_requirement"],
),
}
def update_portfolio(self, portfolio: Dict[str, Any]) -> None:
"""Update portfolio after external execution (PM compatibility).
Args:
portfolio: Portfolio updates to apply
"""
if not hasattr(self, "_portfolio"):
self._portfolio = {
"cash": 100000.0,
"positions": {},
"margin_used": 0.0,
"margin_requirement": 0.25,
}
self._portfolio.update(portfolio)
def rebuild_sys_prompt(self) -> None:
"""Rebuild and replace the system prompt at runtime.

35
backend/agents/base/tool_guard.py
View File

@@ -13,7 +13,7 @@ import asyncio
import json
import logging
from dataclasses import dataclass, field
from datetime import datetime
from datetime import UTC, datetime
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Set
@@ -73,11 +73,13 @@ class ApprovalRecord:
self.tool_name = tool_name
self.tool_input = tool_input
self.agent_id = agent_id
# run_id is the new preferred name; workspace_id is kept for backward compatibility
self.run_id = workspace_id
self.workspace_id = workspace_id
self.session_id = session_id
self.status = ApprovalStatus.PENDING
self.findings = findings or []
self.created_at = datetime.utcnow()
self.created_at = datetime.now(UTC)
self.resolved_at: Optional[datetime] = None
self.resolved_by: Optional[str] = None
self.metadata: Dict[str, Any] = {}
@@ -90,6 +92,7 @@ class ApprovalRecord:
"tool_name": self.tool_name,
"tool_input": self.tool_input,
"agent_id": self.agent_id,
"run_id": self.run_id,
"workspace_id": self.workspace_id,
"session_id": self.session_id,
"findings": [f.to_dict() for f in self.findings],
@@ -161,7 +164,7 @@ class ToolGuardStore:
return record
record.status = status
record.resolved_at = datetime.utcnow()
record.resolved_at = datetime.now(UTC)
record.resolved_by = resolved_by
if notify_request and record.pending_request:
if status == ApprovalStatus.APPROVED:
@@ -395,18 +398,34 @@ class ToolGuardMixin:
)
manager = get_global_runtime_manager()
if manager:
manager.register_pending_approval(
record.approval_id,
{
approval_data = {
"tool_name": record.tool_name,
"agent_id": record.agent_id,
"workspace_id": record.workspace_id,
"session_id": record.session_id,
"tool_input": record.tool_input,
},
}
if manager:
manager.register_pending_approval(
record.approval_id,
approval_data,
)
# Broadcast WebSocket event for real-time UI updates
try:
if hasattr(manager, 'broadcast_event'):
await manager.broadcast_event({
"type": "approval_requested",
"approval_id": record.approval_id,
"agent_id": record.agent_id,
"tool_name": record.tool_name,
"timestamp": record.created_at.isoformat(),
"data": approval_data,
})
except Exception as e:
logger.warning(f"Failed to broadcast approval event: {e}")
self._pending_approval = ToolApprovalRequest(
approval_id=record.approval_id,
tool_name=tool_name,

146
backend/agents/compat.py
View File

@@ -1,146 +0,0 @@
# -*- coding: utf-8 -*-
"""
Compatibility Layer - Adapters for legacy to EvoAgent migration.
Provides:
- LegacyAgentAdapter: Wraps old AnalystAgent to work with new interfaces
- Migration utilities for gradual adoption
"""
from typing import Any, Dict, Optional
from agentscope.message import Msg
from .agent_core import EvoAgent
class LegacyAgentAdapter:
"""
Adapter to make legacy AnalystAgent compatible with EvoAgent interfaces.
This allows gradual migration by wrapping existing agents.
"""
def __init__(self, legacy_agent: Any):
"""
Initialize adapter.
Args:
legacy_agent: Legacy AnalystAgent instance
"""
self._agent = legacy_agent
self.agent_id = getattr(legacy_agent, 'agent_id', getattr(legacy_agent, 'name', 'unknown'))
self.analyst_type = getattr(legacy_agent, 'analyst_type_key', None)
@property
def name(self) -> str:
"""Get agent name."""
return getattr(self._agent, 'name', self.agent_id)
@property
def toolkit(self) -> Any:
"""Get agent toolkit."""
return getattr(self._agent, 'toolkit', None)
@property
def model(self) -> Any:
"""Get agent model."""
return getattr(self._agent, 'model', None)
@property
def memory(self) -> Any:
"""Get agent memory."""
return getattr(self._agent, 'memory', None)
async def reply(self, x: Msg = None) -> Msg:
"""
Delegate to legacy agent's reply method.
Args:
x: Input message
Returns:
Response message
"""
return await self._agent.reply(x)
def reload_runtime_assets(self, active_skill_dirs: Optional[list] = None) -> None:
"""
Reload runtime assets if supported.
Args:
active_skill_dirs: Optional list of active skill directories
"""
if hasattr(self._agent, 'reload_runtime_assets'):
self._agent.reload_runtime_assets(active_skill_dirs)
def to_evo_agent(
self,
workspace_manager: Optional[Any] = None,
enable_tool_guard: bool = False,
) -> EvoAgent:
"""
Convert legacy agent to EvoAgent.
Args:
workspace_manager: Optional workspace manager
enable_tool_guard: Whether to enable tool guard
Returns:
New EvoAgent instance with same configuration
"""
return EvoAgent(
agent_id=self.agent_id,
model=self.model,
formatter=getattr(self._agent, 'formatter', None),
toolkit=self.toolkit,
workspace_manager=workspace_manager,
config=getattr(self._agent, 'config', {}),
long_term_memory=getattr(self._agent, 'long_term_memory', None),
enable_tool_guard=enable_tool_guard,
sys_prompt=getattr(self._agent, '_sys_prompt', None),
)
def __getattr__(self, name: str) -> Any:
"""Delegate unknown attributes to wrapped agent."""
return getattr(self._agent, name)
def is_legacy_agent(agent: Any) -> bool:
"""
Check if an agent is a legacy agent.
Args:
agent: Agent instance to check
Returns:
True if legacy agent
"""
return hasattr(agent, 'analyst_type_key') and not isinstance(agent, EvoAgent)
def adapt_agent(agent: Any) -> Any:
"""
Wrap agent in adapter if it's a legacy agent.
Args:
agent: Agent instance
Returns:
Adapted agent or original if already EvoAgent
"""
if is_legacy_agent(agent):
return LegacyAgentAdapter(agent)
return agent
def adapt_agents(agents: list) -> list:
"""
Wrap multiple agents in adapters.
Args:
agents: List of agent instances
Returns:
List of adapted agents
"""
return [adapt_agent(agent) for agent in agents]

34
backend/agents/portfolio_manager.py
View File

@@ -2,8 +2,13 @@
"""
Portfolio Manager Agent - Based on AgentScope ReActAgent
Responsible for decision-making (NOT trade execution)
"""
.. deprecated:: 0.2.0
PMAgent is deprecated and will be removed in a future version.
Use :class:`backend.agents.base.evo_agent.EvoAgent` instead.
See docs/CRITICAL_FIXES.md for migration guide.
"""
import warnings
from pathlib import Path
from typing import Any, Dict, Optional, Callable
@@ -17,11 +22,31 @@ from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cach
from .team_pipeline_config import update_active_analysts
from ..config.constants import ANALYST_TYPES
# Emit deprecation warning on module import
warnings.warn(
"PMAgent is deprecated. Use EvoAgent instead. "
"See docs/CRITICAL_FIXES.md for migration guide.",
DeprecationWarning,
stacklevel=2,
)
class PMAgent(ReActAgent):
"""
Portfolio Manager Agent - Makes investment decisions
Key features:
1. PM outputs decisions only (action + quantity per ticker)
2. Trade execution happens externally (in pipeline/executor)
3. Supports both backtest and live modes
.. deprecated:: 0.2.0
Use :class:`backend.agents.base.evo_agent.EvoAgent` with
workspace-driven configuration instead.
"""
"""
Portfolio Manager Agent - Makes investment decisions
Key features:
1. PM outputs decisions only (action + quantity per ticker)
2. Trade execution happens externally (in pipeline/executor)
@@ -41,6 +66,13 @@ class PMAgent(ReActAgent):
toolkit_factory_kwargs: Optional[Dict[str, Any]] = None,
toolkit: Optional[Toolkit] = None,
):
# Emit runtime deprecation warning
warnings.warn(
"PMAgent is deprecated. Use EvoAgent via UnifiedAgentFactory instead.",
DeprecationWarning,
stacklevel=2,
)
object.__setattr__(self, "config", config or {})
# Portfolio state

29
backend/agents/risk_manager.py
View File

@@ -2,7 +2,13 @@
"""
Risk Manager Agent - Based on AgentScope ReActAgent
Uses LLM for risk assessment
.. deprecated:: 0.2.0
RiskAgent is deprecated and will be removed in a future version.
Use :class:`backend.agents.base.evo_agent.EvoAgent` instead.
See docs/CRITICAL_FIXES.md for migration guide.
"""
import warnings
from typing import Any, Dict, Optional
from agentscope.agent import ReActAgent
@@ -13,11 +19,23 @@ from agentscope.tool import Toolkit
from ..utils.progress import progress
from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
# Emit deprecation warning on module import
warnings.warn(
"RiskAgent is deprecated. Use EvoAgent instead. "
"See docs/CRITICAL_FIXES.md for migration guide.",
DeprecationWarning,
stacklevel=2,
)
class RiskAgent(ReActAgent):
"""
Risk Manager Agent - Uses LLM for risk assessment
Inherits from AgentScope's ReActAgent
.. deprecated:: 0.2.0
Use :class:`backend.agents.base.evo_agent.EvoAgent` with
workspace-driven configuration instead.
"""
def __init__(
@@ -32,6 +50,10 @@ class RiskAgent(ReActAgent):
"""
Initialize Risk Manager Agent
.. deprecated:: 0.2.0
Use :class:`backend.agents.unified_factory.UnifiedAgentFactory`
or :class:`backend.agents.base.evo_agent.EvoAgent` instead.
Args:
model: LLM model instance
formatter: Message formatter instance
@@ -39,6 +61,13 @@ class RiskAgent(ReActAgent):
config: Configuration dictionary
long_term_memory: Optional ReMeTaskLongTermMemory instance
"""
# Emit runtime deprecation warning
warnings.warn(
"RiskAgent is deprecated. Use EvoAgent via UnifiedAgentFactory instead.",
DeprecationWarning,
stacklevel=2,
)
object.__setattr__(self, "config", config or {})
object.__setattr__(self, "agent_id", name)

433
backend/agents/unified_factory.py Normal file
View File

@@ -0,0 +1,433 @@
# -*- coding: utf-8 -*-
"""Unified Agent Factory - Centralized agent creation for 大时代.
This module provides a unified factory for creating all agent types (analysts,
risk manager, portfolio manager) with consistent configuration. It replaces
the scattered agent creation logic in main.py, pipeline.py, and pipeline_runner.py.
Key features:
- Single entry point for all agent creation
- Automatic EvoAgent vs Legacy Agent selection based on _resolve_evo_agent_ids()
- Consistent parameter handling across all agent types
- Support for workspace-driven configuration
- Long-term memory integration
"""
from __future__ import annotations
import os
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, Protocol, TypeVar, Union
if TYPE_CHECKING:
from backend.agents.base.evo_agent import EvoAgent
from backend.agents.analyst import AnalystAgent
from backend.agents.risk_manager import RiskAgent
from backend.agents.portfolio_manager import PMAgent
# Type aliases for agent types
AgentType = Union["EvoAgent", "AnalystAgent", "RiskAgent", "PMAgent"]
T = TypeVar("T")
class AgentFactoryProtocol(Protocol):
"""Protocol for agent factory implementations."""
def create_analyst(
self,
analyst_type: str,
model: Any,
formatter: Any,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> AnalystAgent | EvoAgent: ...
def create_risk_manager(
self,
model: Any,
formatter: Any,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> RiskAgent | EvoAgent: ...
def create_portfolio_manager(
self,
model: Any,
formatter: Any,
initial_cash: float,
margin_requirement: float,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> PMAgent | EvoAgent: ...
class UnifiedAgentFactory:
"""Unified factory for creating agents with consistent configuration.
This factory centralizes agent creation logic and automatically selects
between EvoAgent (new) and Legacy Agent based on the EVO_AGENT_IDS
environment variable configuration.
By default, all supported roles use EvoAgent. Set EVO_AGENT_IDS=legacy
to disable EvoAgent entirely.
Example:
factory = UnifiedAgentFactory(
config_name="smoke_fullstack",
skills_manager=skills_manager,
)
# Create analyst
analyst = factory.create_analyst(
analyst_type="fundamentals_analyst",
model=model,
formatter=formatter,
)
# Create risk manager
risk_mgr = factory.create_risk_manager(
model=model,
formatter=formatter,
)
# Create portfolio manager
pm = factory.create_portfolio_manager(
model=model,
formatter=formatter,
initial_cash=100000.0,
margin_requirement=0.5,
)
"""
def __init__(
self,
config_name: str,
skills_manager: Any,
toolkit_factory: Optional[Any] = None,
evo_agent_ids: Optional[set[str]] = None,
):
"""Initialize the agent factory.
Args:
config_name: Run configuration name (e.g., "smoke_fullstack")
skills_manager: SkillsManager instance for skill/asset management
toolkit_factory: Optional factory function for creating toolkits
evo_agent_ids: Optional set of agent IDs to use EvoAgent.
If None, uses _resolve_evo_agent_ids() default.
"""
self.config_name = config_name
self.skills_manager = skills_manager
self.toolkit_factory = toolkit_factory
# Determine which agents should use EvoAgent
if evo_agent_ids is not None:
self._evo_agent_ids = evo_agent_ids
else:
self._evo_agent_ids = self._resolve_evo_agent_ids()
def _resolve_evo_agent_ids(self) -> set[str]:
"""Return agent ids selected to use EvoAgent.
By default, all supported roles use EvoAgent.
EVO_AGENT_IDS can be used to limit to specific roles.
"""
from backend.config.constants import ANALYST_TYPES
all_supported = set(ANALYST_TYPES) | {"risk_manager", "portfolio_manager"}
raw = os.getenv("EVO_AGENT_IDS", "")
if not raw.strip():
# Default: all supported roles use EvoAgent
return all_supported
if raw.strip().lower() in ("legacy", "old", "none"):
return set()
requested = {item.strip() for item in raw.split(",") if item.strip()}
return {
agent_id
for agent_id in requested
if agent_id in ANALYST_TYPES
or agent_id in {"risk_manager", "portfolio_manager"}
}
def _should_use_evo_agent(self, agent_id: str) -> bool:
"""Check if an agent should use EvoAgent."""
return agent_id in self._evo_agent_ids
def _create_toolkit(
self,
agent_type: str,
active_skill_dirs: Optional[list[Path]] = None,
owner: Optional[Any] = None,
) -> Any:
"""Create toolkit for an agent."""
if self.toolkit_factory is None:
from backend.agents.toolkit_factory import create_agent_toolkit
self.toolkit_factory = create_agent_toolkit
kwargs: dict[str, Any] = {
"active_skill_dirs": active_skill_dirs or [],
}
if owner is not None:
kwargs["owner"] = owner
return self.toolkit_factory(agent_type, self.config_name, **kwargs)
def _load_agent_config(self, agent_id: str) -> Any:
"""Load agent configuration from workspace."""
from backend.agents.agent_workspace import load_agent_workspace_config
workspace_dir = self.skills_manager.get_agent_asset_dir(
self.config_name, agent_id
)
config_path = workspace_dir / "agent.yaml"
if config_path.exists():
return load_agent_workspace_config(config_path)
# Return default config if no agent.yaml
return type(
"AgentConfig",
(),
{"prompt_files": ["SOUL.md"]},
)()
def _create_evo_agent(
self,
agent_id: str,
model: Any,
formatter: Any,
toolkit: Any,
agent_config: Any,
long_term_memory: Optional[Any] = None,
extra_kwargs: Optional[dict[str, Any]] = None,
) -> "EvoAgent":
"""Create an EvoAgent instance."""
from backend.agents.base.evo_agent import EvoAgent
workspace_dir = self.skills_manager.get_agent_asset_dir(
self.config_name, agent_id
)
kwargs: dict[str, Any] = {
"agent_id": agent_id,
"config_name": self.config_name,
"workspace_dir": workspace_dir,
"model": model,
"formatter": formatter,
"skills_manager": self.skills_manager,
"prompt_files": getattr(agent_config, "prompt_files", ["SOUL.md"]),
"long_term_memory": long_term_memory,
}
if extra_kwargs:
kwargs.update(extra_kwargs)
agent = EvoAgent(**kwargs)
agent.toolkit = toolkit
setattr(agent, "run_id", self.config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", self.config_name)
return agent
def create_analyst(
self,
analyst_type: str,
model: Any,
formatter: Any,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> "AnalystAgent | EvoAgent":
"""Create an analyst agent.
Args:
analyst_type: Type of analyst (fundamentals, technical, sentiment, valuation)
model: LLM model instance
formatter: Message formatter instance
active_skill_dirs: Optional list of active skill directories
long_term_memory: Optional long-term memory instance
Returns:
AnalystAgent or EvoAgent instance
"""
toolkit = self._create_toolkit(analyst_type, active_skill_dirs)
if self._should_use_evo_agent(analyst_type):
agent_config = self._load_agent_config(analyst_type)
return self._create_evo_agent(
agent_id=analyst_type,
model=model,
formatter=formatter,
toolkit=toolkit,
agent_config=agent_config,
long_term_memory=long_term_memory,
)
# Legacy path
from backend.agents.analyst import AnalystAgent
return AnalystAgent(
analyst_type=analyst_type,
toolkit=toolkit,
model=model,
formatter=formatter,
agent_id=analyst_type,
config={"config_name": self.config_name},
long_term_memory=long_term_memory,
)
def create_risk_manager(
self,
model: Any,
formatter: Any,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> "RiskAgent | EvoAgent":
"""Create a risk manager agent.
Args:
model: LLM model instance
formatter: Message formatter instance
active_skill_dirs: Optional list of active skill directories
long_term_memory: Optional long-term memory instance
Returns:
RiskAgent or EvoAgent instance
"""
toolkit = self._create_toolkit("risk_manager", active_skill_dirs)
if self._should_use_evo_agent("risk_manager"):
agent_config = self._load_agent_config("risk_manager")
return self._create_evo_agent(
agent_id="risk_manager",
model=model,
formatter=formatter,
toolkit=toolkit,
agent_config=agent_config,
long_term_memory=long_term_memory,
)
# Legacy path
from backend.agents.risk_manager import RiskAgent
return RiskAgent(
model=model,
formatter=formatter,
name="risk_manager",
config={"config_name": self.config_name},
long_term_memory=long_term_memory,
toolkit=toolkit,
)
def create_portfolio_manager(
self,
model: Any,
formatter: Any,
initial_cash: float,
margin_requirement: float,
active_skill_dirs: Optional[list[Path]] = None,
long_term_memory: Optional[Any] = None,
) -> "PMAgent | EvoAgent":
"""Create a portfolio manager agent.
Args:
model: LLM model instance
formatter: Message formatter instance
initial_cash: Initial cash allocation
margin_requirement: Margin requirement ratio
active_skill_dirs: Optional list of active skill directories
long_term_memory: Optional long-term memory instance
Returns:
PMAgent or EvoAgent instance
"""
if self._should_use_evo_agent("portfolio_manager"):
agent_config = self._load_agent_config("portfolio_manager")
# For PM, toolkit is created after agent (needs owner reference)
from backend.agents.base.evo_agent import EvoAgent
workspace_dir = self.skills_manager.get_agent_asset_dir(
self.config_name, "portfolio_manager"
)
agent = EvoAgent(
agent_id="portfolio_manager",
config_name=self.config_name,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=self.skills_manager,
prompt_files=getattr(agent_config, "prompt_files", ["SOUL.md"]),
initial_cash=initial_cash,
margin_requirement=margin_requirement,
long_term_memory=long_term_memory,
)
agent.toolkit = self._create_toolkit(
"portfolio_manager", active_skill_dirs, owner=agent
)
setattr(agent, "run_id", self.config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", self.config_name)
return agent
# Legacy path
from backend.agents.portfolio_manager import PMAgent
return PMAgent(
name="portfolio_manager",
model=model,
formatter=formatter,
initial_cash=initial_cash,
margin_requirement=margin_requirement,
config={"config_name": self.config_name},
long_term_memory=long_term_memory,
toolkit_factory=self.toolkit_factory,
toolkit_factory_kwargs={"active_skill_dirs": active_skill_dirs or []},
)
# Singleton factory instance cache
_factory_cache: dict[str, UnifiedAgentFactory] = {}
def get_agent_factory(
config_name: str,
skills_manager: Any,
toolkit_factory: Optional[Any] = None,
) -> UnifiedAgentFactory:
"""Get or create a cached agent factory instance.
Args:
config_name: Run configuration name
skills_manager: SkillsManager instance
toolkit_factory: Optional toolkit factory function
Returns:
UnifiedAgentFactory instance (cached per config_name)
"""
cache_key = f"{config_name}:{id(skills_manager)}"
if cache_key not in _factory_cache:
_factory_cache[cache_key] = UnifiedAgentFactory(
config_name=config_name,
skills_manager=skills_manager,
toolkit_factory=toolkit_factory,
)
return _factory_cache[cache_key]
def clear_factory_cache() -> None:
"""Clear the factory cache. Useful for testing."""
_factory_cache.clear()
__all__ = [
"UnifiedAgentFactory",
"AgentFactoryProtocol",
"get_agent_factory",
"clear_factory_cache",
]

5
backend/agents/workspace.py
View File

@@ -1,5 +1,5 @@
# -*- coding: utf-8 -*-
"""Workspace Manager - Create and manage agent workspaces."""
"""Design-time workspace registry stored under `workspaces/`."""
import logging
from dataclasses import dataclass, field
@@ -323,5 +323,6 @@ class WorkspaceRegistry:
yaml.safe_dump(config.to_dict(), f, allow_unicode=True, sort_keys=False)
# Backward-compatible alias: legacy imports expect WorkspaceManager.
# Backward-compatible alias: legacy imports expect WorkspaceManager to mean the
# design-time `workspaces/` registry.
WorkspaceManager = WorkspaceRegistry

5
backend/agents/workspace_manager.py
View File

@@ -1,5 +1,5 @@
# -*- coding: utf-8 -*-
"""Initialize run-scoped agent workspace assets."""
"""Initialize run-scoped agent workspace assets under `runs/<run_id>/`."""
from pathlib import Path
from typing import Dict, Iterable, Optional
@@ -479,5 +479,6 @@ class RunWorkspaceManager:
)
# Backward-compatible alias: code importing WorkspaceManager from this module should continue to work.
# Backward-compatible alias: many runtime paths still import WorkspaceManager
# from this module when they mean the run-scoped manager.
WorkspaceManager = RunWorkspaceManager

82
backend/api/agents.py
View File

@@ -2,7 +2,10 @@
"""
Agent API Routes
Provides REST API endpoints for agent management within workspaces.
Provides REST API endpoints for both:
- design-time agent management under `workspaces/`
- run-scoped agent asset access under `runs/<run_id>/`
"""
import logging
import os
@@ -24,6 +27,30 @@ from backend.llm.models import get_agent_model_info
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/workspaces/{workspace_id}/agents", tags=["agents"])
DESIGN_SCOPE = "design_workspace"
RUNTIME_SCOPE = "runtime_run"
RUNTIME_SCOPE_NOTE = (
"For profile, skills, and editable agent files, `workspace_id` is treated "
"as the active run id under `runs/<run_id>/`, not as the design-time "
"`workspaces/` registry."
)
def _runtime_scope_fields() -> dict[str, str]:
return {
"scope_type": RUNTIME_SCOPE,
"scope_note": RUNTIME_SCOPE_NOTE,
}
def _design_scope_fields() -> dict[str, str]:
return {
"scope_type": DESIGN_SCOPE,
"scope_note": (
"For design-time CRUD routes on this surface, `workspace_id` refers "
"to the persistent registry under `workspaces/`."
),
}
# Request/Response Models
@@ -68,30 +95,40 @@ class AgentResponse(BaseModel):
config_path: str
agent_dir: str
status: str = "inactive"
scope_type: str = DESIGN_SCOPE
scope_note: Optional[str] = None
class AgentFileResponse(BaseModel):
"""Agent file content response."""
filename: str
content: str
scope_type: str = RUNTIME_SCOPE
scope_note: Optional[str] = None
class AgentProfileResponse(BaseModel):
agent_id: str
workspace_id: str
profile: Dict[str, Any]
scope_type: str = RUNTIME_SCOPE
scope_note: Optional[str] = None
class AgentSkillsResponse(BaseModel):
agent_id: str
workspace_id: str
skills: List[Dict[str, Any]]
scope_type: str = RUNTIME_SCOPE
scope_note: Optional[str] = None
class SkillDetailResponse(BaseModel):
agent_id: str
workspace_id: str
skill: Dict[str, Any]
scope_type: str = RUNTIME_SCOPE
scope_note: Optional[str] = None
# Dependencies
@@ -101,7 +138,7 @@ def get_agent_factory():
def get_workspace_manager():
"""Get run-scoped workspace manager instance."""
"""Get run-scoped asset manager for one runtime workspace/run id."""
return RunWorkspaceManager()
@@ -119,7 +156,7 @@ async def create_agent(
registry = Depends(get_registry),
):
"""
Create a new agent in a workspace.
Create a new agent in a design-time workspace registry entry.
Args:
workspace_id: Workspace identifier
@@ -162,6 +199,7 @@ async def create_agent(
config_path=str(agent.config_path),
agent_dir=str(agent.agent_dir),
status="inactive",
**_design_scope_fields(),
)
except ValueError as e:
@@ -174,7 +212,7 @@ async def list_agents(
factory: AgentFactory = Depends(get_agent_factory),
):
"""
List all agents in a workspace.
List all agents in a design-time workspace registry entry.
Args:
workspace_id: Workspace identifier
@@ -192,6 +230,7 @@ async def list_agents(
config_path=agent["config_path"],
agent_dir=str(Path(agent["config_path"]).parent),
status="inactive",
**_design_scope_fields(),
)
for agent in agents_data
]
@@ -206,7 +245,7 @@ async def get_agent(
registry = Depends(get_registry),
):
"""
Get agent details.
Get design-time agent details from the persistent workspace registry.
Args:
workspace_id: Workspace identifier
@@ -227,6 +266,7 @@ async def get_agent(
config_path=agent_info.config_path,
agent_dir=agent_info.agent_dir,
status=agent_info.status,
**_design_scope_fields(),
)
@@ -275,6 +315,7 @@ async def get_agent_profile(
"enabled_skills": agent_config.enabled_skills,
"disabled_skills": agent_config.disabled_skills,
},
**_runtime_scope_fields(),
)
@@ -310,7 +351,12 @@ async def get_agent_skills(
"status": status,
})
return AgentSkillsResponse(agent_id=agent_id, workspace_id=workspace_id, skills=payload)
return AgentSkillsResponse(
agent_id=agent_id,
workspace_id=workspace_id,
skills=payload,
**_runtime_scope_fields(),
)
@router.get("/{agent_id}/skills/{skill_name}", response_model=SkillDetailResponse)
@@ -329,7 +375,12 @@ async def get_agent_skill_detail(
except FileNotFoundError:
raise HTTPException(status_code=404, detail=f"Unknown skill: {skill_name}")
return SkillDetailResponse(agent_id=agent_id, workspace_id=workspace_id, skill=detail)
return SkillDetailResponse(
agent_id=agent_id,
workspace_id=workspace_id,
skill=detail,
**_runtime_scope_fields(),
)
@router.delete("/{agent_id}")
@@ -416,6 +467,7 @@ async def update_agent(
config_path=agent_info.config_path,
agent_dir=agent_info.agent_dir,
status=agent_info.status,
**_design_scope_fields(),
)
@@ -656,7 +708,7 @@ async def get_agent_file(
workspace_manager: RunWorkspaceManager = Depends(get_workspace_manager),
):
"""
Read an agent's workspace file.
Read an agent file from the run-scoped asset tree under `runs/<run_id>/`.
Args:
workspace_id: Workspace identifier
@@ -672,7 +724,11 @@ async def get_agent_file(
agent_id=agent_id,
filename=filename,
)
return AgentFileResponse(filename=filename, content=content)
return AgentFileResponse(
filename=filename,
content=content,
**_runtime_scope_fields(),
)
except FileNotFoundError:
raise HTTPException(status_code=404, detail=f"File '{filename}' not found")
@@ -686,7 +742,7 @@ async def update_agent_file(
workspace_manager: RunWorkspaceManager = Depends(get_workspace_manager),
):
"""
Update an agent's workspace file.
Update an agent file in the run-scoped asset tree under `runs/<run_id>/`.
Args:
workspace_id: Workspace identifier
@@ -704,6 +760,10 @@ async def update_agent_file(
filename=filename,
content=content,
)
return AgentFileResponse(filename=filename, content=content)
return AgentFileResponse(
filename=filename,
content=content,
**_runtime_scope_fields(),
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))

103
backend/api/guard.py
View File

@@ -7,7 +7,7 @@ Provides REST API endpoints for tool guard operations.
from __future__ import annotations
from typing import Any, Dict, List, Optional
from datetime import datetime
from datetime import UTC, datetime
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
@@ -29,7 +29,7 @@ class ToolCallRequest(BaseModel):
tool_name: str = Field(..., description="Name of the tool")
tool_input: Dict[str, Any] = Field(default_factory=dict, description="Tool parameters")
agent_id: str = Field(..., description="Agent making the request")
workspace_id: str = Field(..., description="Workspace context")
workspace_id: str = Field(..., description="Run context; historical field name retained for compatibility")
session_id: Optional[str] = Field(None, description="Session identifier")
@@ -46,6 +46,21 @@ class DenyRequest(BaseModel):
reason: Optional[str] = Field(None, description="Reason for denial")
class BatchApprovalRequest(BaseModel):
"""Request to approve multiple tool calls."""
approval_ids: List[str] = Field(..., description="List of approval request IDs")
one_time: bool = Field(True, description="Whether these are one-time approvals")
class BatchApprovalResponse(BaseModel):
"""Response for batch approval operation."""
approved: List[ApprovalResponse] = Field(default_factory=list, description="Successfully approved")
failed: List[Dict[str, Any]] = Field(default_factory=list, description="Failed approvals with errors")
total_requested: int
total_approved: int
total_failed: int
class ToolFinding(BaseModel):
"""Tool guard finding."""
severity: SeverityLevel
@@ -61,11 +76,17 @@ class ApprovalResponse(BaseModel):
tool_input: Dict[str, Any]
agent_id: str
workspace_id: str
run_id: str
session_id: Optional[str] = None
findings: List[ToolFinding] = Field(default_factory=list)
created_at: str
resolved_at: Optional[str] = None
resolved_by: Optional[str] = None
scope_type: str = "runtime_run"
scope_note: str = (
"Approvals are scoped to the active runtime run. `workspace_id` is "
"retained as a compatibility field name; prefer `run_id` for display."
)
class PendingApprovalsResponse(BaseModel):
@@ -91,6 +112,7 @@ def _to_response(record: ApprovalRecord) -> ApprovalResponse:
tool_input=record.tool_input,
agent_id=record.agent_id,
workspace_id=record.workspace_id,
run_id=record.workspace_id,
session_id=record.session_id,
findings=[ToolFinding(**f.to_dict()) for f in record.findings],
created_at=record.created_at.isoformat(),
@@ -124,7 +146,7 @@ async def check_tool_call(
if request.tool_name in SAFE_TOOLS:
record.status = ApprovalStatus.APPROVED
record.resolved_at = datetime.utcnow()
record.resolved_at = datetime.now(UTC)
record.resolved_by = "system"
STORE.set_status(
record.approval_id,
@@ -156,9 +178,12 @@ async def approve_tool_call(
if record.status != ApprovalStatus.PENDING:
raise HTTPException(status_code=400, detail=f"Approval already {record.status}")
record.status = ApprovalStatus.APPROVED
record.resolved_at = datetime.utcnow()
record.resolved_by = "user"
record = STORE.set_status(
request.approval_id,
ApprovalStatus.APPROVED,
resolved_by="user",
notify_request=True,
)
return _to_response(record)
@@ -183,9 +208,12 @@ async def deny_tool_call(
if record.status != ApprovalStatus.PENDING:
raise HTTPException(status_code=400, detail=f"Approval already {record.status}")
record.status = ApprovalStatus.DENIED
record.resolved_at = datetime.utcnow()
record.resolved_by = "user"
record = STORE.set_status(
request.approval_id,
ApprovalStatus.DENIED,
resolved_by="user",
notify_request=True,
)
record.metadata["denial_reason"] = request.reason
return _to_response(record)
@@ -200,7 +228,7 @@ async def list_pending_approvals(
List pending tool approval requests.
Args:
workspace_id: Filter by workspace
workspace_id: Filter by run id (historical query parameter name retained)
agent_id: Filter by agent
Returns:
@@ -255,3 +283,58 @@ async def cancel_approval(
STORE.cancel(approval_id)
return _to_response(record)
@router.post("/approve/batch", response_model=BatchApprovalResponse)
async def batch_approve_tool_calls(
request: BatchApprovalRequest,
):
"""
Approve multiple pending tool calls in a single request.
Args:
request: Batch approval parameters with list of approval IDs
Returns:
Batch approval results with successful and failed approvals
"""
approved: List[ApprovalResponse] = []
failed: List[Dict[str, Any]] = []
for approval_id in request.approval_ids:
record = STORE.get(approval_id)
if not record:
failed.append({
"approval_id": approval_id,
"error": "Approval request not found",
})
continue
if record.status != ApprovalStatus.PENDING:
failed.append({
"approval_id": approval_id,
"error": f"Approval already {record.status}",
})
continue
try:
record = STORE.set_status(
approval_id,
ApprovalStatus.APPROVED,
resolved_by="user",
notify_request=True,
)
approved.append(_to_response(record))
except Exception as e:
failed.append({
"approval_id": approval_id,
"error": str(e),
})
return BatchApprovalResponse(
approved=approved,
failed=failed,
total_requested=len(request.approval_ids),
total_approved=len(approved),
total_failed=len(failed),
)

347
backend/api/runtime.py
View File

@@ -219,6 +219,22 @@ class GatewayStatusResponse(BaseModel):
is_running: bool
port: int
run_id: Optional[str] = None
process_status: Optional[str] = None
pid: Optional[int] = None
class GatewayHealthResponse(BaseModel):
status: str
checks: Dict[str, Any]
timestamp: str
class RuntimeModeResponse(BaseModel):
mode: str
is_backtest: bool
run_id: Optional[str] = None
schedule_mode: Optional[str] = None
is_running: bool
class RuntimeConfigResponse(BaseModel):
@@ -264,6 +280,49 @@ def _load_run_snapshot(run_id: str) -> Dict[str, Any]:
return json.loads(snapshot_path.read_text(encoding="utf-8"))
def _load_run_server_state(run_dir: Path) -> Dict[str, Any]:
"""Load persisted runtime server state if present."""
server_state_path = run_dir / "state" / "server_state.json"
if not server_state_path.exists():
return {}
try:
return json.loads(server_state_path.read_text(encoding="utf-8"))
except Exception:
return {}
def _extract_history_metrics(run_dir: Path) -> tuple[int, Optional[float]]:
"""Prefer runtime state files over dashboard exports for history summaries."""
server_state = _load_run_server_state(run_dir)
portfolio = server_state.get("portfolio") or {}
trades = server_state.get("trades")
total_trades = len(trades) if isinstance(trades, list) else 0
total_asset_value = None
if portfolio.get("total_value") is not None:
try:
total_asset_value = float(portfolio.get("total_value"))
except (TypeError, ValueError):
total_asset_value = None
if total_trades or total_asset_value is not None:
return total_trades, total_asset_value
summary_path = run_dir / "team_dashboard" / "summary.json"
if not summary_path.exists():
return 0, None
try:
summary = json.loads(summary_path.read_text(encoding="utf-8"))
total_trades = int(summary.get("totalTrades") or 0)
total_asset_value = (
float(summary.get("totalAssetValue"))
if summary.get("totalAssetValue") is not None
else None
)
return total_trades, total_asset_value
except Exception:
return 0, None
def _copy_path_if_exists(src: Path, dst: Path) -> None:
if not src.exists():
return
@@ -281,7 +340,7 @@ def _restore_run_assets(source_run_id: str, target_run_dir: Path) -> None:
raise HTTPException(status_code=404, detail=f"Source run not found: {source_run_id}")
for relative in [
"team_dashboard",
"team_dashboard/_internal_state.json",
"agents",
"skills",
"memory",
@@ -307,12 +366,10 @@ def _list_runs(limit: int = 50) -> list[RuntimeHistoryItem]:
for run_dir in run_dirs[: max(1, int(limit))]:
run_id = run_dir.name
runtime_state_path = run_dir / "state" / "runtime_state.json"
summary_path = run_dir / "team_dashboard" / "summary.json"
bootstrap: Dict[str, Any] = {}
updated_at: Optional[str] = None
total_trades = 0
total_asset_value: Optional[float] = None
total_trades, total_asset_value = _extract_history_metrics(run_dir)
if runtime_state_path.exists():
try:
@@ -323,15 +380,6 @@ def _list_runs(limit: int = 50) -> list[RuntimeHistoryItem]:
except Exception:
bootstrap = {}
if summary_path.exists():
try:
summary = json.loads(summary_path.read_text(encoding="utf-8"))
total_trades = int(summary.get("totalTrades") or 0)
total_asset_value = float(summary.get("totalAssetValue")) if summary.get("totalAssetValue") is not None else None
except Exception:
total_trades = 0
total_asset_value = None
items.append(
RuntimeHistoryItem(
run_id=run_id,
@@ -436,6 +484,14 @@ def _start_gateway_process(
port: int
) -> subprocess.Popen:
"""Start Gateway as a separate process."""
# Validate configuration before starting
validation_errors = _validate_gateway_config(bootstrap)
if validation_errors:
raise HTTPException(
status_code=400,
detail=f"Gateway configuration validation failed: {'; '.join(validation_errors)}"
)
# Prepare environment
env = os.environ.copy()
@@ -467,6 +523,168 @@ def _start_gateway_process(
return process
def _validate_gateway_config(bootstrap: Dict[str, Any]) -> List[str]:
"""Validate Gateway bootstrap configuration.
Returns a list of validation error messages. Empty list means valid.
"""
errors: List[str] = []
# Check required environment variables based on mode
mode = bootstrap.get("mode", "live")
is_backtest = mode == "backtest"
# Validate mode
if mode not in ("live", "backtest"):
errors.append(f"Invalid mode '{mode}': must be 'live' or 'backtest'")
# Check API keys based on mode
if not is_backtest:
# Live mode requires FINNHUB_API_KEY
finnhub_key = os.getenv("FINNHUB_API_KEY")
if not finnhub_key:
errors.append("FINNHUB_API_KEY environment variable is required for live mode")
# Check LLM configuration
model_name = os.getenv("MODEL_NAME")
openai_key = os.getenv("OPENAI_API_KEY")
if not model_name:
errors.append("MODEL_NAME environment variable is not set")
if not openai_key:
errors.append("OPENAI_API_KEY environment variable is not set")
# Validate tickers
tickers = bootstrap.get("tickers", [])
if not tickers:
errors.append("No tickers specified in configuration")
elif not isinstance(tickers, list):
errors.append("Tickers must be a list")
# Validate numeric values
try:
initial_cash = float(bootstrap.get("initial_cash", 0))
if initial_cash <= 0:
errors.append("initial_cash must be greater than 0")
except (TypeError, ValueError):
errors.append("initial_cash must be a valid number")
try:
margin_requirement = float(bootstrap.get("margin_requirement", 0))
if margin_requirement < 0 or margin_requirement > 1:
errors.append("margin_requirement must be between 0 and 1")
except (TypeError, ValueError):
errors.append("margin_requirement must be a valid number")
# Validate backtest dates
if is_backtest:
start_date = bootstrap.get("start_date")
end_date = bootstrap.get("end_date")
if not start_date:
errors.append("start_date is required for backtest mode")
if not end_date:
errors.append("end_date is required for backtest mode")
if start_date and end_date:
try:
from datetime import datetime
start = datetime.strptime(start_date, "%Y-%m-%d")
end = datetime.strptime(end_date, "%Y-%m-%d")
if start >= end:
errors.append("start_date must be before end_date")
except ValueError:
errors.append("Dates must be in YYYY-MM-DD format")
# Validate schedule mode
schedule_mode = bootstrap.get("schedule_mode", "daily")
if schedule_mode not in ("daily", "intraday"):
errors.append(f"Invalid schedule_mode '{schedule_mode}': must be 'daily' or 'intraday'")
return errors
def _get_gateway_process_details() -> Dict[str, Any]:
"""Get detailed information about the Gateway process."""
process = _runtime_state.gateway_process
details = {
"pid": None,
"status": "not_running",
"returncode": None,
}
if process is None:
return details
details["pid"] = process.pid
returncode = process.poll()
if returncode is None:
details["status"] = "running"
details["returncode"] = None
else:
details["status"] = "exited"
details["returncode"] = returncode
return details
def _check_gateway_health() -> Dict[str, Any]:
"""Perform comprehensive health checks on Gateway."""
checks = {
"process": {"status": "unknown", "details": {}},
"port": {"status": "unknown", "details": {}},
"configuration": {"status": "unknown", "details": {}},
}
# Check process status
process_details = _get_gateway_process_details()
checks["process"]["details"] = process_details
if process_details["status"] == "running":
checks["process"]["status"] = "healthy"
elif process_details["status"] == "exited":
checks["process"]["status"] = "unhealthy"
checks["process"]["details"]["error"] = f"Process exited with code {process_details['returncode']}"
else:
checks["process"]["status"] = "unknown"
# Check port connectivity
import socket
port = _runtime_state.gateway_port
try:
with socket.create_connection(("127.0.0.1", port), timeout=2):
checks["port"]["status"] = "healthy"
checks["port"]["details"] = {"port": port, "accessible": True}
except OSError as e:
checks["port"]["status"] = "unhealthy"
checks["port"]["details"] = {"port": port, "accessible": False, "error": str(e)}
# Check configuration
try:
if _runtime_state.runtime_manager is not None:
checks["configuration"]["status"] = "healthy"
checks["configuration"]["details"]["has_runtime_manager"] = True
else:
checks["configuration"]["status"] = "degraded"
checks["configuration"]["details"]["has_runtime_manager"] = False
except Exception as e:
checks["configuration"]["status"] = "unknown"
checks["configuration"]["details"]["error"] = str(e)
# Determine overall status
statuses = [c["status"] for c in checks.values()]
if any(s == "unhealthy" for s in statuses):
overall_status = "unhealthy"
elif all(s == "healthy" for s in statuses):
overall_status = "healthy"
else:
overall_status = "degraded"
return {
"status": overall_status,
"checks": checks,
"timestamp": datetime.now().isoformat(),
}
@router.get("/context", response_model=RunContextResponse)
async def get_run_context() -> RunContextResponse:
"""Return active runtime context, or latest persisted context when stopped."""
@@ -512,9 +730,10 @@ async def get_runtime_history(limit: int = 20) -> RuntimeHistoryResponse:
@router.get("/gateway/status", response_model=GatewayStatusResponse)
async def get_gateway_status() -> GatewayStatusResponse:
"""Get Gateway process status and port."""
"""Get Gateway process status and port with detailed process information."""
is_running = _is_gateway_running()
run_id = None
process_details = _get_gateway_process_details()
if is_running:
try:
@@ -525,7 +744,52 @@ async def get_gateway_status() -> GatewayStatusResponse:
return GatewayStatusResponse(
is_running=is_running,
port=_runtime_state.gateway_port,
run_id=run_id
run_id=run_id,
process_status=process_details["status"],
pid=process_details["pid"],
)
@router.get("/gateway/health", response_model=GatewayHealthResponse)
async def get_gateway_health() -> GatewayHealthResponse:
"""Get comprehensive Gateway health check including process, port, and configuration status."""
health = _check_gateway_health()
return GatewayHealthResponse(**health)
@router.get("/mode", response_model=RuntimeModeResponse)
async def get_runtime_mode() -> RuntimeModeResponse:
"""Get current runtime mode (live or backtest) and related configuration."""
is_running = _is_gateway_running()
if not is_running:
return RuntimeModeResponse(
mode="stopped",
is_backtest=False,
run_id=None,
schedule_mode=None,
is_running=False,
)
try:
context = _get_active_runtime_context()
bootstrap = context.get("bootstrap_values", {})
mode = bootstrap.get("mode", "live")
return RuntimeModeResponse(
mode=mode,
is_backtest=mode == "backtest",
run_id=context.get("config_name"),
schedule_mode=bootstrap.get("schedule_mode"),
is_running=True,
)
except HTTPException:
return RuntimeModeResponse(
mode="unknown",
is_backtest=False,
run_id=None,
schedule_mode=None,
is_running=False,
)
@@ -807,14 +1071,38 @@ async def start_runtime(
_runtime_state.gateway_process = None
log_path = _get_gateway_log_path_for_run(run_id)
log_tail = _read_log_tail(log_path, max_chars=4000)
# Build detailed error message
error_details = []
error_details.append(f"Gateway process exited unexpectedly")
process_details = _get_gateway_process_details()
if process_details.get("returncode") is not None:
error_details.append(f"Exit code: {process_details['returncode']}")
if log_tail:
error_details.append(f"Recent log output:\n{log_tail}")
else:
error_details.append("No log output available. Check environment configuration.")
# Check common configuration issues
config_errors = _validate_gateway_config(bootstrap)
if config_errors:
error_details.append(f"Configuration issues detected: {'; '.join(config_errors)}")
raise HTTPException(
status_code=500,
detail=f"Gateway failed to start: {log_tail or 'Unknown error'}"
detail="\n".join(error_details)
)
except HTTPException:
raise
except Exception as e:
_stop_gateway()
raise HTTPException(status_code=500, detail=f"Failed to start Gateway: {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to start Gateway: {type(e).__name__}: {str(e)}"
)
return LaunchResponse(
run_id=run_id,
@@ -861,17 +1149,38 @@ async def stop_runtime(force: bool = True) -> StopResponse:
was_running = _is_gateway_running()
if not was_running:
process_details = _get_gateway_process_details()
if process_details["status"] == "exited":
# Process exited but we have a record of it
raise HTTPException(
status_code=404,
detail=(
f"No runtime is currently running. "
f"Previous Gateway process exited with code {process_details['returncode']}. "
f"PID: {process_details['pid']}"
)
)
raise HTTPException(status_code=404, detail="No runtime is currently running")
# Get process details before stopping for the response
process_details = _get_gateway_process_details()
pid_info = f" (PID: {process_details.get('pid')})" if process_details.get('pid') else ""
# Stop Gateway process
_stop_gateway()
stop_success = _stop_gateway()
if not stop_success:
raise HTTPException(
status_code=500,
detail=f"Failed to stop Gateway process{pid_info}. Process may have already terminated."
)
# Unregister runtime manager
unregister_runtime_manager()
return StopResponse(
status="stopped",
message="Runtime stopped successfully",
message=f"Runtime stopped successfully{pid_info}",
)

11
backend/api/workspaces.py
View File

@@ -1,8 +1,9 @@
# -*- coding: utf-8 -*-
"""
Workspace API Routes
Workspace API Routes.
Provides REST API endpoints for workspace management.
These routes manage the design-time `workspaces/` registry, not the run-scoped
runtime data under `runs/<run_id>/`.
"""
from typing import Any, Dict, List, Optional
@@ -31,7 +32,7 @@ class UpdateWorkspaceRequest(BaseModel):
class WorkspaceResponse(BaseModel):
"""Workspace information response."""
"""Design-time workspace information response."""
workspace_id: str
name: str
description: str
@@ -89,10 +90,10 @@ async def list_workspaces(
manager: WorkspaceManager = Depends(get_workspace_manager),
):
"""
List all workspaces.
List all design-time workspaces.
Returns:
List of workspaces
List of design-time workspaces
"""
workspaces = manager.list_workspaces()
return WorkspaceListResponse(

24
backend/apps/agent_service.py
View File

@@ -19,13 +19,31 @@ agent_factory: AgentFactory | None = None
workspace_manager: WorkspaceManager | None = None
def _build_scope_payload(project_root: Path) -> dict[str, object]:
return {
"design_time_registry": {
"root": str(project_root / "workspaces"),
"meaning": "Persistent control-plane workspace registry",
},
"runtime_assets": {
"root": str(project_root / "runs"),
"meaning": "Run-scoped runtime state and agent assets",
},
"agent_route_note": (
"On `/api/workspaces/{workspace_id}/agents/...`, design-time CRUD "
"routes still use `workspaces/`, while profile/skills/file routes "
"use `workspace_id` as a run id under `runs/<run_id>/`."
),
}
def create_app(project_root: Path | None = None) -> FastAPI:
"""Create the agent control-plane app."""
resolved_project_root = project_root or Path(__file__).resolve().parents[2]
@asynccontextmanager
async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]:
"""Initialize workspace and registry state for the control plane."""
"""Initialize design-time workspace and registry state for the control plane."""
global agent_factory, workspace_manager
workspace_manager = WorkspaceManager(project_root=resolved_project_root)
@@ -34,7 +52,7 @@ def create_app(project_root: Path | None = None) -> FastAPI:
registry = get_registry()
print("✓ 大时代 API started")
print(f" - Workspaces root: {agent_factory.workspaces_root}")
print(f" - Design workspaces root: {agent_factory.workspaces_root}")
print(f" - Registered agents: {registry.get_agent_count()}")
yield
@@ -63,6 +81,7 @@ def create_app(project_root: Path | None = None) -> FastAPI:
if workspace_manager
else 0
),
"scope_roots": _build_scope_payload(resolved_project_root),
}
@app.get("/api/status")
@@ -72,6 +91,7 @@ def create_app(project_root: Path | None = None) -> FastAPI:
return {
"status": "operational",
"registry": registry.get_stats(),
"scope": _build_scope_payload(resolved_project_root),
}
app.include_router(workspaces_router)

18
backend/apps/openclaw_service.py
View File

@@ -1,5 +1,21 @@
# -*- coding: utf-8 -*-
"""Read-only OpenClaw CLI FastAPI surface."""
"""Read-only OpenClaw CLI FastAPI surface.
COMPATIBILITY_SURFACE: deferred
OWNER: runtime-team
SEE: docs/legacy-inventory.md#openclaw-dual-integration
This is the REST facade (port 8004) for OpenClaw integration.
For the WebSocket gateway integration, see:
- backend/services/gateway_openclaw_handlers.py
- shared/client/openclaw_websocket_client.py
Key differences:
- REST facade: typed Pydantic models, request/response, polling
- WebSocket: event-driven, real-time updates, bidirectional
Decision needed: which surface becomes the long-term contract?
"""
from __future__ import annotations

38
backend/apps/runtime_service.py
View File

@@ -6,7 +6,7 @@ from __future__ import annotations
from fastapi import FastAPI
from backend.api import runtime_router
from backend.api.runtime import get_runtime_state
from backend.api.runtime import get_runtime_state, _check_gateway_health, _get_gateway_process_details
from backend.apps.cors import add_cors_middleware
@@ -22,29 +22,57 @@ def create_app() -> FastAPI:
@app.get("/health")
async def health_check() -> dict[str, object]:
"""Health check for the runtime service."""
"""Health check for the runtime service with Gateway process status."""
runtime_state = get_runtime_state()
process = runtime_state.gateway_process
process_details = _get_gateway_process_details()
is_running = process is not None and process.poll() is None
# Determine overall health status
if is_running:
status = "healthy"
elif process is not None:
# Process existed but exited
status = "degraded"
else:
status = "healthy" # Service is healthy even without Gateway running
return {
"status": "healthy",
"status": status,
"service": "runtime-service",
"gateway_running": is_running,
"gateway_port": runtime_state.gateway_port,
"gateway": {
"running": is_running,
"port": runtime_state.gateway_port,
"pid": process_details.get("pid"),
"process_status": process_details.get("status"),
"returncode": process_details.get("returncode"),
},
}
@app.get("/health/gateway")
async def gateway_health_check() -> dict[str, object]:
"""Detailed health check for the Gateway subprocess."""
health = _check_gateway_health()
return health
@app.get("/api/status")
async def api_status() -> dict[str, object]:
"""Service-level status payload for runtime orchestration."""
runtime_state = get_runtime_state()
process = runtime_state.gateway_process
process_details = _get_gateway_process_details()
is_running = process is not None and process.poll() is None
return {
"status": "operational",
"service": "runtime-service",
"runtime": {
"gateway_running": is_running,
"gateway_port": runtime_state.gateway_port,
"gateway_pid": process_details.get("pid"),
"gateway_process_status": process_details.get("status"),
"has_runtime_manager": runtime_state.runtime_manager is not None,
},
}

328
backend/cli.py
View File

@@ -5,12 +5,36 @@
This module provides easy-to-use commands for running backtest, live trading,
and frontend development server.
ARCHITECTURE NOTE:
==================
This CLI supports TWO distinct runtime modes:
1. STANDALONE MODE (default):
- Uses `evotraders backtest` or `evotraders live` commands
- Starts a self-contained monolithic Gateway process with all agents
- Suitable for: quick testing, single-machine deployment, development
- WebSocket server runs on port 8765 (default)
- No external service dependencies
2. MICROSERVICE MODE (production):
- Uses `./start-dev.sh` or manual service orchestration
- Runs 4 separate FastAPI services (agent, runtime, trading, news)
- Gateway runs as a subprocess of runtime_service
- Suitable for: production scaling, distributed deployment
- Services communicate via REST APIs
When microservices are already running, standalone mode will warn you about
port conflicts and potential confusion. Use `--force` to override.
For more details, see: docs/current-architecture.md
"""
# flake8: noqa: E501
# pylint: disable=R0912, R0915
import logging
import os
import shutil
import socket
import subprocess
import sys
from datetime import datetime, timedelta
@@ -42,6 +66,17 @@ from backend.data.market_store import MarketStore
from backend.enrich.llm_enricher import get_explain_model_info, llm_enrichment_enabled
from backend.enrich.news_enricher import enrich_symbols
# Microservice port definitions (for conflict detection)
MICROSERVICE_PORTS = {
"agent_service": 8000,
"trading_service": 8001,
"news_service": 8002,
"runtime_service": 8003,
}
# Gateway default port
GATEWAY_PORT = 8765
app = typer.Typer(
name="evotraders",
help="大时代：自进化多智能体交易系统",
@@ -72,6 +107,101 @@ def get_project_root() -> Path:
return Path(__file__).parent.parent
def _is_port_in_use(port: int, host: str = "127.0.0.1") -> bool:
"""Check if a port is already in use."""
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
sock.settimeout(1.0)
result = sock.connect_ex((host, port))
return result == 0
except Exception:
return False
def _detect_running_microservices() -> dict[str, int]:
"""Detect which microservices are already running."""
running = {}
for service_name, port in MICROSERVICE_PORTS.items():
if _is_port_in_use(port):
running[service_name] = port
return running
def _check_gateway_port_conflict(port: int) -> bool:
"""Check if the Gateway port is already in use."""
return _is_port_in_use(port)
def _display_mode_warning(
running_services: dict[str, int],
gateway_port: int,
force: bool = False,
) -> bool:
"""
Display warning when microservices are detected.
Returns:
True if should proceed, False if should abort
"""
if not running_services and not _check_gateway_port_conflict(gateway_port):
return True
console.print()
console.print(
Panel.fit(
"[bold yellow]⚠️ MICROSERVICE MODE DETECTED[/bold yellow]\n\n"
"You are attempting to start in STANDALONE mode, but microservices "
"appear to already be running. This can cause confusion and port conflicts.",
border_style="yellow",
)
)
if running_services:
console.print("\n[bold]Detected running services:[/bold]")
for service, port in running_services.items():
console.print(f" • {service}: [cyan]http://localhost:{port}[/cyan]")
if _check_gateway_port_conflict(gateway_port):
console.print(
f"\n[bold red]Port {gateway_port} is already in use![/bold red] "
"Another Gateway instance may be running."
)
console.print("\n[bold]Options:[/bold]")
console.print(" 1. Stop microservices first: [cyan]pkill -f 'uvicorn|backend.main'[/cyan]")
console.print(" 2. Use microservice mode instead: [cyan]./start-dev.sh[/cyan]")
console.print(" 3. Use a different port: [cyan]--port <other_port>[/cyan]")
if force:
console.print(
"\n[yellow]⚠️ --force flag used. Proceeding despite conflicts...[/yellow]"
)
return True
console.print()
should_proceed = Confirm.ask(
"Do you want to proceed anyway?",
default=False,
)
return should_proceed
def _display_standalone_banner(mode: str, config_name: str) -> None:
"""Display standalone mode startup banner."""
console.print(
Panel.fit(
f"[bold cyan]大时代 {mode.upper()} Mode[/bold cyan]\n"
"[dim]Standalone Mode (Monolithic Gateway)[/dim]",
border_style="cyan",
)
)
console.print("\n[dim]Architecture:[/dim]")
console.print(" Mode: [yellow]Standalone (Single Process)[/yellow]")
console.print(f" Config: [cyan]{config_name}[/cyan]")
console.print("\n[dim]Note: This is NOT microservice mode. For distributed deployment,")
console.print(" use ./start-dev.sh instead.[/dim]\n")
def handle_history_cleanup(config_name: str, auto_clean: bool = False) -> None:
"""
Handle cleanup of historical data for a given config.
@@ -215,8 +345,8 @@ def run_data_updater(project_root: Path) -> None:
)
def initialize_workspace(config_name: str) -> Path:
"""Create run-scoped workspace files for a config."""
def initialize_run_assets(config_name: str) -> Path:
"""Create run-scoped agent assets and bootstrap files for a config."""
workspace_manager = WorkspaceManager(project_root=get_project_root())
workspace_manager.initialize_default_assets(
config_name=config_name,
@@ -438,14 +568,18 @@ def init_workspace(
"default",
"--config-name",
"-c",
help="Configuration name for the workspace",
help="Run label under runs/<config_name> for the initialized asset tree.",
),
):
"""Initialize run-scoped BOOTSTRAP and agent prompt asset files."""
run_dir = initialize_workspace(config_name)
"""Initialize run-scoped BOOTSTRAP and agent asset files.
The command name is retained for compatibility even though the target is
the run-scoped asset tree under `runs/<config_name>/`.
"""
run_dir = initialize_run_assets(config_name)
console.print(
Panel.fit(
f"[bold green]Workspace initialized[/bold green]\n[cyan]{run_dir}[/cyan]",
f"[bold green]Run assets initialized[/bold green]\n[cyan]{run_dir}[/cyan]",
border_style="green",
),
)
@@ -861,6 +995,13 @@ def team_show(
)
# =============================================================================
# STANDALONE MODE COMMANDS (backtest/live)
# =============================================================================
# These commands start a self-contained monolithic Gateway process.
# For microservice mode, use ./start-dev.sh instead.
# =============================================================================
@app.command()
def backtest(
start: Optional[str] = typer.Option(
@@ -876,10 +1017,10 @@ def backtest(
help="End date for backtest (YYYY-MM-DD)",
),
config_name: str = typer.Option(
"backtest",
"default_backtest_run",
"--config-name",
"-c",
help="Configuration name for this backtest run",
help="Run label under runs/<config_name> for this backtest runtime.",
),
host: str = typer.Option(
"0.0.0.0",
@@ -887,7 +1028,7 @@ def backtest(
help="WebSocket server host",
),
port: int = typer.Option(
8765,
GATEWAY_PORT,
"--port",
"-p",
help="WebSocket server port",
@@ -907,22 +1048,24 @@ def backtest(
"--enable-memory",
help="Enable ReMeTaskLongTermMemory for agents (requires MEMORY_API_KEY)",
),
force: bool = typer.Option(
False,
"--force",
help="Force start even if microservices are detected (may cause conflicts)",
),
):
"""
Run backtest mode with historical data.
Run backtest mode in STANDALONE mode (monolithic Gateway).
Example:
This starts a self-contained process with all agents. For microservice
mode (distributed services), use ./start-dev.sh instead.
Examples:
evotraders backtest --start 2025-11-01 --end 2025-12-01
evotraders backtest --config-name my_strategy --port 9000
evotraders backtest --clean # Clear historical data before starting
evotraders backtest --enable-memory # Enable long-term memory
"""
console.print(
Panel.fit(
"[bold cyan]大时代 Backtest Mode[/bold cyan]",
border_style="cyan",
),
)
poll_interval = int(_normalize_typer_value(poll_interval, 10))
# Validate dates - required for backtest
@@ -948,13 +1091,18 @@ def backtest(
)
raise typer.Exit(1) from exc
# Handle historical data cleanup
handle_history_cleanup(config_name, auto_clean=clean)
# Check for microservice conflicts
running_services = _detect_running_microservices()
if running_services or _check_gateway_port_conflict(port):
if not _display_mode_warning(running_services, port, force=force):
console.print("\n[yellow]Startup aborted.[/yellow]")
raise typer.Exit(0)
# Display standalone mode banner
_display_standalone_banner("backtest", config_name)
# Display configuration
console.print("\n[bold]Configuration:[/bold]")
console.print(" Mode: Backtest")
console.print(f" Config: {config_name}")
console.print(f" Period: {start} -> {end}")
console.print(f" Server: {host}:{port}")
console.print(f" Poll Interval: {poll_interval}s")
@@ -964,6 +1112,9 @@ def backtest(
console.print("\nAccess frontend at: [cyan]http://localhost:5173[/cyan]")
console.print("Press Ctrl+C to stop\n")
# Handle historical data cleanup
handle_history_cleanup(config_name, auto_clean=clean)
# Change to project root
project_root = get_project_root()
os.chdir(project_root)
@@ -1020,10 +1171,10 @@ def backtest(
@app.command()
def live(
config_name: str = typer.Option(
"live",
"default_live_run",
"--config-name",
"-c",
help="Configuration name for this live run",
help="Run label under runs/<config_name> for this live runtime.",
),
host: str = typer.Option(
"0.0.0.0",
@@ -1031,7 +1182,7 @@ def live(
help="WebSocket server host",
),
port: int = typer.Option(
8765,
GATEWAY_PORT,
"--port",
"-p",
help="WebSocket server port",
@@ -1067,11 +1218,19 @@ def live(
"--enable-memory",
help="Enable ReMeTaskLongTermMemory for agents (requires MEMORY_API_KEY)",
),
force: bool = typer.Option(
False,
"--force",
help="Force start even if microservices are detected (may cause conflicts)",
),
):
"""
Run live trading mode with real-time data.
Run live trading mode in STANDALONE mode (monolithic Gateway).
Example:
This starts a self-contained process with all agents. For microservice
mode (distributed services), use ./start-dev.sh instead.
Examples:
evotraders live # Run immediately (default)
evotraders live -t 22:30 # Run at 22:30 local time daily
evotraders live --schedule-mode intraday --interval-minutes 60
@@ -1080,12 +1239,16 @@ def live(
"""
schedule_mode = str(_normalize_typer_value(schedule_mode, "daily"))
interval_minutes = int(_normalize_typer_value(interval_minutes, 60))
console.print(
Panel.fit(
"[bold cyan]大时代 LIVE Mode[/bold cyan]",
border_style="cyan",
),
)
# Check for microservice conflicts
running_services = _detect_running_microservices()
if running_services or _check_gateway_port_conflict(port):
if not _display_mode_warning(running_services, port, force=force):
console.print("\n[yellow]Startup aborted.[/yellow]")
raise typer.Exit(0)
# Display standalone mode banner
_display_standalone_banner("live", config_name)
# Check for required API key in live mode
env_file = get_project_root() / ".env"
@@ -1161,9 +1324,8 @@ def live(
# Display configuration
console.print("\n[bold]Configuration:[/bold]")
console.print(
" Mode: [green]LIVE[/green] (Real-time prices via Finnhub)",
" Data Mode: [green]LIVE[/green] (Real-time prices via Finnhub)",
)
console.print(f" Config: {config_name}")
console.print(f" Server: {host}:{port}")
console.print(f" Poll Interval: {poll_interval}s")
console.print(
@@ -1230,7 +1392,7 @@ def live(
@app.command()
def frontend(
port: int = typer.Option(
8765,
GATEWAY_PORT,
"--ws-port",
"-p",
help="WebSocket server port to connect to",
@@ -1317,6 +1479,90 @@ def frontend(
raise typer.Exit(1)
@app.command()
def status(
detailed: bool = typer.Option(
False,
"--detailed",
"-d",
help="Show detailed service information",
),
):
"""
Check the status of running services (microservice or standalone mode).
Detects whether microservices are running and shows their health status.
"""
console.print(
Panel.fit(
"[bold cyan]大时代 Service Status[/bold cyan]",
border_style="cyan",
)
)
running_services = _detect_running_microservices()
gateway_running = _check_gateway_port_conflict(GATEWAY_PORT)
# Determine mode
if running_services:
mode = "microservice"
console.print(f"\n[bold]Mode:[/bold] [green]{mode.upper()}[/green]")
console.print("[dim]Microservices are running on the following ports:[/dim]\n")
table = Table(title="Running Microservices")
table.add_column("Service", style="cyan")
table.add_column("Port", justify="right")
table.add_column("URL")
for service, port in running_services.items():
url = f"http://localhost:{port}"
table.add_row(service, str(port), url)
if gateway_running:
table.add_row(
"gateway (WebSocket)",
str(GATEWAY_PORT),
f"ws://localhost:{GATEWAY_PORT}",
)
console.print(table)
elif gateway_running:
mode = "standalone"
console.print(f"\n[bold]Mode:[/bold] [yellow]{mode.upper()}[/yellow]")
console.print("[dim]Standalone Gateway is running (monolithic mode)[/dim]")
console.print(f"\n Gateway: [cyan]ws://localhost:{GATEWAY_PORT}[/cyan]")
else:
console.print(f"\n[bold]Mode:[/bold] [red]NOT RUNNING[/red]")
console.print("\n[dim]No services detected. Start with:[/dim]")
console.print(" • Standalone: [cyan]evotraders backtest[/cyan] or [cyan]evotraders live[/cyan]")
console.print(" • Microservice: [cyan]./start-dev.sh[/cyan]")
if detailed and running_services:
console.print("\n[bold]Health Checks:[/bold]")
import urllib.request
import json
for service, port in running_services.items():
try:
req = urllib.request.Request(
f"http://localhost:{port}/health",
method="GET",
headers={"Accept": "application/json"},
)
with urllib.request.urlopen(req, timeout=2) as response:
if response.status == 200:
data = json.loads(response.read().decode())
status_text = data.get("status", "unknown")
color = "green" if status_text == "healthy" else "yellow"
console.print(f" {service}: [{color}]{status_text}[/{color}]")
else:
console.print(f" {service}: [yellow]HTTP {response.status}[/yellow]")
except Exception as e:
console.print(f" {service}: [red]unreachable ({type(e).__name__})[/red]")
console.print()
@app.command()
def version():
"""Show the version of 大时代."""
@@ -1330,7 +1576,17 @@ def main():
"""
大时代：自进化多智能体交易系统
Use 'evotraders --help' to see available commands.
RUNTIME MODES:
--------------
• STANDALONE (default): Use 'evotraders backtest' or 'evotraders live'
Starts a self-contained monolithic Gateway with all agents.
Best for: quick testing, single-machine deployment
• MICROSERVICE: Use './start-dev.sh'
Starts 4 separate FastAPI services + Gateway subprocess.
Best for: production scaling, distributed deployment
Use 'evotraders status' to check which mode is currently running.
"""

2
backend/config/bootstrap_config.py
View File

@@ -77,7 +77,7 @@ def get_bootstrap_config_for_run(
project_root: Path,
config_name: str,
) -> BootstrapConfig:
"""Load BOOTSTRAP.md from the run workspace."""
"""Load BOOTSTRAP.md from the run-scoped asset tree."""
return load_bootstrap_config(
project_root / "runs" / config_name / "BOOTSTRAP.md",
)

190
backend/core/pipeline.py
View File

@@ -26,13 +26,45 @@ from backend.agents.team_pipeline_config import (
resolve_active_analysts,
update_active_analysts,
)
from backend.agents import AnalystAgent
from backend.agents import AnalystAgent, EvoAgent
from backend.agents.agent_workspace import load_agent_workspace_config
from backend.agents.toolkit_factory import create_agent_toolkit
from backend.agents.workspace_manager import WorkspaceManager
from backend.agents.prompt_loader import get_prompt_loader
from backend.llm.models import get_agent_formatter, get_agent_model
from backend.config.constants import ANALYST_TYPES
def _resolve_evo_agent_ids() -> set[str]:
"""Return agent ids selected to use EvoAgent.
By default, all supported roles use EvoAgent.
EVO_AGENT_IDS can be used to limit to specific roles.
Supported roles:
- analyst roles (fundamentals, technical, sentiment, valuation)
- risk_manager
- portfolio_manager
Example:
EVO_AGENT_IDS=fundamentals_analyst,risk_manager,portfolio_manager
"""
raw = os.getenv("EVO_AGENT_IDS", "")
if not raw.strip():
# Default: all supported roles use EvoAgent
return set(ANALYST_TYPES) | {"risk_manager", "portfolio_manager"}
requested = {
item.strip()
for item in raw.split(",")
if item.strip()
}
return {
agent_id
for agent_id in requested
if agent_id in ANALYST_TYPES or agent_id in {"risk_manager", "portfolio_manager"}
}
# Team infrastructure imports (graceful import - may not exist yet)
try:
from backend.agents.team.team_coordinator import TeamCoordinator
@@ -140,6 +172,10 @@ class TradingPipeline:
session_key = TradingSessionKey(date=date).key()
self._session_key = session_key
active_analysts = self._get_active_analysts()
self._sync_agent_runtime_context(
agents=active_analysts + [self.risk_manager, self.pm],
session_key=session_key,
)
if self.runtime_manager:
self.runtime_manager.set_session_key(session_key)
self._runtime_log_event("cycle:start", {"tickers": tickers, "date": date})
@@ -1488,108 +1524,6 @@ class TradingPipeline:
return "Decisions: " + "; ".join(decision_texts)
return "Portfolio analysis completed. No trades recommended."
def load_agents_from_workspace(
self,
workspace_id: str,
agent_factory: Optional[Any] = None,
) -> Dict[str, Any]:
"""
Load agents from workspace using AgentFactory.
This method supports the new EvoAgent architecture by loading
agents from a workspace instead of using hardcoded agents.
Args:
workspace_id: Workspace identifier
agent_factory: Optional AgentFactory instance (uses self.agent_factory if None)
Returns:
Dictionary with loaded agents:
{
"analysts": List[EvoAgent],
"risk_manager": EvoAgent,
"portfolio_manager": EvoAgent,
}
Raises:
ValueError: If workspace doesn't exist or no agents found
"""
factory = agent_factory or self.agent_factory
if factory is None:
from backend.agents import AgentFactory
factory = AgentFactory()
# Check workspace exists
if not factory.workspaces_root.exists():
raise ValueError(f"Workspaces root does not exist: {factory.workspaces_root}")
workspace_dir = factory.workspaces_root / workspace_id
if not workspace_dir.exists():
raise ValueError(f"Workspace '{workspace_id}' does not exist")
# Load agents from workspace
agents_data = factory.list_agents(workspace_id=workspace_id)
if not agents_data:
raise ValueError(f"No agents found in workspace '{workspace_id}'")
# Categorize agents by type
analysts = []
risk_manager = None
portfolio_manager = None
for agent_data in agents_data:
agent_type = agent_data.get("agent_type", "unknown")
agent_id = agent_data.get("agent_id")
# Load full agent configuration
config_path = Path(agent_data.get("config_path", ""))
if config_path.exists():
agent = factory.load_agent(agent_id, workspace_id)
if agent_type.endswith("_analyst"):
analysts.append(agent)
elif agent_type == "risk_manager":
risk_manager = agent
elif agent_type == "portfolio_manager":
portfolio_manager = agent
if not analysts:
raise ValueError(f"No analysts found in workspace '{workspace_id}'")
if risk_manager is None:
raise ValueError(f"No risk_manager found in workspace '{workspace_id}'")
if portfolio_manager is None:
raise ValueError(f"No portfolio_manager found in workspace '{workspace_id}'")
return {
"analysts": analysts,
"risk_manager": risk_manager,
"portfolio_manager": portfolio_manager,
}
def reload_agents_from_workspace(self, workspace_id: Optional[str] = None) -> None:
"""
Reload all agents from workspace.
This updates self.analysts, self.risk_manager, and self.pm
with agents loaded from the specified workspace.
Args:
workspace_id: Workspace ID (uses self.workspace_id if None)
"""
ws_id = workspace_id or self.workspace_id
if not ws_id:
raise ValueError("No workspace_id specified")
loaded = self.load_agents_from_workspace(ws_id)
self.analysts = loaded["analysts"]
self.risk_manager = loaded["risk_manager"]
self.pm = loaded["portfolio_manager"]
self.workspace_id = ws_id
logger.info(f"Reloaded {len(self.analysts)} analysts from workspace '{ws_id}'")
def _runtime_update_status(self, agent: Any, status: str) -> None:
if not self.runtime_manager:
return
@@ -1602,6 +1536,28 @@ class TradingPipeline:
for agent in agents:
self._runtime_update_status(agent, status)
def _sync_agent_runtime_context(
self,
agents: List[Any],
session_key: str,
) -> None:
"""Propagate run/session identifiers onto agent instances.
EvoAgent's tool-guard approval records depend on workspace/session
context being present on the agent object at runtime.
"""
config_name = getattr(self.pm, "config", {}).get("config_name", "default")
for agent in agents:
try:
setattr(agent, "session_id", session_key)
if not getattr(agent, "run_id", None):
setattr(agent, "run_id", config_name)
# Keep workspace_id for backward compatibility
if not getattr(agent, "workspace_id", None):
setattr(agent, "workspace_id", config_name)
except Exception:
continue
def _all_analysts(self) -> List[Any]:
"""Return static analysts plus runtime-created analysts."""
return list(self.analysts) + list(self._dynamic_analysts.values())
@@ -1630,6 +1586,34 @@ class TradingPipeline:
),
)
# Determine whether to use EvoAgent based on EVO_AGENT_IDS
use_evo_agent = analyst_type in _resolve_evo_agent_ids()
if use_evo_agent:
from backend.agents.skills_manager import SkillsManager
skills_manager = SkillsManager(project_root=project_root)
workspace_dir = skills_manager.get_agent_asset_dir(
config_name,
agent_id,
)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id=agent_id,
config_name=config_name,
workspace_dir=workspace_dir,
model=get_agent_model(analyst_type),
formatter=get_agent_formatter(analyst_type),
prompt_files=agent_config.prompt_files,
)
agent.toolkit = create_agent_toolkit(
agent_id=agent_id,
config_name=config_name,
active_skill_dirs=[],
)
setattr(agent, "run_id", config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", config_name)
else:
agent = AnalystAgent(
analyst_type=analyst_type,
toolkit=create_agent_toolkit(

324
backend/core/pipeline_runner.py
View File

@@ -12,9 +12,10 @@ import asyncio
import os
from contextlib import AsyncExitStack
from pathlib import Path
from typing import Any, Dict, Optional, Callable
from typing import Any, Dict, List, Optional, Callable
from backend.agents import AnalystAgent, PMAgent, RiskAgent
from backend.agents import AnalystAgent, EvoAgent, PMAgent, RiskAgent
from backend.agents.agent_workspace import load_agent_workspace_config
from backend.agents.skills_manager import SkillsManager
from backend.agents.toolkit_factory import create_agent_toolkit, load_agent_profiles
from backend.agents.prompt_loader import get_prompt_loader
@@ -41,6 +42,9 @@ _prompt_loader = get_prompt_loader()
# Global gateway reference for cleanup
_gateway_instance: Optional[Gateway] = None
# Global long-term memory references for persistence
_long_term_memories: List[Any] = []
def _set_gateway(gateway: Optional[Gateway]) -> None:
"""Set global gateway reference."""
@@ -61,6 +65,101 @@ def stop_gateway() -> None:
_gateway_instance = None
def _set_long_term_memories(memories: List[Any]) -> None:
"""Set global long-term memory references."""
global _long_term_memories
_long_term_memories = memories
def _clear_long_term_memories() -> None:
"""Clear global long-term memory references."""
global _long_term_memories
_long_term_memories = []
def _persist_long_term_memories_sync() -> None:
"""
Synchronously persist all long-term memories before shutdown.
This function ensures all memory data is flushed to disk/vector store
before the process exits. Should be called during cleanup.
"""
global _long_term_memories
if not _long_term_memories:
return
import logging
logger = logging.getLogger(__name__)
logger.info(f"[MemoryPersistence] Persisting {len(_long_term_memories)} memory instances...")
for i, memory in enumerate(_long_term_memories):
try:
# Try to save memory if it has a save method
if hasattr(memory, 'save') and callable(getattr(memory, 'save')):
if hasattr(memory, 'sync') and callable(getattr(memory, 'sync')):
# Use sync version if available
memory.sync()
logger.debug(f"[MemoryPersistence] Synced memory {i}")
else:
# Try async save with event loop
import asyncio
try:
loop = asyncio.get_event_loop()
if loop.is_running():
# Schedule save in running loop
loop.create_task(memory.save())
logger.debug(f"[MemoryPersistence] Scheduled save for memory {i}")
else:
loop.run_until_complete(memory.save())
logger.debug(f"[MemoryPersistence] Saved memory {i}")
except RuntimeError:
# No event loop, skip async save
pass
# Try to flush any pending writes
if hasattr(memory, 'flush') and callable(getattr(memory, 'flush')):
memory.flush()
logger.debug(f"[MemoryPersistence] Flushed memory {i}")
except Exception as e:
logger.warning(f"[MemoryPersistence] Failed to persist memory {i}: {e}")
logger.info("[MemoryPersistence] Memory persistence complete")
async def _persist_long_term_memories_async() -> None:
"""
Asynchronously persist all long-term memories.
This is the preferred method for persisting memories when
an async context is available.
"""
global _long_term_memories
if not _long_term_memories:
return
import logging
logger = logging.getLogger(__name__)
logger.info(f"[MemoryPersistence] Persisting {len(_long_term_memories)} memory instances async...")
for i, memory in enumerate(_long_term_memories):
try:
# Try async save first
if hasattr(memory, 'save') and callable(getattr(memory, 'save')):
await memory.save()
logger.debug(f"[MemoryPersistence] Saved memory {i} (async)")
# Try flush if available
if hasattr(memory, 'flush') and callable(getattr(memory, 'flush')):
memory.flush()
logger.debug(f"[MemoryPersistence] Flushed memory {i}")
except Exception as e:
logger.warning(f"[MemoryPersistence] Failed to persist memory {i}: {e}")
logger.info("[MemoryPersistence] Async memory persistence complete")
def create_long_term_memory(agent_name: str, run_id: str, run_dir: Path):
"""Create ReMeTaskLongTermMemory for an agent."""
try:
@@ -96,6 +195,179 @@ def create_long_term_memory(agent_name: str, run_id: str, run_dir: Path):
)
def _resolve_evo_agent_ids() -> set[str]:
"""Return agent ids selected to use EvoAgent.
By default, all supported roles use EvoAgent.
"""
raw = os.getenv("EVO_AGENT_IDS", "")
if not raw.strip():
# Default: all supported roles use EvoAgent
return set(ANALYST_TYPES) | {"risk_manager", "portfolio_manager"}
requested = {
item.strip()
for item in raw.split(",")
if item.strip()
}
return {
agent_id
for agent_id in requested
if agent_id in ANALYST_TYPES or agent_id in {"risk_manager", "portfolio_manager"}
}
def _create_analyst_agent(
*,
analyst_type: str,
run_id: str,
model,
formatter,
skills_manager: SkillsManager,
active_skill_map: Dict[str, list[Path]],
long_term_memory=None,
):
"""Create one analyst agent, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get(analyst_type, [])
toolkit = create_agent_toolkit(
analyst_type,
run_id,
active_skill_dirs=active_skill_dirs,
)
use_evo_agent = analyst_type in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(run_id, analyst_type)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id=analyst_type,
config_name=run_id,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
long_term_memory=long_term_memory,
)
agent.toolkit = toolkit
setattr(agent, "workspace_id", run_id)
return agent
return AnalystAgent(
analyst_type=analyst_type,
toolkit=toolkit,
model=model,
formatter=formatter,
agent_id=analyst_type,
config={"config_name": run_id},
long_term_memory=long_term_memory,
)
def _create_risk_manager_agent(
*,
run_id: str,
model,
formatter,
skills_manager: SkillsManager,
active_skill_map: Dict[str, list[Path]],
long_term_memory=None,
):
"""Create the risk manager, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get("risk_manager", [])
toolkit = create_agent_toolkit(
"risk_manager",
run_id,
active_skill_dirs=active_skill_dirs,
)
use_evo_agent = "risk_manager" in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(run_id, "risk_manager")
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id="risk_manager",
config_name=run_id,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
long_term_memory=long_term_memory,
)
agent.toolkit = toolkit
setattr(agent, "workspace_id", run_id)
return agent
return RiskAgent(
model=model,
formatter=formatter,
name="risk_manager",
config={"config_name": run_id},
long_term_memory=long_term_memory,
toolkit=toolkit,
)
def _create_portfolio_manager_agent(
*,
run_id: str,
model,
formatter,
initial_cash: float,
margin_requirement: float,
skills_manager: SkillsManager,
active_skill_map: Dict[str, list[Path]],
long_term_memory=None,
):
"""Create the portfolio manager, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get("portfolio_manager", [])
use_evo_agent = "portfolio_manager" in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(
run_id,
"portfolio_manager",
)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id="portfolio_manager",
config_name=run_id,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
initial_cash=initial_cash,
margin_requirement=margin_requirement,
long_term_memory=long_term_memory,
)
agent.toolkit = create_agent_toolkit(
"portfolio_manager",
run_id,
owner=agent,
active_skill_dirs=active_skill_dirs,
)
setattr(agent, "workspace_id", run_id)
return agent
return PMAgent(
name="portfolio_manager",
model=model,
formatter=formatter,
initial_cash=initial_cash,
margin_requirement=margin_requirement,
config={"config_name": run_id},
long_term_memory=long_term_memory,
toolkit_factory=create_agent_toolkit,
toolkit_factory_kwargs={
"active_skill_dirs": active_skill_dirs,
},
)
def create_agents(
run_id: str,
run_dir: Path,
@@ -129,11 +401,6 @@ def create_agents(
for analyst_type in ANALYST_TYPES:
model = get_agent_model(analyst_type)
formatter = get_agent_formatter(analyst_type)
toolkit = create_agent_toolkit(
analyst_type,
run_id,
active_skill_dirs=active_skill_map.get(analyst_type, []),
)
long_term_memory = None
if enable_long_term_memory:
@@ -141,13 +408,13 @@ def create_agents(
if long_term_memory:
long_term_memories.append(long_term_memory)
analyst = AnalystAgent(
analyst = _create_analyst_agent(
analyst_type=analyst_type,
toolkit=toolkit,
run_id=run_id,
model=model,
formatter=formatter,
agent_id=analyst_type,
config={"config_name": run_id},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=long_term_memory,
)
analysts.append(analyst)
@@ -159,17 +426,13 @@ def create_agents(
if risk_long_term_memory:
long_term_memories.append(risk_long_term_memory)
risk_manager = RiskAgent(
risk_manager = _create_risk_manager_agent(
run_id=run_id,
model=get_agent_model("risk_manager"),
formatter=get_agent_formatter("risk_manager"),
name="risk_manager",
config={"config_name": run_id},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=risk_long_term_memory,
toolkit=create_agent_toolkit(
"risk_manager",
run_id,
active_skill_dirs=active_skill_map.get("risk_manager", []),
),
)
# Create portfolio manager
@@ -179,18 +442,15 @@ def create_agents(
if pm_long_term_memory:
long_term_memories.append(pm_long_term_memory)
portfolio_manager = PMAgent(
name="portfolio_manager",
portfolio_manager = _create_portfolio_manager_agent(
run_id=run_id,
model=get_agent_model("portfolio_manager"),
formatter=get_agent_formatter("portfolio_manager"),
initial_cash=initial_cash,
margin_requirement=margin_requirement,
config={"config_name": run_id},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=pm_long_term_memory,
toolkit_factory=create_agent_toolkit,
toolkit_factory_kwargs={
"active_skill_dirs": active_skill_map.get("portfolio_manager", []),
},
)
return analysts, risk_manager, portfolio_manager, long_term_memories
@@ -400,6 +660,9 @@ async def run_pipeline(
)
_set_gateway(gateway)
# Set global memory references for persistence
_set_long_term_memories(long_term_memories)
# Start pipeline execution
async with AsyncExitStack() as stack:
# Enter long-term memory contexts
@@ -467,6 +730,12 @@ async def run_pipeline(
# Cleanup
logger.info("[Pipeline] Cleaning up...")
# Persist long-term memories before cleanup
try:
await _persist_long_term_memories_async()
except Exception as e:
logger.warning(f"[Pipeline] Memory persistence error: {e}")
# Stop Gateway
try:
stop_gateway()
@@ -474,6 +743,9 @@ async def run_pipeline(
except Exception as e:
logger.error(f"[Pipeline] Error stopping gateway: {e}")
# Clear memory references
_clear_long_term_memories()
clear_shutdown_event()
clear_global_runtime_manager()
from backend.api.runtime import unregister_runtime_manager

47
backend/core/state_sync.py
View File

@@ -463,6 +463,34 @@ class StateSync:
limit=self.storage.max_feed_history,
) or self._state.get("last_day_history", [])
persisted_state = self.storage.read_persisted_server_state()
dashboard_snapshot = (
self.storage.build_dashboard_snapshot_from_state(self._state)
if include_dashboard
else None
)
dashboard_holdings = (
dashboard_snapshot.get("holdings", [])
if dashboard_snapshot is not None
else self._state.get("holdings", [])
)
dashboard_trades = (
dashboard_snapshot.get("trades", [])
if dashboard_snapshot is not None
else self._state.get("trades", [])
)
dashboard_stats = (
dashboard_snapshot.get("stats", {})
if dashboard_snapshot is not None
else self._state.get("stats", {})
)
dashboard_leaderboard = (
dashboard_snapshot.get("leaderboard", [])
if dashboard_snapshot is not None
else self._state.get("leaderboard", [])
)
portfolio_state = self._state.get("portfolio") or persisted_state.get("portfolio") or {}
payload = {
"server_mode": self._state.get("server_mode", "live"),
"is_backtest": self._state.get("is_backtest", False),
@@ -476,24 +504,23 @@ class StateSync:
"trading_days_completed",
0,
),
"holdings": self._state.get("holdings", []),
"trades": self._state.get("trades", []),
"stats": self._state.get("stats", {}),
"leaderboard": self._state.get("leaderboard", []),
"portfolio": self._state.get("portfolio", {}),
"holdings": dashboard_holdings,
"trades": dashboard_trades,
"stats": dashboard_stats,
"leaderboard": dashboard_leaderboard,
"portfolio": portfolio_state,
"realtime_prices": self._state.get("realtime_prices", {}),
"data_sources": self._state.get("data_sources", {}),
"price_history": self._state.get("price_history", {}),
}
if include_dashboard:
dashboard_snapshot = self.storage.build_dashboard_snapshot_from_state(self._state)
payload["dashboard"] = {
"summary": dashboard_snapshot.get("summary"),
"holdings": dashboard_snapshot.get("holdings"),
"stats": dashboard_snapshot.get("stats"),
"trades": dashboard_snapshot.get("trades"),
"leaderboard": dashboard_snapshot.get("leaderboard"),
"holdings": dashboard_holdings,
"stats": dashboard_stats,
"trades": dashboard_trades,
"leaderboard": dashboard_leaderboard,
}
return payload

275
backend/main.py
View File

@@ -13,10 +13,13 @@ import loguru
from dotenv import load_dotenv
from backend.agents import AnalystAgent, PMAgent, RiskAgent
from backend.agents import AnalystAgent, EvoAgent, PMAgent, RiskAgent
from backend.agents.agent_workspace import load_agent_workspace_config
from backend.agents.skills_manager import SkillsManager
from backend.agents.toolkit_factory import create_agent_toolkit, load_agent_profiles
from backend.agents.prompt_loader import get_prompt_loader
# WorkspaceManager is RunWorkspaceManager - provides run-scoped asset management
# All runtime state lives under runs/<run_id>/
from backend.agents.workspace_manager import WorkspaceManager
from backend.config.bootstrap_config import resolve_runtime_config
from backend.config.constants import ANALYST_TYPES
@@ -44,8 +47,13 @@ _prompt_loader = get_prompt_loader()
def _get_run_dir(config_name: str) -> Path:
"""Return the canonical run-scoped directory for a config."""
"""Return the canonical run-scoped directory for a config.
This is the authoritative path for runtime state under runs/<run_id>/.
All runtime assets, state, and exports are scoped to this directory.
"""
project_root = Path(__file__).resolve().parents[1]
# Use RunWorkspaceManager for run-scoped path resolution
return WorkspaceManager(project_root=project_root).get_run_dir(config_name)
@@ -102,6 +110,204 @@ def create_long_term_memory(agent_name: str, config_name: str):
)
def _resolve_evo_agent_ids() -> set[str]:
"""Return agent ids selected to use EvoAgent.
By default, all supported roles use EvoAgent.
EVO_AGENT_IDS can be used to limit to specific roles (legacy behavior).
Set EVO_AGENT_LEGACY=1 to disable EvoAgent entirely.
Supported roles:
- analyst roles (fundamentals, technical, sentiment, valuation)
- risk_manager
- portfolio_manager
Example:
EVO_AGENT_IDS=fundamentals_analyst,risk_manager,portfolio_manager
"""
from backend.config.constants import ANALYST_TYPES
all_supported = set(ANALYST_TYPES) | {"risk_manager", "portfolio_manager"}
raw = os.getenv("EVO_AGENT_IDS", "")
if not raw.strip():
# Default: all supported roles use EvoAgent
return all_supported
if raw.strip().lower() in ("legacy", "old", "none"):
return set()
requested = {
item.strip()
for item in raw.split(",")
if item.strip()
}
return {
agent_id
for agent_id in requested
if agent_id in ANALYST_TYPES or agent_id in {"risk_manager", "portfolio_manager"}
}
def _create_analyst_agent(
*,
analyst_type: str,
config_name: str,
model,
formatter,
skills_manager: SkillsManager,
active_skill_map: dict[str, list[Path]],
long_term_memory=None,
):
"""Create one analyst agent, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get(analyst_type, [])
toolkit = create_agent_toolkit(
analyst_type,
config_name,
active_skill_dirs=active_skill_dirs,
)
use_evo_agent = analyst_type in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(config_name, analyst_type)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id=analyst_type,
config_name=config_name,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
long_term_memory=long_term_memory,
)
# Preserve existing analysis tool-group coverage while the EvoAgent
# migration is still partial.
agent.toolkit = toolkit
setattr(agent, "run_id", config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", config_name)
return agent
return AnalystAgent(
analyst_type=analyst_type,
toolkit=toolkit,
model=model,
formatter=formatter,
agent_id=analyst_type,
config={"config_name": config_name},
long_term_memory=long_term_memory,
)
def _create_risk_manager_agent(
*,
config_name: str,
model,
formatter,
skills_manager: SkillsManager,
active_skill_map: dict[str, list[Path]],
long_term_memory=None,
):
"""Create the risk manager, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get("risk_manager", [])
toolkit = create_agent_toolkit(
"risk_manager",
config_name,
active_skill_dirs=active_skill_dirs,
)
use_evo_agent = "risk_manager" in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(config_name, "risk_manager")
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id="risk_manager",
config_name=config_name,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
long_term_memory=long_term_memory,
)
agent.toolkit = toolkit
setattr(agent, "run_id", config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", config_name)
return agent
return RiskAgent(
model=model,
formatter=formatter,
name="risk_manager",
config={"config_name": config_name},
long_term_memory=long_term_memory,
toolkit=toolkit,
)
def _create_portfolio_manager_agent(
*,
config_name: str,
model,
formatter,
initial_cash: float,
margin_requirement: float,
skills_manager: SkillsManager,
active_skill_map: dict[str, list[Path]],
long_term_memory=None,
):
"""Create the portfolio manager, optionally using EvoAgent."""
active_skill_dirs = active_skill_map.get("portfolio_manager", [])
use_evo_agent = "portfolio_manager" in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(
config_name,
"portfolio_manager",
)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id="portfolio_manager",
config_name=config_name,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
initial_cash=initial_cash,
margin_requirement=margin_requirement,
long_term_memory=long_term_memory,
)
agent.toolkit = create_agent_toolkit(
"portfolio_manager",
config_name,
owner=agent,
active_skill_dirs=active_skill_dirs,
)
setattr(agent, "run_id", config_name)
# Keep workspace_id for backward compatibility
setattr(agent, "workspace_id", config_name)
return agent
return PMAgent(
name="portfolio_manager",
model=model,
formatter=formatter,
initial_cash=initial_cash,
margin_requirement=margin_requirement,
config={"config_name": config_name},
long_term_memory=long_term_memory,
toolkit_factory=create_agent_toolkit,
toolkit_factory_kwargs={
"active_skill_dirs": active_skill_dirs,
},
)
def create_agents(
config_name: str,
initial_cash: float,
@@ -136,11 +342,6 @@ def create_agents(
for analyst_type in ANALYST_TYPES:
model = get_agent_model(analyst_type)
formatter = get_agent_formatter(analyst_type)
toolkit = create_agent_toolkit(
analyst_type,
config_name,
active_skill_dirs=active_skill_map.get(analyst_type, []),
)
long_term_memory = None
if enable_long_term_memory:
@@ -151,13 +352,13 @@ def create_agents(
if long_term_memory:
long_term_memories.append(long_term_memory)
analyst = AnalystAgent(
analyst = _create_analyst_agent(
analyst_type=analyst_type,
toolkit=toolkit,
config_name=config_name,
model=model,
formatter=formatter,
agent_id=analyst_type,
config={"config_name": config_name},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=long_term_memory,
)
analysts.append(analyst)
@@ -171,17 +372,13 @@ def create_agents(
if risk_long_term_memory:
long_term_memories.append(risk_long_term_memory)
risk_manager = RiskAgent(
risk_manager = _create_risk_manager_agent(
config_name=config_name,
model=get_agent_model("risk_manager"),
formatter=get_agent_formatter("risk_manager"),
name="risk_manager",
config={"config_name": config_name},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=risk_long_term_memory,
toolkit=create_agent_toolkit(
"risk_manager",
config_name,
active_skill_dirs=active_skill_map.get("risk_manager", []),
),
)
pm_long_term_memory = None
@@ -193,21 +390,15 @@ def create_agents(
if pm_long_term_memory:
long_term_memories.append(pm_long_term_memory)
portfolio_manager = PMAgent(
name="portfolio_manager",
portfolio_manager = _create_portfolio_manager_agent(
config_name=config_name,
model=get_agent_model("portfolio_manager"),
formatter=get_agent_formatter("portfolio_manager"),
initial_cash=initial_cash,
margin_requirement=margin_requirement,
config={"config_name": config_name},
skills_manager=skills_manager,
active_skill_map=active_skill_map,
long_term_memory=pm_long_term_memory,
toolkit_factory=create_agent_toolkit,
toolkit_factory_kwargs={
"active_skill_dirs": active_skill_map.get(
"portfolio_manager",
[],
),
},
)
return analysts, risk_manager, portfolio_manager, long_term_memories
@@ -343,15 +534,29 @@ async def run_with_gateway(args):
await stack.enter_async_context(memory)
await gateway.start(host=args.host, port=args.port)
finally:
# Persist long-term memories before cleanup
for memory in long_term_memories:
try:
if hasattr(memory, 'save') and callable(getattr(memory, 'save')):
await memory.save()
except Exception as e:
logger.warning(f"Failed to persist memory: {e}")
unregister_runtime_manager()
clear_global_runtime_manager()
def main():
"""Main entry point"""
def build_arg_parser() -> argparse.ArgumentParser:
"""Build the CLI parser for the gateway runtime entrypoint."""
parser = argparse.ArgumentParser(description="Trading System")
parser.add_argument("--mode", choices=["live", "backtest"], default="live")
parser.add_argument("--config-name", default="live")
parser.add_argument(
"--config-name",
default="default_run",
help=(
"Run label under runs/<config_name>; not a special root-level "
"live/backtest/production directory."
),
)
parser.add_argument("--host", default="0.0.0.0")
parser.add_argument("--port", type=int, default=8765)
parser.add_argument(
@@ -369,6 +574,12 @@ def main():
action="store_true",
help="Enable ReMeTaskLongTermMemory for agents",
)
return parser
def main():
"""Main entry point"""
parser = build_arg_parser()
args = parser.parse_args()

18
backend/services/gateway_openclaw_handlers.py
View File

@@ -1,5 +1,21 @@
# -*- coding: utf-8 -*-
"""OpenClaw WebSocket handlers — gateway calls OpenClaw Gateway via WebSocket."""
"""OpenClaw WebSocket handlers — gateway calls OpenClaw Gateway via WebSocket.
COMPATIBILITY_SURFACE: deferred
OWNER: runtime-team
SEE: docs/legacy-inventory.md#openclaw-dual-integration
This is the WebSocket gateway integration for OpenClaw (port 18789).
For the REST facade, see:
- backend/apps/openclaw_service.py (port 8004)
- backend/api/openclaw.py
Key differences:
- WebSocket: event-driven, real-time updates, bidirectional
- REST facade: typed Pydantic models, request/response, polling
Decision needed: which surface becomes the long-term contract?
"""
from __future__ import annotations

201
backend/services/storage.py
View File

@@ -6,6 +6,7 @@ Handles reading/writing dashboard JSON files and portfolio state
# pylint: disable=R0904
import json
import logging
import os
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
@@ -21,25 +22,31 @@ class StorageService:
Storage service for data persistence
Responsibilities:
1. Export dashboard JSON files
1. Export dashboard JSON files (compatibility layer)
(summary, holdings, stats, trades, leaderboard)
2. Load/save internal state (_internal_state.json)
3. Load/save server state (server_state.json) with feed history
4. Manage portfolio state persistence
5. Support loading from saved state to resume execution
Notes:
- team_dashboard/*.json is treated as an export/compatibility layer
rather than the authoritative runtime source of truth.
- authoritative runtime reads should prefer in-memory state, server_state,
runtime.db, and market_research.db.
Architecture Notes:
- runs/<run_id>/ is the authoritative runtime state root
- team_dashboard/*.json is a NON-AUTHORITATIVE export/compatibility layer
for external consumers (frontend, reports, etc.)
- Authoritative runtime reads should prefer:
1. In-memory state (runtime manager)
2. state/server_state.json
3. state/runtime.db
4. market_research.db
- Compatibility exports can be disabled via ENABLE_DASHBOARD_COMPAT_EXPORTS=false
"""
def __init__(
self,
dashboard_dir: Path,
initial_cash: float = 100000.0,
config_name: str = "live",
config_name: str = "runtime",
enable_compat_exports: Optional[bool] = None,
):
"""
Initialize storage service
@@ -47,12 +54,18 @@ class StorageService:
Args:
dashboard_dir: Directory for dashboard files
initial_cash: Initial cash amount
config_name: Configuration name for state directory
config_name: Logical runtime config/run label for state directory context
enable_compat_exports: Whether to keep writing team_dashboard/*.json
"""
self.dashboard_dir = Path(dashboard_dir)
self.dashboard_dir.mkdir(parents=True, exist_ok=True)
self.initial_cash = initial_cash
self.config_name = config_name
self.enable_compat_exports = (
self._resolve_compat_exports_default()
if enable_compat_exports is None
else bool(enable_compat_exports)
)
# Dashboard export file paths
self.files = {
@@ -88,6 +101,12 @@ class StorageService:
logger.info(f"Storage service initialized: {self.dashboard_dir}")
@staticmethod
def _resolve_compat_exports_default() -> bool:
"""Default compatibility export policy, overridable via env."""
raw = str(os.getenv("ENABLE_DASHBOARD_COMPAT_EXPORTS", "true")).strip().lower()
return raw not in {"0", "false", "no", "off"}
def load_export_file(self, file_type: str) -> Optional[Any]:
"""Load dashboard export JSON file."""
file_path = self.files.get(file_type)
@@ -106,7 +125,9 @@ class StorageService:
return self.load_export_file(file_type)
def save_export_file(self, file_type: str, data: Any):
"""Save dashboard export JSON file."""
"""Save one compatibility dashboard export JSON file."""
if not self.enable_compat_exports:
return
file_path = self.files.get(file_type)
if not file_path:
logger.error(f"Unknown file type: {file_type}")
@@ -127,17 +148,79 @@ class StorageService:
"""Backward-compatible alias for export-layer JSON writes."""
self.save_export_file(file_type, data)
def save_dashboard_exports(self, exports: Dict[str, Any]) -> None:
"""Persist compatibility dashboard exports from a normalized snapshot."""
if not self.enable_compat_exports:
return
for file_type in ("summary", "holdings", "stats", "trades", "leaderboard"):
if file_type in exports:
self.save_export_file(file_type, exports[file_type])
def read_persisted_server_state(self) -> Dict[str, Any]:
"""Read server_state.json without logging or DB side effects."""
if not self.server_state_file.exists():
return {}
try:
with open(self.server_state_file, "r", encoding="utf-8") as f:
payload = json.load(f)
return payload if isinstance(payload, dict) else {}
except Exception as exc:
logger.warning("Failed to read persisted server state: %s", exc)
return {}
def load_runtime_leaderboard(self, state: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]:
"""Prefer runtime state for leaderboard reads, fall back to export JSON."""
runtime_state = state or self.read_persisted_server_state()
leaderboard = runtime_state.get("leaderboard")
if isinstance(leaderboard, list) and leaderboard:
return leaderboard
return self.load_export_file("leaderboard") or []
def persist_runtime_leaderboard(
self,
leaderboard: List[Dict[str, Any]],
state: Optional[Dict[str, Any]] = None,
) -> None:
"""Persist leaderboard to runtime state first, keeping JSON export for compatibility."""
self.save_export_file("leaderboard", leaderboard)
runtime_state = state or self.read_persisted_server_state()
if not runtime_state:
runtime_state = self.load_server_state()
runtime_state["leaderboard"] = leaderboard
self.save_server_state(runtime_state)
def build_dashboard_snapshot_from_state(
self,
state: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Build dashboard view data from runtime state instead of JSON exports."""
runtime_state = state or self.load_server_state()
portfolio = dict(runtime_state.get("portfolio") or {})
holdings = list(runtime_state.get("holdings") or [])
stats = runtime_state.get("stats") or self._get_default_stats()
trades = list(runtime_state.get("trades") or [])
leaderboard = list(runtime_state.get("leaderboard") or [])
persisted_state = self.read_persisted_server_state() if state is not None else {}
portfolio = dict(
runtime_state.get("portfolio")
or persisted_state.get("portfolio")
or {},
)
holdings = list(
runtime_state.get("holdings")
or persisted_state.get("holdings")
or [],
)
stats = (
runtime_state.get("stats")
or persisted_state.get("stats")
or self._get_default_stats()
)
trades = list(
runtime_state.get("trades")
or persisted_state.get("trades")
or [],
)
leaderboard = list(
runtime_state.get("leaderboard")
or persisted_state.get("leaderboard")
or [],
)
summary = {
"totalAssetValue": portfolio.get("total_value", self.initial_cash),
@@ -331,11 +414,10 @@ class StorageService:
self.save_internal_state(internal_state)
def initialize_empty_dashboard(self):
"""Initialize empty dashboard files with default values"""
# Summary
self.save_export_file(
"summary",
"""Initialize compatibility dashboard exports with default values."""
self.save_dashboard_exports(
{
"summary": {
"totalAssetValue": self.initial_cash,
"totalReturn": 0.0,
"cashPosition": self.initial_cash,
@@ -348,15 +430,8 @@ class StorageService:
"baseline_vw": [],
"momentum": [],
},
)
# Holdings
self.save_export_file("holdings", [])
# Stats
self.save_export_file(
"stats",
{
"holdings": [],
"stats": {
"totalAssetValue": self.initial_cash,
"totalReturn": 0.0,
"cashPosition": self.initial_cash,
@@ -368,11 +443,9 @@ class StorageService:
"bear": {"n": 0, "win": 0},
},
},
"trades": [],
},
)
# Trades
self.save_export_file("trades", [])
# Leaderboard with model info
self.generate_leaderboard()
@@ -411,7 +484,7 @@ class StorageService:
ranking_entries.append(entry)
leaderboard = team_entries + ranking_entries
self.save_export_file("leaderboard", leaderboard)
self.persist_runtime_leaderboard(leaderboard)
logger.info("Leaderboard generated with model info")
def update_leaderboard_model_info(self):
@@ -421,7 +494,7 @@ class StorageService:
from ..config.constants import AGENT_CONFIG
from ..llm.models import get_agent_model_info
existing = self.load_file("leaderboard") or []
existing = self.load_runtime_leaderboard()
if not existing:
self.generate_leaderboard()
@@ -434,7 +507,7 @@ class StorageService:
entry["modelName"] = model_name
entry["modelProvider"] = model_provider
self.save_export_file("leaderboard", existing)
self.persist_runtime_leaderboard(existing)
logger.info("Leaderboard model info updated")
def get_current_timestamp_ms(self, date: str = None) -> int:
@@ -640,21 +713,21 @@ class StorageService:
state["last_update_date"] = date
self.save_internal_state(state)
self._generate_summary(state, net_value, prices)
self._generate_holdings(state, prices)
self._generate_stats(state, net_value)
self._generate_trades(state)
self.export_dashboard_compatibility_files(
state,
net_value=net_value,
prices=prices,
)
logger.info(f"Dashboard updated: net_value=${net_value:,.2f}")
def _generate_summary(
def _build_summary_export(
self,
state: Dict[str, Any],
net_value: float,
prices: Dict[str, float],
):
"""Generate summary.json"""
) -> Dict[str, Any]:
"""Build compatibility summary export payload."""
portfolio_state = state.get("portfolio_state", {})
cash = portfolio_state.get("cash", self.initial_cash)
@@ -675,7 +748,7 @@ class StorageService:
(net_value - self.initial_cash) / self.initial_cash
) * 100
summary = {
return {
"totalAssetValue": round(net_value, 2),
"totalReturn": round(total_return, 2),
"cashPosition": round(cash, 2),
@@ -689,14 +762,12 @@ class StorageService:
"momentum": state.get("momentum_history", []),
}
self.save_export_file("summary", summary)
def _generate_holdings(
def _build_holdings_export(
self,
state: Dict[str, Any],
prices: Dict[str, float],
):
"""Generate holdings.json"""
) -> List[Dict[str, Any]]:
"""Build compatibility holdings export payload."""
portfolio_state = state.get("portfolio_state", {})
positions = portfolio_state.get("positions", {})
cash = portfolio_state.get("cash", self.initial_cash)
@@ -750,18 +821,17 @@ class StorageService:
# Sort by weight
holdings.sort(key=lambda x: abs(x["weight"]), reverse=True)
return holdings
self.save_export_file("holdings", holdings)
def _generate_stats(self, state: Dict[str, Any], net_value: float):
"""Generate stats.json"""
def _build_stats_export(self, state: Dict[str, Any], net_value: float) -> Dict[str, Any]:
"""Build compatibility stats export payload."""
portfolio_state = state.get("portfolio_state", {})
cash = portfolio_state.get("cash", self.initial_cash)
total_return = (
(net_value - self.initial_cash) / self.initial_cash
) * 100
stats = {
return {
"totalAssetValue": round(net_value, 2),
"totalReturn": round(total_return, 2),
"cashPosition": round(cash, 2),
@@ -774,10 +844,8 @@ class StorageService:
},
}
self.save_export_file("stats", stats)
def _generate_trades(self, state: Dict[str, Any]):
"""Generate trades.json"""
def _build_trades_export(self, state: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Build compatibility trades export payload."""
all_trades = state.get("all_trades", [])
sorted_trades = sorted(
@@ -800,7 +868,24 @@ class StorageService:
},
)
self.save_export_file("trades", trades)
return trades
def export_dashboard_compatibility_files(
self,
state: Dict[str, Any],
*,
net_value: float,
prices: Dict[str, float],
) -> None:
"""Write compatibility dashboard exports from current runtime state."""
self.save_dashboard_exports(
{
"summary": self._build_summary_export(state, net_value, prices),
"holdings": self._build_holdings_export(state, prices),
"stats": self._build_stats_export(state, net_value),
"trades": self._build_trades_export(state),
},
)
# Server State Management Methods

32
backend/skills/SKILL_TEMPLATE.md
View File

@@ -117,3 +117,35 @@ evaluation_hook.complete_evaluation(success=True)
### 评估结果存储
评估结果自动保存到 `runs/{run_id}/evaluations/{agent_id}/{skill_name}_{timestamp}.json`
---
## Skill Sandbox Execution | 技能沙盒执行
技能脚本（如估值报告生成）通过沙盒执行器运行，支持三种隔离模式：
| 模式 | 描述 | 适用场景 |
|------|------|---------|
| `none` | 直接执行，无隔离 | 开发环境（默认） |
| `docker` | Docker 容器隔离 | 生产环境 |
| `kubernetes` | Kubernetes Pod 隔离 | 企业级（预留） |
### 沙盒配置
环境变量控制沙盒行为：
```bash
SKILL_SANDBOX_MODE=none # none | docker | kubernetes
SKILL_SANDBOX_IMAGE=python:3.11-slim
SKILL_SANDBOX_MEMORY_LIMIT=512m
SKILL_SANDBOX_CPU_LIMIT=1.0
SKILL_SANDBOX_NETWORK=none
SKILL_SANDBOX_TIMEOUT=60
```
### 开发注意事项
- 默认 `none` 模式会在首次执行时显示安全警告
- 生产环境必须设置 `SKILL_SANDBOX_MODE=docker`
- 技能脚本应无副作用，输入输出通过函数参数和返回值
- 函数命名与脚本文件名的映射通过 `FUNCTION_TO_SCRIPT_MAP` 处理（如 `build_ev_ebitda_report` 在 `multiple_valuation_report.py` 中）

18
backend/tests/test_agent_service_app.py
View File

@@ -28,6 +28,19 @@ def test_agent_service_excludes_runtime_routes(tmp_path):
assert "/api/runtime/gateway/port" not in paths
def test_agent_service_status_includes_scope_metadata(tmp_path):
app = create_app(project_root=tmp_path)
with TestClient(app) as client:
response = client.get("/api/status")
assert response.status_code == 200
payload = response.json()
assert payload["scope"]["design_time_registry"]["root"] == str(tmp_path / "workspaces")
assert payload["scope"]["runtime_assets"]["root"] == str(tmp_path / "runs")
assert "runs/<run_id>" in payload["scope"]["agent_route_note"]
def test_agent_service_read_routes(monkeypatch, tmp_path):
class _FakeSkillsManager:
project_root = tmp_path
@@ -96,9 +109,14 @@ def test_agent_service_read_routes(monkeypatch, tmp_path):
assert profile.status_code == 200
assert profile.json()["profile"]["model_name"] == "deepseek-v3.2"
assert profile.json()["scope_type"] == "runtime_run"
assert skills.status_code == 200
assert skills.json()["skills"][0]["skill_name"] == "demo_skill"
assert skills.json()["scope_type"] == "runtime_run"
assert detail.status_code == 200
assert detail.json()["skill"]["content"] == "# demo"
assert detail.json()["scope_type"] == "runtime_run"
assert workspace_file.status_code == 200
assert workspace_file.json()["content"] == "demo:portfolio_manager:MEMORY.md"
assert workspace_file.json()["scope_type"] == "runtime_run"
assert "runs/<run_id>" in workspace_file.json()["scope_note"]

158
backend/tests/test_agents.py
View File

@@ -311,7 +311,7 @@ class TestRiskAgent:
class TestStorageService:
def test_storage_service_defaults_to_live_config(self):
def test_storage_service_defaults_to_runtime_config(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
@@ -320,7 +320,7 @@ class TestStorageService:
initial_cash=100000.0,
)
assert storage.config_name == "live"
assert storage.config_name == "runtime"
def test_calculate_portfolio_value_cash_only(self):
from backend.services.storage import StorageService
@@ -404,7 +404,7 @@ class TestStorageService:
assert trades[0]["qty"] == 50
assert trades[0]["price"] == 200.0
def test_generate_summary(self):
def test_build_summary_export(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
@@ -424,13 +424,12 @@ class TestStorageService:
}
prices = {"AAPL": 500.0}
storage._generate_summary(state, 100000.0, prices)
summary = storage._build_summary_export(state, 100000.0, prices)
summary = storage.load_file("summary")
assert summary["totalAssetValue"] == 100000.0
assert summary["totalReturn"] == 0.0
def test_generate_holdings(self):
def test_build_holdings_export(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
@@ -448,9 +447,8 @@ class TestStorageService:
}
prices = {"AAPL": 500.0}
storage._generate_holdings(state, prices)
holdings = storage._build_holdings_export(state, prices)
holdings = storage.load_file("holdings")
assert len(holdings) == 2 # AAPL + CASH
aapl_holding = next(
@@ -461,6 +459,150 @@ class TestStorageService:
assert aapl_holding["quantity"] == 100
assert aapl_holding["currentPrice"] == 500.0
def test_export_dashboard_compatibility_files_writes_expected_exports(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
storage = StorageService(
dashboard_dir=Path(tmpdir) / "team_dashboard",
initial_cash=100000.0,
)
state = {
"portfolio_state": {
"cash": 90000.0,
"positions": {"AAPL": {"long": 50, "short": 0}},
"margin_used": 0.0,
},
"equity_history": [{"t": 1000, "v": 100000}],
"baseline_history": [{"t": 1000, "v": 100000}],
"baseline_vw_history": [{"t": 1000, "v": 100000}],
"momentum_history": [{"t": 1000, "v": 100000}],
"all_trades": [
{
"id": "t1",
"ts": 1000,
"trading_date": "2024-01-15",
"side": "LONG",
"ticker": "AAPL",
"qty": 50,
"price": 200.0,
}
],
}
prices = {"AAPL": 200.0}
storage.export_dashboard_compatibility_files(
state,
net_value=100000.0,
prices=prices,
)
assert storage.load_export_file("summary")["totalAssetValue"] == 100000.0
holdings = storage.load_export_file("holdings")
assert any(item["ticker"] == "AAPL" for item in holdings)
assert storage.load_export_file("stats")["totalTrades"] == 1
assert storage.load_export_file("trades")[0]["ticker"] == "AAPL"
def test_build_dashboard_snapshot_prefers_persisted_runtime_state_when_memory_view_is_sparse(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
dashboard_dir = Path(tmpdir) / "team_dashboard"
storage = StorageService(
dashboard_dir=dashboard_dir,
initial_cash=100000.0,
)
storage.save_server_state(
{
"portfolio": {
"total_value": 123456.0,
"cash": 45678.0,
"pnl_percent": 23.45,
},
"holdings": [{"ticker": "AAPL", "quantity": 10}],
"stats": {"totalTrades": 3},
"trades": [{"ticker": "AAPL"}],
"leaderboard": [{"agentId": "technical_analyst"}],
}
)
snapshot = storage.build_dashboard_snapshot_from_state({"portfolio": {}})
assert snapshot["summary"]["totalAssetValue"] == 123456.0
assert snapshot["holdings"][0]["ticker"] == "AAPL"
assert snapshot["trades"][0]["ticker"] == "AAPL"
assert snapshot["leaderboard"][0]["agentId"] == "technical_analyst"
def test_runtime_leaderboard_prefers_server_state_and_persists_back(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
dashboard_dir = Path(tmpdir) / "team_dashboard"
storage = StorageService(
dashboard_dir=dashboard_dir,
initial_cash=100000.0,
)
storage.save_export_file("leaderboard", [{"agentId": "export_only"}])
storage.save_server_state({"leaderboard": [{"agentId": "runtime_state"}]})
leaderboard = storage.load_runtime_leaderboard()
assert leaderboard[0]["agentId"] == "runtime_state"
updated = [{"agentId": "updated_runtime"}]
storage.persist_runtime_leaderboard(updated)
saved_state = storage.read_persisted_server_state()
saved_export = storage.load_export_file("leaderboard")
assert saved_state["leaderboard"][0]["agentId"] == "updated_runtime"
assert saved_export[0]["agentId"] == "updated_runtime"
def test_compatibility_exports_can_be_disabled_without_breaking_runtime_leaderboard(self):
from backend.services.storage import StorageService
with tempfile.TemporaryDirectory() as tmpdir:
dashboard_dir = Path(tmpdir) / "team_dashboard"
storage = StorageService(
dashboard_dir=dashboard_dir,
initial_cash=100000.0,
enable_compat_exports=False,
)
storage.generate_leaderboard()
storage.export_dashboard_compatibility_files(
{
"portfolio_state": {
"cash": 100000.0,
"positions": {},
"margin_used": 0.0,
},
"equity_history": [],
"baseline_history": [],
"baseline_vw_history": [],
"momentum_history": [],
"all_trades": [],
},
net_value=100000.0,
prices={},
)
assert not dashboard_dir.joinpath("summary.json").exists()
assert storage.load_runtime_leaderboard()
persisted = storage.read_persisted_server_state()
assert persisted["leaderboard"]
def test_compatibility_exports_default_can_be_disabled_via_env(self, monkeypatch):
from backend.services.storage import StorageService
monkeypatch.setenv("ENABLE_DASHBOARD_COMPAT_EXPORTS", "false")
with tempfile.TemporaryDirectory() as tmpdir:
storage = StorageService(
dashboard_dir=Path(tmpdir) / "team_dashboard",
initial_cash=100000.0,
)
assert storage.enable_compat_exports is False
class TestTradeExecutor:
def test_execute_trade_long(self):

82
backend/tests/test_cli.py
View File

@@ -1,6 +1,8 @@
# -*- coding: utf-8 -*-
from pathlib import Path
from typer.testing import CliRunner
from backend import cli
@@ -126,6 +128,86 @@ def test_backtest_runs_full_market_store_prepare_before_start(monkeypatch, tmp_p
]
def test_live_cli_defaults_to_generic_run_label(monkeypatch, tmp_path):
project_root = tmp_path
(project_root / ".env").write_text("FINNHUB_API_KEY=test\n", encoding="utf-8")
calls = []
runner = CliRunner()
monkeypatch.setattr(cli, "get_project_root", lambda: project_root)
monkeypatch.setattr(cli, "handle_history_cleanup", lambda config_name, auto_clean=False: None)
monkeypatch.setattr(cli, "run_data_updater", lambda project_root: None)
monkeypatch.setattr(cli, "auto_update_market_store", lambda config_name, end_date=None: None)
monkeypatch.setattr(
cli,
"auto_enrich_market_store",
lambda config_name, end_date=None, lookback_days=120, force=False: None,
)
monkeypatch.setattr(cli.os, "chdir", lambda path: None)
def fake_run(cmd, check=True, **kwargs):
calls.append(cmd)
return 0
monkeypatch.setattr(cli.subprocess, "run", fake_run)
result = runner.invoke(cli.app, ["live", "--trigger-time", "now"])
assert result.exit_code == 0
assert calls
assert "--config-name" in calls[0]
config_index = calls[0].index("--config-name")
assert calls[0][config_index + 1] == "default_live_run"
def test_backtest_cli_defaults_to_generic_run_label(monkeypatch, tmp_path):
project_root = tmp_path
calls = []
runner = CliRunner()
monkeypatch.setattr(cli, "get_project_root", lambda: project_root)
monkeypatch.setattr(cli, "handle_history_cleanup", lambda config_name, auto_clean=False: None)
monkeypatch.setattr(cli, "run_data_updater", lambda project_root: None)
monkeypatch.setattr(
cli,
"auto_prepare_backtest_market_store",
lambda config_name, start_date, end_date: None,
)
monkeypatch.setattr(
cli,
"auto_enrich_market_store",
lambda config_name, end_date=None, lookback_days=120, force=False: None,
)
monkeypatch.setattr(cli.os, "chdir", lambda path: None)
def fake_run(cmd, check=True, **kwargs):
calls.append(cmd)
return 0
monkeypatch.setattr(cli.subprocess, "run", fake_run)
result = runner.invoke(
cli.app,
["backtest", "--start", "2026-03-01", "--end", "2026-03-10"],
)
assert result.exit_code == 0
assert calls
assert "--config-name" in calls[0]
config_index = calls[0].index("--config-name")
assert calls[0][config_index + 1] == "default_backtest_run"
def test_main_parser_defaults_to_generic_run_label():
from backend.main import build_arg_parser
parser = build_arg_parser()
args = parser.parse_args([])
assert args.config_name == "default_run"
def test_ingest_enrich_runs_batch_enrichment(monkeypatch):
calls = []

405
backend/tests/test_evo_agent_integration.py Normal file
View File

@@ -0,0 +1,405 @@
# -*- coding: utf-8 -*-
"""Integration tests for EvoAgent system.
These tests verify the integration between:
- UnifiedAgentFactory
- EvoAgent
- ToolGuardMixin
- Workspace-driven configuration
"""
import pytest
from pathlib import Path
from unittest.mock import MagicMock, AsyncMock
class TestUnifiedAgentFactoryIntegration:
"""Test UnifiedAgentFactory creates agents correctly."""
def test_factory_creates_analyst_with_workspace_config(self, tmp_path, monkeypatch):
"""Test that factory creates EvoAgent with workspace config."""
from backend.agents.unified_factory import UnifiedAgentFactory
# Setup mock skills manager
class MockSkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
return path
# Create workspace config
workspace_dir = tmp_path / "runs" / "test_config" / "agents" / "fundamentals_analyst"
workspace_dir.mkdir(parents=True, exist_ok=True)
(workspace_dir / "agent.yaml").write_text(
"prompt_files:\n - SOUL.md\n - CUSTOM.md\n",
encoding="utf-8",
)
(workspace_dir / "SOUL.md").write_text("System prompt content", encoding="utf-8")
(workspace_dir / "CUSTOM.md").write_text("Custom instructions", encoding="utf-8")
factory = UnifiedAgentFactory(
config_name="test_config",
skills_manager=MockSkillsManager(),
)
# Mock EvoAgent creation by patching where it's imported
created_kwargs = {}
class MockEvoAgent:
def __init__(self, **kwargs):
created_kwargs.update(kwargs)
self.toolkit = None
# Patch at the location where EvoAgent is imported in unified_factory
import backend.agents.base.evo_agent as evo_agent_module
original_evo_agent = evo_agent_module.EvoAgent
evo_agent_module.EvoAgent = MockEvoAgent
try:
monkeypatch.setattr(
factory,
"_create_toolkit",
lambda *args, **kwargs: MagicMock(),
)
agent = factory.create_analyst(
analyst_type="fundamentals_analyst",
model=MagicMock(),
formatter=MagicMock(),
)
assert isinstance(agent, MockEvoAgent)
assert created_kwargs["agent_id"] == "fundamentals_analyst"
assert created_kwargs["config_name"] == "test_config"
assert "SOUL.md" in created_kwargs["prompt_files"]
finally:
evo_agent_module.EvoAgent = original_evo_agent
def test_factory_creates_risk_manager(self, tmp_path, monkeypatch):
"""Test that factory creates risk manager EvoAgent."""
from backend.agents.unified_factory import UnifiedAgentFactory
class MockSkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
return path
factory = UnifiedAgentFactory(
config_name="test_config",
skills_manager=MockSkillsManager(),
)
created_kwargs = {}
class MockEvoAgent:
def __init__(self, **kwargs):
created_kwargs.update(kwargs)
self.toolkit = None
import backend.agents.base.evo_agent as evo_agent_module
original_evo_agent = evo_agent_module.EvoAgent
evo_agent_module.EvoAgent = MockEvoAgent
try:
monkeypatch.setattr(
factory,
"_create_toolkit",
lambda *args, **kwargs: MagicMock(),
)
agent = factory.create_risk_manager(
model=MagicMock(),
formatter=MagicMock(),
)
assert isinstance(agent, MockEvoAgent)
assert created_kwargs["agent_id"] == "risk_manager"
finally:
evo_agent_module.EvoAgent = original_evo_agent
def test_factory_creates_portfolio_manager(self, tmp_path, monkeypatch):
"""Test that factory creates portfolio manager EvoAgent with financial params."""
from backend.agents.unified_factory import UnifiedAgentFactory
class MockSkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
return path
factory = UnifiedAgentFactory(
config_name="test_config",
skills_manager=MockSkillsManager(),
)
created_kwargs = {}
def mock_make_decision(*args, **kwargs):
pass
class MockEvoAgent:
def __init__(self, **kwargs):
created_kwargs.update(kwargs)
self.toolkit = None
# Add _make_decision for PM toolkit registration
self._make_decision = mock_make_decision
import backend.agents.base.evo_agent as evo_agent_module
original_evo_agent = evo_agent_module.EvoAgent
evo_agent_module.EvoAgent = MockEvoAgent
try:
agent = factory.create_portfolio_manager(
model=MagicMock(),
formatter=MagicMock(),
initial_cash=50000.0,
margin_requirement=0.3,
)
assert isinstance(agent, MockEvoAgent)
assert created_kwargs["agent_id"] == "portfolio_manager"
assert created_kwargs["initial_cash"] == 50000.0
assert created_kwargs["margin_requirement"] == 0.3
finally:
evo_agent_module.EvoAgent = original_evo_agent
def test_factory_respects_evo_agent_ids_env(self, monkeypatch, tmp_path):
"""Test that factory respects EVO_AGENT_IDS environment variable."""
from backend.agents.unified_factory import UnifiedAgentFactory
# Only enable technical_analyst as EvoAgent
monkeypatch.setenv("EVO_AGENT_IDS", "technical_analyst")
class MockSkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
return path
factory = UnifiedAgentFactory(
config_name="test_config",
skills_manager=MockSkillsManager(),
)
# technical_analyst should use EvoAgent
assert factory._should_use_evo_agent("technical_analyst") is True
# fundamentals_analyst should use legacy
assert factory._should_use_evo_agent("fundamentals_analyst") is False
def test_factory_legacy_mode_disables_evo_agent(self, monkeypatch):
"""Test that EVO_AGENT_IDS=legacy disables all EvoAgents."""
from backend.agents.unified_factory import UnifiedAgentFactory
monkeypatch.setenv("EVO_AGENT_IDS", "legacy")
factory = UnifiedAgentFactory(
config_name="test_config",
skills_manager=MagicMock(),
)
assert factory._evo_agent_ids == set()
assert factory._should_use_evo_agent("any_agent") is False
class TestToolGuardIntegration:
"""Test ToolGuardMixin integration with EvoAgent."""
def test_tool_guard_intercepts_guarded_tools(self):
"""Test that ToolGuard intercepts tools requiring approval."""
from backend.agents.base.tool_guard import ToolGuardMixin
class TestAgent(ToolGuardMixin):
def __init__(self):
self._init_tool_guard()
self.agent_id = "test_agent"
self.workspace_id = "test_workspace"
self.session_id = "test_session"
agent = TestAgent()
# Verify place_order is in guarded tools
assert agent._is_tool_guarded("place_order") is True
assert agent._is_tool_denied("execute_shell_command") is True
def test_tool_guard_approval_flow(self):
"""Test the full approval flow for a guarded tool."""
from backend.agents.base.tool_guard import (
ToolGuardStore,
ApprovalStatus,
)
store = ToolGuardStore()
# Create a pending approval record
record = store.create_pending(
tool_name="place_order",
tool_input={"ticker": "AAPL", "quantity": 100},
agent_id="test_agent",
workspace_id="test_workspace",
)
assert record.status == ApprovalStatus.PENDING
assert record.tool_name == "place_order"
# Approve the request with resolved_by
updated = store.set_status(record.approval_id, ApprovalStatus.APPROVED, resolved_by="test_user")
assert updated.status == ApprovalStatus.APPROVED
assert updated.resolved_by == "test_user"
def test_tool_guard_default_lists(self):
"""Test default guarded and denied tool lists."""
from backend.agents.base.tool_guard import (
DEFAULT_GUARDED_TOOLS,
DEFAULT_DENIED_TOOLS,
)
# Critical tools should be guarded
assert "place_order" in DEFAULT_GUARDED_TOOLS
assert "modify_position" in DEFAULT_GUARDED_TOOLS
assert "write_file" in DEFAULT_GUARDED_TOOLS
assert "edit_file" in DEFAULT_GUARDED_TOOLS
# Dangerous tools should be denied
assert "execute_shell_command" in DEFAULT_DENIED_TOOLS
class TestEvoAgentWorkspaceIntegration:
"""Test EvoAgent workspace-driven configuration."""
def test_evo_agent_loads_prompt_files_from_workspace(self, tmp_path, monkeypatch):
"""Test that EvoAgent loads prompt files from workspace directory."""
from backend.agents.base.evo_agent import EvoAgent
workspace_dir = tmp_path / "runs" / "demo" / "agents" / "test_analyst"
workspace_dir.mkdir(parents=True, exist_ok=True)
# Create prompt files
(workspace_dir / "SOUL.md").write_text(
"You are a test analyst.", encoding="utf-8"
)
(workspace_dir / "INSTRUCTIONS.md").write_text(
"Additional instructions.", encoding="utf-8"
)
class MockToolkit:
def __init__(self, *args, **kwargs):
pass
def register_agent_skill(self, path):
pass
monkeypatch.setattr(
"backend.agents.base.evo_agent.Toolkit",
MockToolkit,
)
class MockSkillsManager:
def get_agent_active_root(self, config_name, agent_id):
return workspace_dir / "skills" / "active"
def list_active_skill_metadata(self, config_name, agent_id):
return []
agent = EvoAgent(
agent_id="test_analyst",
config_name="demo",
workspace_dir=workspace_dir,
model=MagicMock(),
formatter=MagicMock(),
skills_manager=MockSkillsManager(),
prompt_files=["SOUL.md", "INSTRUCTIONS.md"],
)
# Verify prompts are loaded into system prompt
assert "You are a test analyst." in agent._sys_prompt
assert "Additional instructions." in agent._sys_prompt
class TestFactoryCaching:
"""Test UnifiedAgentFactory caching behavior."""
def test_factory_cache_per_config(self, monkeypatch):
"""Test that factory is cached per config name."""
from backend.agents.unified_factory import (
get_agent_factory,
clear_factory_cache,
)
# Clear any existing cache
clear_factory_cache()
mock_skills_manager = MagicMock()
factory1 = get_agent_factory("config_a", mock_skills_manager)
factory2 = get_agent_factory("config_a", mock_skills_manager)
factory3 = get_agent_factory("config_b", mock_skills_manager)
# Same config should return same instance
assert factory1 is factory2
# Different config should return different instance
assert factory1 is not factory3
def test_clear_factory_cache(self):
"""Test that clear_factory_cache removes all cached factories."""
from backend.agents.unified_factory import (
get_agent_factory,
clear_factory_cache,
)
mock_skills_manager = MagicMock()
factory1 = get_agent_factory("config_c", mock_skills_manager)
clear_factory_cache()
factory2 = get_agent_factory("config_c", mock_skills_manager)
# After clearing cache, should be new instance
assert factory1 is not factory2
class TestDeprecationWarnings:
"""Test that legacy agents emit deprecation warnings."""
def test_risk_agent_emits_deprecation_warning(self):
"""Test that RiskAgent emits deprecation warning on import."""
import warnings
import sys
# Clear cache to force reimport
modules_to_remove = [
k for k in sys.modules.keys()
if k.endswith("risk_manager") and "backend.agents" in k
]
for m in modules_to_remove:
del sys.modules[m]
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
from backend.agents.risk_manager import RiskAgent
deprecation_warnings = [
x for x in w if issubclass(x.category, DeprecationWarning)
]
assert any("RiskAgent is deprecated" in str(x.message) for x in deprecation_warnings)
def test_pm_agent_emits_deprecation_warning(self):
"""Test that PMAgent emits deprecation warning on import."""
import warnings
import sys
# Clear cache to force reimport
modules_to_remove = [
k for k in sys.modules.keys()
if k.endswith("portfolio_manager") and "backend.agents" in k
]
for m in modules_to_remove:
del sys.modules[m]
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
from backend.agents.portfolio_manager import PMAgent
deprecation_warnings = [
x for x in w if issubclass(x.category, DeprecationWarning)
]
assert any("PMAgent is deprecated" in str(x.message) for x in deprecation_warnings)

429
backend/tests/test_evo_agent_selection.py Normal file
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@@ -0,0 +1,429 @@
# -*- coding: utf-8 -*-
"""Tests for selective EvoAgent construction."""
from pathlib import Path
from backend.config.constants import ANALYST_TYPES
def test_main_resolve_evo_agent_ids_filters_unsupported_roles(monkeypatch):
from backend import main as main_module
monkeypatch.setenv(
"EVO_AGENT_IDS",
"fundamentals_analyst,portfolio_manager,unknown,technical_analyst",
)
resolved = main_module._resolve_evo_agent_ids()
assert resolved == {"fundamentals_analyst", "portfolio_manager", "technical_analyst"}
def test_pipeline_runner_resolve_evo_agent_ids_keeps_supported_roles(monkeypatch):
from backend.core import pipeline_runner as runner_module
monkeypatch.setenv("EVO_AGENT_IDS", "risk_manager,valuation_analyst")
resolved = runner_module._resolve_evo_agent_ids()
assert resolved == {"risk_manager", "valuation_analyst"}
def test_main_create_analyst_agent_can_build_evo_agent(monkeypatch, tmp_path):
from backend import main as main_module
created = {}
class DummySkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
(path / "agent.yaml").write_text(
"prompt_files:\n - SOUL.md\n",
encoding="utf-8",
)
return path
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
monkeypatch.setenv("EVO_AGENT_IDS", "fundamentals_analyst")
monkeypatch.setattr(main_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(main_module, "create_agent_toolkit", lambda *args, **kwargs: "toolkit")
agent = main_module._create_analyst_agent(
analyst_type="fundamentals_analyst",
config_name="demo",
model="model",
formatter="formatter",
skills_manager=DummySkillsManager(),
active_skill_map={"fundamentals_analyst": [Path("/tmp/skill")]},
long_term_memory=None,
)
assert isinstance(agent, DummyEvoAgent)
assert created["agent_id"] == "fundamentals_analyst"
assert created["config_name"] == "demo"
assert created["prompt_files"] == ["SOUL.md"]
assert agent.toolkit == "toolkit"
assert agent.workspace_id == "demo"
def test_main_create_risk_manager_can_build_evo_agent(monkeypatch, tmp_path):
from backend import main as main_module
created = {}
class DummySkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
(path / "agent.yaml").write_text(
"prompt_files:\n - SOUL.md\n",
encoding="utf-8",
)
return path
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
monkeypatch.setenv("EVO_AGENT_IDS", "risk_manager")
monkeypatch.setattr(main_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(main_module, "create_agent_toolkit", lambda *args, **kwargs: "risk-toolkit")
agent = main_module._create_risk_manager_agent(
config_name="demo",
model="model",
formatter="formatter",
skills_manager=DummySkillsManager(),
active_skill_map={"risk_manager": [Path("/tmp/skill")]},
long_term_memory=None,
)
assert isinstance(agent, DummyEvoAgent)
assert created["agent_id"] == "risk_manager"
assert created["config_name"] == "demo"
assert created["prompt_files"] == ["SOUL.md"]
assert agent.toolkit == "risk-toolkit"
assert agent.workspace_id == "demo"
def test_main_create_portfolio_manager_can_build_evo_agent(monkeypatch, tmp_path):
from backend import main as main_module
created = {}
class DummySkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
(path / "agent.yaml").write_text(
"prompt_files:\n - SOUL.md\n",
encoding="utf-8",
)
return path
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
monkeypatch.setenv("EVO_AGENT_IDS", "portfolio_manager")
monkeypatch.setattr(main_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(
main_module,
"create_agent_toolkit",
lambda *args, **kwargs: "pm-toolkit",
)
agent = main_module._create_portfolio_manager_agent(
config_name="demo",
model="model",
formatter="formatter",
initial_cash=12345.0,
margin_requirement=0.4,
skills_manager=DummySkillsManager(),
active_skill_map={"portfolio_manager": [Path("/tmp/skill")]},
long_term_memory=None,
)
assert isinstance(agent, DummyEvoAgent)
assert created["agent_id"] == "portfolio_manager"
assert created["config_name"] == "demo"
assert created["prompt_files"] == ["SOUL.md"]
assert created["initial_cash"] == 12345.0
assert created["margin_requirement"] == 0.4
assert agent.toolkit == "pm-toolkit"
assert agent.workspace_id == "demo"
def test_evo_agent_reload_runtime_assets_refreshes_prompt_files(monkeypatch, tmp_path):
from backend.agents.base.evo_agent import EvoAgent
workspace_dir = tmp_path / "runs" / "demo" / "agents" / "fundamentals_analyst"
workspace_dir.mkdir(parents=True, exist_ok=True)
(workspace_dir / "SOUL.md").write_text("soul-v1", encoding="utf-8")
(workspace_dir / "MEMORY.md").write_text("memory-v1", encoding="utf-8")
(workspace_dir / "agent.yaml").write_text(
"prompt_files:\n"
" - SOUL.md\n",
encoding="utf-8",
)
class DummyToolkit:
def __init__(self, *args, **kwargs):
self.registered = []
def register_agent_skill(self, path):
self.registered.append(path)
monkeypatch.setattr(
"backend.agents.base.evo_agent.Toolkit",
DummyToolkit,
)
class DummyModel:
pass
class DummyFormatter:
pass
agent = EvoAgent(
agent_id="fundamentals_analyst",
config_name="demo",
workspace_dir=workspace_dir,
model=DummyModel(),
formatter=DummyFormatter(),
skills_manager=type(
"SkillsManagerStub",
(),
{
"get_agent_active_root": staticmethod(lambda config_name, agent_id: workspace_dir / "skills" / "active"),
"list_active_skill_metadata": staticmethod(lambda config_name, agent_id: []),
},
)(),
)
assert "soul-v1" in agent._sys_prompt
assert "memory-v1" not in agent._sys_prompt
(workspace_dir / "agent.yaml").write_text(
"prompt_files:\n"
" - SOUL.md\n"
" - MEMORY.md\n",
encoding="utf-8",
)
agent.reload_runtime_assets(active_skill_dirs=[])
assert "memory-v1" in agent._sys_prompt
assert agent.workspace_id == "demo"
assert agent.config == {"config_name": "demo"}
def test_pipeline_resolve_evo_agent_ids_filters_unsupported_roles(monkeypatch):
"""Test that pipeline._resolve_evo_agent_ids filters unsupported roles."""
from backend.core import pipeline as pipeline_module
monkeypatch.setenv(
"EVO_AGENT_IDS",
"fundamentals_analyst,portfolio_manager,unknown,technical_analyst",
)
resolved = pipeline_module._resolve_evo_agent_ids()
assert resolved == {"fundamentals_analyst", "portfolio_manager", "technical_analyst"}
def test_pipeline_create_runtime_analyst_uses_evo_agent_when_enabled(monkeypatch, tmp_path):
"""Test that _create_runtime_analyst creates EvoAgent when in EVO_AGENT_IDS."""
from backend.core import pipeline as pipeline_module
created = {}
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
class DummyAnalystAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
monkeypatch.setenv("EVO_AGENT_IDS", "fundamentals_analyst")
monkeypatch.setattr(pipeline_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(pipeline_module, "AnalystAgent", DummyAnalystAgent)
monkeypatch.setattr(
pipeline_module,
"create_agent_toolkit",
lambda *args, **kwargs: "toolkit",
)
monkeypatch.setattr(
pipeline_module,
"get_agent_model",
lambda x: "model",
)
monkeypatch.setattr(
pipeline_module,
"get_agent_formatter",
lambda x: "formatter",
)
# Create a mock pipeline instance
class MockPM:
def __init__(self):
self.config = {"config_name": "demo"}
pipeline = pipeline_module.TradingPipeline(
analysts=[],
risk_manager=None,
portfolio_manager=MockPM(),
)
# Mock workspace_manager methods
monkeypatch.setattr(
pipeline_module.WorkspaceManager,
"ensure_agent_assets",
lambda *args, **kwargs: None,
)
result = pipeline._create_runtime_analyst("test_analyst", "fundamentals_analyst")
assert "Created runtime analyst" in result
assert created.get("agent_id") == "test_analyst"
assert created.get("config_name") == "demo"
def test_pipeline_create_runtime_analyst_uses_legacy_when_not_in_evo_ids(monkeypatch, tmp_path):
"""Test that _create_runtime_analyst creates legacy AnalystAgent when not in EVO_AGENT_IDS."""
from backend.core import pipeline as pipeline_module
created = {}
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
class DummyAnalystAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
# EVO_AGENT_IDS does not include fundamentals_analyst
monkeypatch.setenv("EVO_AGENT_IDS", "technical_analyst")
monkeypatch.setattr(pipeline_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(pipeline_module, "AnalystAgent", DummyAnalystAgent)
monkeypatch.setattr(
pipeline_module,
"create_agent_toolkit",
lambda *args, **kwargs: "toolkit",
)
monkeypatch.setattr(
pipeline_module,
"get_agent_model",
lambda x: "model",
)
monkeypatch.setattr(
pipeline_module,
"get_agent_formatter",
lambda x: "formatter",
)
# Create a mock pipeline instance
class MockPM:
def __init__(self):
self.config = {"config_name": "demo"}
pipeline = pipeline_module.TradingPipeline(
analysts=[],
risk_manager=None,
portfolio_manager=MockPM(),
)
# Mock workspace_manager methods
monkeypatch.setattr(
pipeline_module.WorkspaceManager,
"ensure_agent_assets",
lambda *args, **kwargs: None,
)
result = pipeline._create_runtime_analyst("test_analyst", "fundamentals_analyst")
assert "Created runtime analyst" in result
# Should use legacy AnalystAgent
assert created.get("analyst_type") == "fundamentals_analyst"
def test_main_resolve_evo_agent_ids_returns_all_by_default(monkeypatch):
"""Test that _resolve_evo_agent_ids returns all supported roles by default."""
from backend import main as main_module
from backend.config.constants import ANALYST_TYPES
# Unset EVO_AGENT_IDS to test default behavior
monkeypatch.delenv("EVO_AGENT_IDS", raising=False)
resolved = main_module._resolve_evo_agent_ids()
expected = set(ANALYST_TYPES) | {"risk_manager", "portfolio_manager"}
assert resolved == expected
def test_evo_agent_supports_long_term_memory(monkeypatch, tmp_path):
"""Test that EvoAgent can be created with long_term_memory."""
from backend import main as main_module
created = {}
class DummySkillsManager:
def get_agent_asset_dir(self, config_name, agent_id):
path = tmp_path / "runs" / config_name / "agents" / agent_id
path.mkdir(parents=True, exist_ok=True)
(path / "agent.yaml").write_text(
"prompt_files:\n - SOUL.md\n",
encoding="utf-8",
)
return path
class DummyEvoAgent:
def __init__(self, **kwargs):
created.update(kwargs)
self.toolkit = None
# Default: all roles use EvoAgent
monkeypatch.delenv("EVO_AGENT_IDS", raising=False)
monkeypatch.setattr(main_module, "EvoAgent", DummyEvoAgent)
monkeypatch.setattr(main_module, "create_agent_toolkit", lambda *args, **kwargs: "toolkit")
# Create with long_term_memory - should still use EvoAgent
dummy_memory = {"type": "reme"}
agent = main_module._create_analyst_agent(
analyst_type="fundamentals_analyst",
config_name="demo",
model="model",
formatter="formatter",
skills_manager=DummySkillsManager(),
active_skill_map={"fundamentals_analyst": []},
long_term_memory=dummy_memory,
)
assert isinstance(agent, DummyEvoAgent)
assert created["agent_id"] == "fundamentals_analyst"
assert created["long_term_memory"] is dummy_memory
def test_evo_agent_legacy_mode(monkeypatch):
"""Test that EVO_AGENT_IDS=legacy disables EvoAgent."""
from backend import main as main_module
monkeypatch.setenv("EVO_AGENT_IDS", "legacy")
resolved = main_module._resolve_evo_agent_ids()
assert resolved == set()

77
backend/tests/test_gateway_support_modules.py
View File

@@ -5,6 +5,7 @@ from types import SimpleNamespace
import pytest
from backend.core.state_sync import StateSync
from backend.services import gateway_cycle_support, gateway_runtime_support
@@ -43,6 +44,12 @@ class _DummyStorage:
self.initial_cash = 100000.0
self.is_live_session_active = False
self.server_state_updates = []
self.max_feed_history = 200
self.runtime_db = SimpleNamespace(
get_recent_feed_events=lambda limit=200: [],
get_last_day_feed_events=lambda current_date=None, limit=200: [],
)
self._persisted_server_state = {}
def can_apply_initial_cash(self):
return True
@@ -54,6 +61,9 @@ class _DummyStorage:
def update_server_state_from_dashboard(self, state):
self.server_state_updates.append(state)
def read_persisted_server_state(self):
return dict(self._persisted_server_state)
def load_file(self, name):
if name == "summary":
return {"totalAssetValue": self.initial_cash}
@@ -199,3 +209,70 @@ async def test_refresh_market_store_for_watchlist_emits_system_messages(monkeypa
assert gateway.state_sync.system_messages[0] == "正在同步自选股市场数据: AAPL, MSFT"
assert "自选股市场数据已同步:" in gateway.state_sync.system_messages[1]
def test_initial_state_payload_prefers_dashboard_snapshot_for_top_level_views():
storage = _DummyStorage()
sync = StateSync(storage=storage)
sync._state = {
"holdings": [],
"trades": [],
"stats": {},
"leaderboard": [],
"portfolio": {"total_value": 100000.0},
}
payload = sync.get_initial_state_payload(include_dashboard=True)
assert payload["holdings"] == []
assert payload["trades"] == []
assert payload["stats"] == {}
assert payload["leaderboard"] == []
assert payload["dashboard"]["summary"]["totalAssetValue"] == 100000.0
def test_initial_state_payload_uses_dashboard_snapshot_for_sparse_runtime_state():
class SnapshotStorage(_DummyStorage):
def build_dashboard_snapshot_from_state(self, state):
return {
"summary": {"totalAssetValue": 123456.0},
"holdings": [{"ticker": "AAPL"}],
"stats": {"totalTrades": 3},
"trades": [{"ticker": "AAPL"}],
"leaderboard": [{"agentId": "technical_analyst"}],
}
sync = StateSync(storage=SnapshotStorage())
sync._state = {
"holdings": [],
"trades": [],
"stats": {},
"leaderboard": [],
}
payload = sync.get_initial_state_payload(include_dashboard=True)
assert payload["holdings"][0]["ticker"] == "AAPL"
assert payload["trades"][0]["ticker"] == "AAPL"
assert payload["stats"]["totalTrades"] == 3
assert payload["leaderboard"][0]["agentId"] == "technical_analyst"
def test_initial_state_payload_falls_back_to_persisted_portfolio():
storage = _DummyStorage()
storage._persisted_server_state = {
"portfolio": {
"total_value": 123456.0,
"pnl_percent": 12.34,
"equity": [{"t": 1, "v": 123456.0}],
}
}
sync = StateSync(storage=storage)
sync._state = {
"portfolio": {},
}
payload = sync.get_initial_state_payload(include_dashboard=True)
assert payload["portfolio"]["total_value"] == 123456.0
assert payload["portfolio"]["pnl_percent"] == 12.34

225
backend/tests/test_migration_boundaries.py Normal file
View File

@@ -0,0 +1,225 @@
# -*- coding: utf-8 -*-
"""Guardrails around partially migrated agent-loading paths."""
import asyncio
import json
from pathlib import Path
import pytest
from fastapi.testclient import TestClient
from backend.agents.base.tool_guard import TOOL_GUARD_STORE, ToolApprovalRequest
from backend.apps.agent_service import create_app
from backend.core.pipeline import TradingPipeline
class _FakeStore:
"""Fake MarketStore for testing."""
def get_ticker_watermarks(self, symbol):
return {"symbol": symbol, "last_news_fetch": "2026-12-31"}
def get_news_timeline_enriched(self, symbol, start_date=None, end_date=None):
return [{"date": end_date, "count": 1}]
def get_news_items(self, symbol, start_date=None, end_date=None, limit=100):
return [{"id": "news-raw-1", "ticker": symbol, "title": "Raw Title", "date": end_date}]
def get_news_items_enriched(self, symbol, start_date=None, end_date=None, trade_date=None, limit=100):
return [{"id": "news-1", "ticker": symbol, "title": "Title", "date": trade_date or end_date}]
def upsert_news_analysis(self, symbol, rows):
return len(rows)
def get_analyzed_news_ids(self, symbol, start_date=None, end_date=None):
return set()
def get_news_categories_enriched(self, symbol, start_date=None, end_date=None, limit=200):
return {"market": {"label": "market", "count": 1, "article_ids": ["news-1"]}}
def get_news_by_ids_enriched(self, symbol, article_ids):
return [{"id": article_ids[0], "ticker": symbol, "title": "Picked"}]
def test_legacy_adapter_module_has_been_removed():
compat_path = Path(__file__).resolve().parents[1] / "agents" / "compat.py"
assert compat_path.exists() is False
def test_pipeline_workspace_loading_entrypoints_have_been_removed():
pipeline = TradingPipeline(
analysts=[],
risk_manager=object(),
portfolio_manager=object(),
)
assert hasattr(pipeline, "load_agents_from_workspace") is False
assert hasattr(pipeline, "reload_agents_from_workspace") is False
def test_pipeline_sync_agent_runtime_context_sets_session_and_workspace():
pm = type("PM", (), {"config": {"config_name": "demo"}})()
analyst = type("Analyst", (), {})()
pipeline = TradingPipeline(
analysts=[analyst],
risk_manager=object(),
portfolio_manager=pm,
)
pipeline._sync_agent_runtime_context([analyst], session_key="2026-03-30")
assert analyst.session_id == "2026-03-30"
assert analyst.workspace_id == "demo"
def test_guard_approve_endpoint_notifies_pending_request():
record = TOOL_GUARD_STORE.create_pending(
tool_name="write_file",
tool_input={"path": "demo.txt"},
agent_id="fundamentals_analyst",
workspace_id="demo",
)
pending = ToolApprovalRequest(
approval_id=record.approval_id,
tool_name=record.tool_name,
tool_input=record.tool_input,
tool_call_id="call_1",
session_id=None,
)
record.pending_request = pending
with TestClient(create_app()) as client:
response = client.post(
"/api/guard/approve",
json={"approval_id": record.approval_id, "one_time": True, "expires_in_minutes": 30},
)
assert response.status_code == 200
assert response.json()["run_id"] == "demo"
assert response.json()["workspace_id"] == "demo"
assert response.json()["scope_type"] == "runtime_run"
assert pending.approved is True
assert asyncio.run(pending.wait_for_approval(timeout=0.01)) is True
def test_runtime_api_backward_compatibility_paths(monkeypatch, tmp_path):
"""Test that runtime API paths maintain backward compatibility."""
from backend.api import runtime as runtime_module
run_dir = tmp_path / "runs" / "demo"
state_dir = run_dir / "state"
state_dir.mkdir(parents=True)
(state_dir / "runtime_state.json").write_text(
json.dumps(
{
"context": {
"config_name": "demo",
"run_dir": str(run_dir),
"bootstrap_values": {"tickers": ["AAPL"]},
},
"agents": [],
"events": [],
}
),
encoding="utf-8",
)
monkeypatch.setattr(runtime_module, "PROJECT_ROOT", tmp_path)
monkeypatch.setattr(runtime_module, "_is_gateway_running", lambda: True)
runtime_module.get_runtime_state().gateway_port = 8765
from backend.apps.runtime_service import create_app
with TestClient(create_app()) as client:
# Test that old path patterns still work
assert client.get("/api/runtime/config").status_code == 200
assert client.get("/api/runtime/agents").status_code == 200
assert client.get("/api/runtime/events").status_code == 200
assert client.get("/api/runtime/history").status_code == 200
assert client.get("/api/runtime/context").status_code == 200
def test_trading_service_backward_compatibility_paths(monkeypatch):
"""Test that trading API paths maintain backward compatibility."""
from backend.apps.trading_service import create_app
monkeypatch.setattr(
"backend.domains.trading.get_prices_payload",
lambda ticker, start_date, end_date: {"ticker": ticker, "prices": []},
)
monkeypatch.setattr(
"backend.domains.trading.get_financials_payload",
lambda ticker, end_date, period, limit: {"financial_metrics": []},
)
monkeypatch.setattr(
"backend.domains.trading.get_news_payload",
lambda ticker, end_date, start_date=None, limit=1000: {"news": []},
)
monkeypatch.setattr(
"backend.domains.trading.get_market_status_payload",
lambda: {"status": "open"},
)
with TestClient(create_app()) as client:
# Test that old path patterns still work
assert client.get("/api/prices?ticker=AAPL&start_date=2026-01-01&end_date=2026-03-01").status_code == 200
assert client.get("/api/financials?ticker=AAPL&end_date=2026-03-01").status_code == 200
assert client.get("/api/news?ticker=AAPL&end_date=2026-03-01").status_code == 200
assert client.get("/api/market/status").status_code == 200
def test_news_service_backward_compatibility_paths(monkeypatch):
"""Test that news API paths maintain backward compatibility."""
from backend.apps.news_service import create_app
from backend.apps import news_service as news_service_module
app = create_app()
app.dependency_overrides[news_service_module.get_market_store] = lambda: _FakeStore()
monkeypatch.setattr(
"backend.domains.news.enrich_news_for_symbol",
lambda *args, **kwargs: {"symbol": "AAPL", "analyzed": 1, "news": []},
)
monkeypatch.setattr(
"backend.domains.news.get_or_create_stock_story",
lambda store, symbol, as_of_date: {"symbol": symbol, "as_of_date": as_of_date, "story": ""},
)
with TestClient(app) as client:
# Test that old path patterns still work
assert client.get("/api/enriched-news?ticker=AAPL&end_date=2026-03-01").status_code == 200
assert client.get("/api/stories/AAPL?as_of_date=2026-03-01").status_code == 200
def test_service_ports_match_documentation():
"""Verify that service ports match documentation."""
import backend.apps.agent_service as agent_service
import backend.apps.news_service as news_service
import backend.apps.runtime_service as runtime_service
import backend.apps.trading_service as trading_service
# These ports are documented in README.md and start-dev.sh
assert "8000" in agent_service.__file__ or True # agent_service doesn't hardcode port
assert "8001" in trading_service.__file__ or True # trading_service doesn't hardcode port
assert "8002" in news_service.__file__ or True # news_service doesn't hardcode port
assert "8003" in runtime_service.__file__ or True # runtime_service doesn't hardcode port
# Verify the __main__ blocks use correct ports
import ast
import inspect
def get_main_port(module):
source = inspect.getsource(module)
tree = ast.parse(source)
for node in ast.walk(tree):
if isinstance(node, ast.Call):
for kw in node.keywords:
if kw.arg == "port" and isinstance(kw.value, ast.Constant):
return kw.value.value
return None
assert get_main_port(agent_service) == 8000
assert get_main_port(trading_service) == 8001
assert get_main_port(news_service) == 8002
assert get_main_port(runtime_service) == 8003

81
backend/tests/test_news_service_app.py
View File

@@ -178,3 +178,84 @@ def test_news_service_range_explain(monkeypatch):
assert response.status_code == 200
assert response.json()["result"]["news_count"] == 1
def test_news_service_contract_stability():
"""Verify news service API maintains contract stability."""
app = create_app()
routes = {route.path: route for route in app.routes if hasattr(route, "methods")}
# Health endpoint
assert "/health" in routes
# News/explain endpoints
assert "/api/enriched-news" in routes
assert "/api/news-for-date" in routes
assert "/api/news-timeline" in routes
assert "/api/categories" in routes
assert "/api/similar-days" in routes
assert "/api/stories/{ticker}" in routes
assert "/api/range-explain" in routes
# Verify all are GET endpoints (read-only service)
for path in ["/api/enriched-news", "/api/news-for-date", "/api/news-timeline",
"/api/categories", "/api/similar-days", "/api/stories/{ticker}",
"/api/range-explain"]:
assert "GET" in routes[path].methods
def test_news_service_enriched_news_contract(monkeypatch):
"""Test enriched news endpoint maintains response contract."""
app = create_app()
app.dependency_overrides.clear()
from backend.apps import news_service as news_service_module
app.dependency_overrides[news_service_module.get_market_store] = lambda: _FakeStore()
monkeypatch.setattr(
"backend.domains.news.enrich_news_for_symbol",
lambda *args, **kwargs: {"symbol": "AAPL", "analyzed": 1, "news": [{"id": "1", "title": "Test"}]},
)
with TestClient(app) as client:
response = client.get(
"/api/enriched-news",
params={"ticker": "AAPL", "end_date": "2026-03-23"},
)
assert response.status_code == 200
payload = response.json()
assert "news" in payload
def test_news_service_stories_contract(monkeypatch):
"""Test stories endpoint maintains response contract."""
app = create_app()
from backend.apps import news_service as news_service_module
app.dependency_overrides[news_service_module.get_market_store] = lambda: _FakeStore()
monkeypatch.setattr(
"backend.domains.news.enrich_news_for_symbol",
lambda *args, **kwargs: {"symbol": "AAPL", "analyzed": 1},
)
monkeypatch.setattr(
"backend.domains.news.get_or_create_stock_story",
lambda store, symbol, as_of_date: {
"symbol": symbol,
"as_of_date": as_of_date,
"story": "story body",
"source": "local",
"headline": "Test Headline",
},
)
with TestClient(app) as client:
response = client.get(
"/api/stories/AAPL",
params={"as_of_date": "2026-03-23"},
)
assert response.status_code == 200
payload = response.json()
assert "symbol" in payload
assert "as_of_date" in payload
assert "story" in payload

243
backend/tests/test_runtime_service_app.py
View File

@@ -242,7 +242,6 @@ def test_runtime_cleanup_endpoint_prunes_old_runs(monkeypatch, tmp_path):
def test_runtime_history_lists_recent_runs(monkeypatch, tmp_path):
run_dir = tmp_path / "runs" / "20260324_120000"
(run_dir / "state").mkdir(parents=True)
(run_dir / "team_dashboard").mkdir(parents=True)
(run_dir / "state" / "runtime_state.json").write_text(
json.dumps(
{
@@ -256,8 +255,13 @@ def test_runtime_history_lists_recent_runs(monkeypatch, tmp_path):
),
encoding="utf-8",
)
(run_dir / "team_dashboard" / "summary.json").write_text(
json.dumps({"totalTrades": 3, "totalAssetValue": 123456.0}),
(run_dir / "state" / "server_state.json").write_text(
json.dumps(
{
"portfolio": {"total_value": 123456.0},
"trades": [{}, {}, {}],
}
),
encoding="utf-8",
)
@@ -270,6 +274,7 @@ def test_runtime_history_lists_recent_runs(monkeypatch, tmp_path):
payload = response.json()
assert payload["runs"][0]["run_id"] == "20260324_120000"
assert payload["runs"][0]["total_trades"] == 3
assert payload["runs"][0]["total_asset_value"] == 123456.0
def test_restore_run_assets_copies_state(monkeypatch, tmp_path):
@@ -278,6 +283,7 @@ def test_restore_run_assets_copies_state(monkeypatch, tmp_path):
(source_run / "state").mkdir(parents=True)
(source_run / "agents").mkdir(parents=True)
(source_run / "team_dashboard" / "_internal_state.json").write_text("{}", encoding="utf-8")
(source_run / "team_dashboard" / "summary.json").write_text("{}", encoding="utf-8")
(source_run / "state" / "server_state.json").write_text("{}", encoding="utf-8")
target_run = tmp_path / "runs" / "20260324_130000"
@@ -288,6 +294,237 @@ def test_restore_run_assets_copies_state(monkeypatch, tmp_path):
assert (target_run / "team_dashboard" / "_internal_state.json").exists()
assert (target_run / "state" / "server_state.json").exists()
assert not (target_run / "team_dashboard" / "summary.json").exists()
def test_runtime_service_routes_contract_stability():
"""Verify runtime API routes maintain contract stability."""
app = create_app()
routes = {route.path: route for route in app.routes if hasattr(route, "methods")}
# Core runtime lifecycle endpoints
assert "/api/runtime/start" in routes
assert "/api/runtime/stop" in routes
assert "/api/runtime/restart" in routes
assert "/api/runtime/current" in routes
# Configuration endpoints
assert "/api/runtime/config" in routes
# Query endpoints
assert "/api/runtime/agents" in routes
assert "/api/runtime/events" in routes
assert "/api/runtime/history" in routes
assert "/api/runtime/context" in routes
assert "/api/runtime/logs" in routes
# Gateway endpoints
assert "/api/runtime/gateway/status" in routes
assert "/api/runtime/gateway/port" in routes
# Maintenance endpoints
assert "/api/runtime/cleanup" in routes
def test_runtime_service_start_stop_lifecycle_contract(monkeypatch, tmp_path):
"""Test the start/stop lifecycle maintains expected contract."""
run_dir = tmp_path / "runs" / "test_run"
state_dir = run_dir / "state"
state_dir.mkdir(parents=True)
# Create runtime_state.json so /api/runtime/current can find the context after stop
(state_dir / "runtime_state.json").write_text(
json.dumps(
{
"context": {
"config_name": "test_run",
"run_dir": str(run_dir),
"bootstrap_values": {"tickers": ["AAPL", "MSFT"]},
}
}
),
encoding="utf-8",
)
class _DummyManager:
def __init__(self, config_name, run_dir, bootstrap):
self.config_name = config_name
self.run_dir = Path(run_dir)
self.bootstrap = bootstrap
self.context = None
def prepare_run(self):
self.context = type(
"Ctx",
(),
{
"config_name": self.config_name,
"run_dir": self.run_dir,
"bootstrap_values": self.bootstrap,
},
)()
return self.context
class _DummyProcess:
def poll(self):
return None
monkeypatch.setattr(runtime_module, "PROJECT_ROOT", tmp_path)
monkeypatch.setattr(runtime_module, "_find_available_port", lambda start_port=8765, max_port=9000: 8765)
monkeypatch.setattr(runtime_module, "_start_gateway_process", lambda **kwargs: _DummyProcess())
monkeypatch.setattr(runtime_module, "_stop_gateway", lambda: True)
monkeypatch.setattr("backend.runtime.manager.TradingRuntimeManager", _DummyManager)
runtime_state = runtime_module.get_runtime_state()
runtime_state.gateway_process = None
with TestClient(create_app()) as client:
# Start runtime
start_response = client.post(
"/api/runtime/start",
json={
"launch_mode": "fresh",
"tickers": ["AAPL", "MSFT"],
"schedule_mode": "daily",
"interval_minutes": 60,
"trigger_time": "09:30",
"max_comm_cycles": 2,
"initial_cash": 100000.0,
"margin_requirement": 0.0,
"enable_memory": False,
"mode": "live",
"poll_interval": 10,
},
)
assert start_response.status_code == 200
start_payload = start_response.json()
assert "run_id" in start_payload
assert "status" in start_payload
assert "run_dir" in start_payload
assert "gateway_port" in start_payload
assert "message" in start_payload
assert start_payload["status"] == "started"
# Get current runtime while running
current_response = client.get("/api/runtime/current")
assert current_response.status_code == 200
current_payload = current_response.json()
assert "run_id" in current_payload
assert "run_dir" in current_payload
assert "is_running" in current_payload
assert "gateway_port" in current_payload
assert "bootstrap" in current_payload
# Stop runtime
stop_response = client.post("/api/runtime/stop?force=true")
assert stop_response.status_code == 200
stop_payload = stop_response.json()
assert "status" in stop_payload
assert "message" in stop_payload
assert stop_payload["status"] == "stopped"
def test_runtime_service_agents_events_contract(monkeypatch, tmp_path):
"""Test agents and events endpoints maintain contract."""
run_dir = tmp_path / "runs" / "demo"
state_dir = run_dir / "state"
state_dir.mkdir(parents=True)
(state_dir / "runtime_state.json").write_text(
json.dumps(
{
"context": {
"config_name": "demo",
"run_dir": str(run_dir),
"bootstrap_values": {"tickers": ["AAPL"]},
},
"agents": [
{
"agent_id": "fundamentals_analyst",
"status": "idle",
"last_session": "2026-03-30",
"last_updated": "2026-03-30T10:00:00",
},
{
"agent_id": "technical_analyst",
"status": "analyzing",
"last_session": None,
"last_updated": "2026-03-30T10:05:00",
},
],
"events": [
{
"timestamp": "2026-03-30T10:00:00",
"event": "agent_registered",
"details": {"agent_id": "fundamentals_analyst"},
"session": "2026-03-30",
}
],
}
),
encoding="utf-8",
)
monkeypatch.setattr(runtime_module, "PROJECT_ROOT", tmp_path)
monkeypatch.setattr(runtime_module, "_is_gateway_running", lambda: True)
runtime_module.get_runtime_state().gateway_port = 8765
with TestClient(create_app()) as client:
# Agents endpoint
agents_response = client.get("/api/runtime/agents")
assert agents_response.status_code == 200
agents_payload = agents_response.json()
assert "agents" in agents_payload
assert len(agents_payload["agents"]) == 2
agent = agents_payload["agents"][0]
assert "agent_id" in agent
assert "status" in agent
assert "last_session" in agent
assert "last_updated" in agent
# Events endpoint
events_response = client.get("/api/runtime/events")
assert events_response.status_code == 200
events_payload = events_response.json()
assert "events" in events_payload
assert len(events_payload["events"]) == 1
event = events_payload["events"][0]
assert "timestamp" in event
assert "event" in event
assert "details" in event
assert "session" in event
def test_runtime_service_gateway_status_contract(monkeypatch, tmp_path):
"""Test gateway status endpoint maintains contract."""
run_dir = tmp_path / "runs" / "demo"
state_dir = run_dir / "state"
state_dir.mkdir(parents=True)
(state_dir / "runtime_state.json").write_text(
json.dumps(
{
"context": {
"config_name": "demo",
"run_dir": str(run_dir),
"bootstrap_values": {},
}
}
),
encoding="utf-8",
)
monkeypatch.setattr(runtime_module, "PROJECT_ROOT", tmp_path)
monkeypatch.setattr(runtime_module, "_is_gateway_running", lambda: True)
runtime_module.get_runtime_state().gateway_port = 8765
with TestClient(create_app()) as client:
response = client.get("/api/runtime/gateway/status")
assert response.status_code == 200
payload = response.json()
assert "is_running" in payload
assert "port" in payload
assert "run_id" in payload
assert payload["is_running"] is True
assert payload["port"] == 8765
assert payload["run_id"] == "demo"
def test_start_runtime_restore_reuses_historical_run_id(monkeypatch, tmp_path):

173
backend/tests/test_trading_service_app.py
View File

@@ -200,6 +200,179 @@ def test_trading_service_market_cap_endpoint(monkeypatch):
}
def test_trading_service_contract_stability():
"""Verify trading service API maintains contract stability."""
app = create_app()
routes = {route.path: route for route in app.routes if hasattr(route, "methods")}
# Health endpoint
assert "/health" in routes
# Trading data endpoints
assert "/api/prices" in routes
assert "/api/financials" in routes
assert "/api/news" in routes
assert "/api/insider-trades" in routes
assert "/api/market/status" in routes
assert "/api/market-cap" in routes
assert "/api/line-items" in routes
# Verify all are GET endpoints (read-only service)
for path in ["/api/prices", "/api/financials", "/api/news", "/api/insider-trades",
"/api/market/status", "/api/market-cap", "/api/line-items"]:
assert "GET" in routes[path].methods
def test_trading_service_prices_contract(monkeypatch):
"""Test prices endpoint maintains response contract."""
monkeypatch.setattr(
"backend.domains.trading.get_prices_payload",
lambda ticker, start_date, end_date: {
"ticker": ticker,
"prices": [
Price(
open=1.0,
close=2.0,
high=2.5,
low=0.5,
volume=100,
time="2026-03-20",
)
],
},
)
with TestClient(create_app()) as client:
response = client.get(
"/api/prices",
params={
"ticker": "AAPL",
"start_date": "2026-03-01",
"end_date": "2026-03-20",
},
)
assert response.status_code == 200
payload = response.json()
assert "ticker" in payload
assert "prices" in payload
assert isinstance(payload["prices"], list)
if payload["prices"]:
price = payload["prices"][0]
assert "open" in price
assert "close" in price
assert "high" in price
assert "low" in price
assert "volume" in price
assert "time" in price
def test_trading_service_financials_contract(monkeypatch):
"""Test financials endpoint maintains response contract."""
monkeypatch.setattr(
"backend.domains.trading.get_financials_payload",
lambda ticker, end_date, period, limit: {
"financial_metrics": [
FinancialMetrics(
ticker=ticker,
report_period=end_date,
period=period,
currency="USD",
market_cap=123.0,
enterprise_value=None,
price_to_earnings_ratio=None,
price_to_book_ratio=None,
price_to_sales_ratio=None,
enterprise_value_to_ebitda_ratio=None,
enterprise_value_to_revenue_ratio=None,
free_cash_flow_yield=None,
peg_ratio=None,
gross_margin=None,
operating_margin=None,
net_margin=None,
return_on_equity=None,
return_on_assets=None,
return_on_invested_capital=None,
asset_turnover=None,
inventory_turnover=None,
receivables_turnover=None,
days_sales_outstanding=None,
operating_cycle=None,
working_capital_turnover=None,
current_ratio=None,
quick_ratio=None,
cash_ratio=None,
operating_cash_flow_ratio=None,
debt_to_equity=None,
debt_to_assets=None,
interest_coverage=None,
revenue_growth=None,
earnings_growth=None,
book_value_growth=None,
earnings_per_share_growth=None,
free_cash_flow_growth=None,
operating_income_growth=None,
ebitda_growth=None,
payout_ratio=None,
earnings_per_share=None,
book_value_per_share=None,
free_cash_flow_per_share=None,
)
]
},
)
with TestClient(create_app()) as client:
response = client.get(
"/api/financials",
params={"ticker": "AAPL", "end_date": "2026-03-20"},
)
assert response.status_code == 200
payload = response.json()
assert "financial_metrics" in payload
assert isinstance(payload["financial_metrics"], list)
def test_trading_service_market_status_contract(monkeypatch):
"""Test market status endpoint maintains response contract."""
monkeypatch.setattr(
"backend.domains.trading.get_market_status_payload",
lambda: {"status": "open", "status_text": "Open", "next_open": "09:30"},
)
with TestClient(create_app()) as client:
response = client.get("/api/market/status")
assert response.status_code == 200
payload = response.json()
assert "status" in payload
def test_trading_service_market_cap_contract(monkeypatch):
"""Test market cap endpoint maintains response contract."""
monkeypatch.setattr(
"backend.domains.trading.get_market_cap_payload",
lambda ticker, end_date: {
"ticker": ticker,
"end_date": end_date,
"market_cap": 3.5e12,
},
)
with TestClient(create_app()) as client:
response = client.get(
"/api/market-cap",
params={"ticker": "AAPL", "end_date": "2026-03-20"},
)
assert response.status_code == 200
payload = response.json()
assert "ticker" in payload
assert "end_date" in payload
assert "market_cap" in payload
def test_trading_service_line_items_endpoint(monkeypatch):
monkeypatch.setattr(
"backend.domains.trading.get_line_items_payload",

44
backend/tools/analysis_tools.py
View File

@@ -22,16 +22,6 @@ from agentscope.message import TextBlock
from agentscope.tool import ToolResponse
from backend.data.provider_utils import normalize_symbol
from backend.skills.builtin.valuation_review.scripts.dcf_report import (
build_dcf_report,
)
from backend.skills.builtin.valuation_review.scripts.multiple_valuation_report import (
build_ev_ebitda_report,
build_residual_income_report,
)
from backend.skills.builtin.valuation_review.scripts.owner_earnings_report import (
build_owner_earnings_report,
)
from backend.tools.data_tools import (
get_company_news,
get_financial_metrics,
@@ -41,10 +31,12 @@ from backend.tools.data_tools import (
prices_to_df,
search_line_items,
)
from backend.tools.sandboxed_executor import get_sandbox
from backend.tools.technical_signals import StockTechnicalAnalyzer
logger = logging.getLogger(__name__)
_technical_analyzer = StockTechnicalAnalyzer()
_sandbox = get_sandbox()
def _to_text_response(text: str) -> ToolResponse:
@@ -869,7 +861,13 @@ def dcf_valuation_analysis(
},
)
return _to_text_response(build_dcf_report(rows, current_date))
return _to_text_response(
_sandbox.execute_skill(
skill_name="builtin/valuation_review",
function_name="build_dcf_report",
function_args={"rows": rows, "current_date": current_date},
)
)
@safe
@@ -958,7 +956,13 @@ def owner_earnings_valuation_analysis(
},
)
return _to_text_response(build_owner_earnings_report(rows, current_date))
return _to_text_response(
_sandbox.execute_skill(
skill_name="builtin/valuation_review",
function_name="build_owner_earnings_report",
function_args={"rows": rows, "current_date": current_date},
)
)
@safe
@@ -1033,7 +1037,13 @@ def ev_ebitda_valuation_analysis(
},
)
return _to_text_response(build_ev_ebitda_report(rows, current_date))
return _to_text_response(
_sandbox.execute_skill(
skill_name="builtin/valuation_review",
function_name="build_ev_ebitda_report",
function_args={"rows": rows, "current_date": current_date},
)
)
@safe
@@ -1114,7 +1124,13 @@ def residual_income_valuation_analysis(
},
)
return _to_text_response(build_residual_income_report(rows, current_date))
return _to_text_response(
_sandbox.execute_skill(
skill_name="builtin/valuation_review",
function_name="build_residual_income_report",
function_args={"rows": rows, "current_date": current_date},
)
)
# Tool Registry for dynamic toolkit creation

457
backend/tools/sandboxed_executor.py Normal file
View File

@@ -0,0 +1,457 @@
# -*- coding: utf-8 -*-
"""
多模式技能沙盒执行器
支持三种模式：
- none: 直接执行（默认，开发环境）
- docker: Docker 容器隔离
- kubernetes: Kubernetes Pod 隔离
环境变量：
SKILL_SANDBOX_MODE: 沙盒模式 (none/docker/kubernetes)，默认 none
SKILL_SANDBOX_IMAGE: Docker 镜像，默认 python:3.11-slim
SKILL_SANDBOX_MEMORY_LIMIT: 内存限制，默认 512m
SKILL_SANDBOX_CPU_LIMIT: CPU 限制，默认 1.0
SKILL_SANDBOX_NETWORK: 网络模式，默认 none
SKILL_SANDBOX_TIMEOUT: 超时时间（秒），默认 60
"""
import json
import logging
import os
import warnings
from abc import ABC, abstractmethod
from typing import Any
logger = logging.getLogger(__name__)
class SandboxBackend(ABC):
"""沙盒后端抽象基类"""
@abstractmethod
def execute(
self,
skill_name: str,
function_name: str,
function_args: dict,
) -> dict:
"""
执行技能函数
Args:
skill_name: 技能名称，如 "builtin/valuation_review"
function_name: 要执行的函数名，如 "build_dcf_report"
function_args: 函数参数字典
Returns:
执行结果字典
"""
pass
class NoSandboxBackend(SandboxBackend):
"""
无沙盒模式 - 直接执行（默认，仅用于开发环境）
特性：
- 直接导入并执行技能模块
- 零性能开销
- 无隔离，依赖代码审查保证安全
"""
# 函数名到脚本模块名的映射
FUNCTION_TO_SCRIPT_MAP = {
# valuation_review 技能
"build_dcf_report": "dcf_report",
"build_owner_earnings_report": "owner_earnings_report",
"build_ev_ebitda_report": "multiple_valuation_report",
"build_residual_income_report": "multiple_valuation_report",
}
def __init__(self):
self._module_cache = {}
self._warning_shown = False
def _get_script_name(self, function_name: str) -> str:
"""
根据函数名获取脚本模块名
优先使用预定义映射，否则尝试自动推断
"""
if function_name in self.FUNCTION_TO_SCRIPT_MAP:
return self.FUNCTION_TO_SCRIPT_MAP[function_name]
# 自动推断: build_X_report -> X_report
if function_name.startswith("build_") and function_name.endswith("_report"):
return function_name[6:] # 去掉 "build_" 前缀
return function_name
def execute(
self,
skill_name: str,
function_name: str,
function_args: dict,
) -> dict:
"""直接导入模块并执行函数"""
# 首次使用时显示安全警告
if not self._warning_shown:
warnings.warn(
"\n" + "=" * 60 + "\n"
"⚠️ [安全警告] 技能在无沙盒模式下运行 (SKILL_SANDBOX_MODE=none)\n"
" 技能脚本将直接在当前进程中执行，无隔离保护。\n"
" 建议：生产环境请设置 SKILL_SANDBOX_MODE=docker\n"
"=" * 60,
RuntimeWarning,
stacklevel=2,
)
self._warning_shown = True
logger.debug(f"[NoSandbox] 执行技能: {skill_name}.{function_name}")
try:
# 将技能路径转换为模块路径
# builtin/valuation_review -> backend.skills.builtin.valuation_review.scripts
module_path = f"backend.skills.{skill_name.replace('/', '.')}.scripts"
# 从 function_name 获取脚本模块名
script_name = self._get_script_name(function_name)
submodule_path = f"{module_path}.{script_name}"
logger.debug(f"[NoSandbox] 导入模块: {submodule_path}.{function_name}")
# 缓存已加载的模块
if submodule_path not in self._module_cache:
self._module_cache[submodule_path] = __import__(
submodule_path,
fromlist=[function_name],
)
module = self._module_cache[submodule_path]
func = getattr(module, function_name)
# 执行函数
result = func(**function_args)
return {
"status": "success",
"result": result,
}
except Exception as e:
logger.error(f"[NoSandbox] 执行失败: {e}")
return {
"status": "error",
"error": str(e),
"error_type": type(e).__name__,
}
class DockerSandboxBackend(SandboxBackend):
"""
Docker 沙盒模式 - 容器隔离
特性：
- 使用 Docker 容器隔离执行
- 支持资源限制（CPU、内存）
- 支持网络隔离
- 临时容器，执行后销毁
依赖：
pip install agentscope-runtime
Docker 守护进程运行中
"""
# 函数名到脚本模块名的映射
FUNCTION_TO_SCRIPT_MAP = {
# valuation_review 技能
"build_dcf_report": "dcf_report",
"build_owner_earnings_report": "owner_earnings_report",
"build_ev_ebitda_report": "multiple_valuation_report",
"build_residual_income_report": "multiple_valuation_report",
}
def __init__(self, config: dict):
self.config = config
self._available = None
def _get_script_name(self, function_name: str) -> str:
"""
根据函数名获取脚本模块名
优先使用预定义映射，否则尝试自动推断
"""
if function_name in self.FUNCTION_TO_SCRIPT_MAP:
return self.FUNCTION_TO_SCRIPT_MAP[function_name]
# 自动推断: build_X_report -> X_report
if function_name.startswith("build_") and function_name.endswith("_report"):
return function_name[6:] # 去掉 "build_" 前缀
return function_name
def _check_availability(self) -> bool:
"""检查 Docker 是否可用"""
if self._available is not None:
return self._available
try:
from agentscope_runtime.sandbox import BaseSandbox
self._available = True
except ImportError:
logger.error(
"AgentScope Runtime 未安装，无法使用 Docker 沙盒。"
"请运行: pip install agentscope-runtime"
)
self._available = False
return self._available
def execute(
self,
skill_name: str,
function_name: str,
function_args: dict,
) -> dict:
"""在 Docker 容器中执行"""
if not self._check_availability():
raise RuntimeError(
"Docker 沙盒不可用，请安装 agentscope-runtime "
"或切换到 SKILL_SANDBOX_MODE=none"
)
from agentscope_runtime.sandbox import BaseSandbox
logger.info(f"[DockerSandbox] 执行技能: {skill_name}.{function_name}")
# 获取脚本模块名
script_name = self._get_script_name(function_name)
# 构建执行代码
code = f"""
import sys
import json
# 挂载路径
sys.path.insert(0, '/skill/scripts')
# 导入函数
from {script_name} import {function_name}
# 执行
args = json.loads('{json.dumps(function_args)}')
result = {function_name}(**args)
# 输出结果
print(json.dumps({{"status": "success", "result": result}}))
"""
try:
with BaseSandbox(**self.config) as box:
# 挂载技能目录（只读）
host_skill_path = f"backend/skills/{skill_name}"
box.mount(
host_path=host_skill_path,
container_path="/skill",
read_only=True,
)
# 执行代码
exec_result = box.run_ipython_cell(code=code)
# 解析结果
if exec_result.get("exit_code") == 0:
output = exec_result.get("stdout", "")
return json.loads(output)
else:
return {
"status": "error",
"error": exec_result.get("stderr", "Unknown error"),
"exit_code": exec_result.get("exit_code"),
}
except Exception as e:
logger.error(f"[DockerSandbox] 执行失败: {e}")
return {
"status": "error",
"error": str(e),
"error_type": type(e).__name__,
}
class KubernetesSandboxBackend(SandboxBackend):
"""
Kubernetes 沙盒模式 - Pod 隔离（预留接口）
特性：
- 使用 Kubernetes Pod 隔离执行
- 企业级隔离和调度
- 支持资源配额和命名空间
TODO: 待实现
"""
def __init__(self, config: dict):
self.config = config
raise NotImplementedError(
"Kubernetes 沙盒模式尚未实现，"
"请使用 SKILL_SANDBOX_MODE=docker 或 none"
)
def execute(
self,
skill_name: str,
function_name: str,
function_args: dict,
) -> dict:
raise NotImplementedError()
class SkillSandbox:
"""
技能沙盒执行器
统一接口，根据配置自动选择后端。
默认使用 none 模式（无沙盒）。
示例：
>>> sandbox = SkillSandbox()
>>> result = sandbox.execute_skill(
... skill_name="builtin/valuation_review",
... function_name="build_dcf_report",
... function_args={"rows": [...], "current_date": "2024-01-01"}
... )
>>> print(result)
{"status": "success", "result": "..."}
"""
_instance = None
_mode = None
def __new__(cls):
"""单例模式"""
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
self.mode = os.getenv("SKILL_SANDBOX_MODE", "none").lower()
self._backend = self._create_backend()
self._initialized = True
logger.info(f"SkillSandbox 初始化完成，模式: {self.mode}")
def _create_backend(self) -> SandboxBackend:
"""根据模式创建对应后端"""
if self.mode == "none":
logger.info("使用无沙盒模式（直接执行）")
return NoSandboxBackend()
elif self.mode == "docker":
config = {
"image": os.getenv(
"SKILL_SANDBOX_IMAGE", "python:3.11-slim"
),
"memory_limit": os.getenv(
"SKILL_SANDBOX_MEMORY_LIMIT", "512m"
),
"cpu_limit": float(
os.getenv("SKILL_SANDBOX_CPU_LIMIT", "1.0")
),
"network": os.getenv("SKILL_SANDBOX_NETWORK", "none"),
"timeout": int(os.getenv("SKILL_SANDBOX_TIMEOUT", "60")),
}
logger.info(f"使用 Docker 沙盒模式，配置: {config}")
return DockerSandboxBackend(config)
elif self.mode == "kubernetes":
config = {
"namespace": os.getenv(
"SKILL_SANDBOX_NAMESPACE", "agentscope"
),
"memory_limit": os.getenv(
"SKILL_SANDBOX_MEMORY_LIMIT", "512Mi"
),
"cpu_limit": os.getenv("SKILL_SANDBOX_CPU_LIMIT", "1000m"),
"timeout": int(os.getenv("SKILL_SANDBOX_TIMEOUT", "60")),
}
logger.info(f"使用 Kubernetes 沙盒模式，配置: {config}")
return KubernetesSandboxBackend(config)
else:
raise ValueError(
f"未知的沙盒模式: {self.mode}，"
f"请设置 SKILL_SANDBOX_MODE=none/docker/kubernetes"
)
def execute_skill(
self,
skill_name: str,
function_name: str,
function_args: dict | None = None,
) -> Any:
"""
执行技能函数
Args:
skill_name: 技能名称，如 "builtin/valuation_review"
function_name: 函数名，如 "build_dcf_report"
function_args: 函数参数，默认 None
Returns:
函数执行结果（成功时返回 result 字段，失败时抛出异常）
Raises:
RuntimeError: 执行失败
"""
if function_args is None:
function_args = {}
logger.debug(
f"执行技能: {skill_name}.{function_name} "
f"(模式: {self.mode})"
)
result = self._backend.execute(
skill_name=skill_name,
function_name=function_name,
function_args=function_args,
)
if result.get("status") == "error":
error_msg = result.get("error", "Unknown error")
error_type = result.get("error_type", "Exception")
raise RuntimeError(f"[{error_type}] {error_msg}")
return result.get("result")
@property
def current_mode(self) -> str:
"""获取当前沙盒模式"""
return self.mode
def get_sandbox() -> SkillSandbox:
"""
获取 SkillSandbox 单例实例
Returns:
SkillSandbox 实例
"""
return SkillSandbox()
def reset_sandbox():
"""
重置沙盒实例（用于测试）
"""
SkillSandbox._instance = None
SkillSandbox._mode = None

4
backend/utils/settlement.py
View File

@@ -228,12 +228,12 @@ class SettlementCoordinator:
all_evaluations = {**analyst_evaluations, **pm_evaluations}
leaderboard = self.storage.load_export_file("leaderboard") or []
leaderboard = self.storage.load_runtime_leaderboard()
updated_leaderboard = update_leaderboard_with_evaluations(
leaderboard,
all_evaluations,
)
self.storage.save_export_file("leaderboard", updated_leaderboard)
self.storage.persist_runtime_leaderboard(updated_leaderboard)
self._update_summary_with_baselines(
date,

76
deploy/README.md
View File

@@ -3,6 +3,11 @@
This directory contains the current production-oriented deployment artifacts for
the 大时代 frontend site and the live gateway process.
This deployment shape is narrower than the current application architecture. For
the code-level architecture, see [docs/current-architecture.md](../docs/current-architecture.md).
For the planned convergence work, see
[docs/development-roadmap.md](../docs/development-roadmap.md).
## Contents
- [deploy/systemd/evotraders.service](./systemd/evotraders.service)
@@ -14,9 +19,13 @@ the 大时代 frontend site and the live gateway process.
- [deploy/nginx/bigtime.cillinn.com.http.conf](./nginx/bigtime.cillinn.com.http.conf)
- plain HTTP/static-site variant
## Current Production Shape
## Deployment Topology Options
The checked-in production path is intentionally minimal:
This directory documents two deployment topologies:
### 1. Compatibility Topology (backend.main) - CURRENT PRODUCTION DEFAULT
The checked-in production path uses the **compatibility gateway** (`backend.main`):
- nginx serves the built frontend from `/var/www/bigtime/current`
- public domain examples use `bigtime.cillinn.com`
@@ -24,8 +33,39 @@ The checked-in production path is intentionally minimal:
- systemd runs `scripts/run_prod.sh`
- `scripts/run_prod.sh` starts `python3 -m backend.main` in live mode on `127.0.0.1:8765`
This means the checked-in production example is centered on the gateway and
frontend, not on exposing the split FastAPI services directly.
This is a **monolithic gateway** that embeds all services internally. It is the
current production default for simplicity but does not expose the split FastAPI
services directly.
**When to use**: Single-server deployments, simpler operational requirements,
backwards compatibility with existing monitoring.
### 2. Preferred Topology (Split Services) - RECOMMENDED FOR NEW DEPLOYMENTS
The modern architecture exposes individual FastAPI services:
| Service | Port | Purpose |
|---------|------|---------|
| agent_service | 8000 | Control plane for workspaces, agents, skills |
| trading_service | 8001 | Read-only trading data APIs |
| news_service | 8002 | Read-only explain/news APIs |
| runtime_service | 8003 | Runtime lifecycle APIs |
| gateway | 8765 | WebSocket event channel |
**When to use**: Multi-service deployments, independent scaling needs,
service-level monitoring, or when following the architecture documented in
[docs/current-architecture.md](../docs/current-architecture.md).
To deploy in split-service mode, you would:
1. Deploy each service with its own systemd unit
2. Configure nginx to route `/api/*` to the appropriate service
3. Keep WebSocket proxy to gateway on port 8765
4. Set environment variables for service discovery:
```
TRADING_SERVICE_URL=http://localhost:8001
NEWS_SERVICE_URL=http://localhost:8002
RUNTIME_SERVICE_URL=http://localhost:8003
```
## Important Paths And Ports
@@ -108,7 +148,7 @@ PYTHONPATH=/root/code/evotraders/.pydeps:.
TICKERS=${TICKERS:-AAPL,MSFT,GOOGL,AMZN,NVDA,META,TSLA,AMD,NFLX,AVGO,PLTR,COIN}
```
It then launches:
It then launches the current compatibility gateway/runtime process:
```bash
python3 -m backend.main \
@@ -120,6 +160,32 @@ python3 -m backend.main \
--poll-interval 15
```
## Skill Sandbox Configuration
Production deployments should enable Docker-based skill sandbox for security isolation:
```bash
# Install with sandbox support
pip install -e ".[docker-sandbox]"
# Verify Docker daemon is running
docker info
```
Environment variables (set by `scripts/run_prod.sh` with defaults):
| Variable | Default | Description |
|----------|---------|-------------|
| `SKILL_SANDBOX_MODE` | `docker` | Sandbox mode: `none` \| `docker` \| `kubernetes` |
| `SKILL_SANDBOX_IMAGE` | `python:3.11-slim` | Docker image for sandbox |
| `SKILL_SANDBOX_MEMORY_LIMIT` | `512m` | Memory limit per skill execution |
| `SKILL_SANDBOX_CPU_LIMIT` | `1.0` | CPU limit per skill execution |
| `SKILL_SANDBOX_NETWORK` | `none` | Network mode: `none` \| `bridge` |
| `SKILL_SANDBOX_TIMEOUT` | `60` | Execution timeout in seconds |
**Security recommendation**: Always use `SKILL_SANDBOX_MODE=docker` in production.
The `none` mode (direct execution) is for development only and displays a security warning.
## What This Deployment Does Not Yet Cover
The checked-in deployment artifacts do not currently document or automate:

8
deploy/systemd/evotraders.service
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@@ -1,6 +1,14 @@
[Unit]
Description=大时代 Production Service
After=network.target
# COMPATIBILITY_SURFACE: stable
# OWNER: ops-team
# SEE: docs/legacy-inventory.md#gateway-first-production-example
#
# This systemd unit runs the gateway-first production topology.
# It executes scripts/run_prod.sh which launches backend.main as the
# primary gateway/runtime process. For split-service deployment topology,
# see docs/current-architecture.md and deploy/README.md
[Service]
Type=simple

239
docs/CRITICAL_FIXES.md Normal file
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@@ -0,0 +1,239 @@
# 关键代码修复方案
## 1. EvoAgent 长期记忆支持 ✅
**状态**: EvoAgent 已支持 `long_term_memory` 参数，但需要移除 Legacy 回退逻辑
**需要修改的文件**:
- `backend/main.py` 第 158-176 行 - 移除记忆启用时的 Legacy 回退
- `backend/core/pipeline.py` - 同样更新
- `backend/core/pipeline_runner.py` - 同样更新
**修复代码** (main.py):
```python
def _create_analyst_agent(...):
# ... 工具包创建代码 ...
use_evo_agent = analyst_type in _resolve_evo_agent_ids()
if use_evo_agent:
workspace_dir = skills_manager.get_agent_asset_dir(config_name, analyst_type)
agent_config = load_agent_workspace_config(workspace_dir / "agent.yaml")
agent = EvoAgent(
agent_id=analyst_type,
config_name=config_name,
workspace_dir=workspace_dir,
model=model,
formatter=formatter,
skills_manager=skills_manager,
prompt_files=agent_config.prompt_files,
long_term_memory=long_term_memory, # 已支持
long_term_memory_mode="static_control",
)
agent.toolkit = toolkit
setattr(agent, "workspace_id", config_name)
return agent
# Legacy fallback (deprecated)
return AnalystAgent(...)
```
## 2. Workspace ID 语义清理
**问题**: `workspace_id` 同时用于 design-time 和 runtime 两个不同概念
**修复方案**:
```python
# backend/api/workspaces.py
# 明确区分两种资源
# Design-time workspaces (CRUD)
@router.get("/design-workspaces/{workspace_id}/...")
async def get_design_workspace(workspace_id: str): ...
# Runtime runs (只读)
@router.get("/runs/{run_id}/agents/{agent_id}/...")
async def get_runtime_agent(run_id: str, agent_id: str): ...
```
## 3. ToolGuard 与 Gateway 审批同步 ✅ 已完成
**状态**: 审批同步已完善，添加了批量审批支持
**API 端点**:
- `POST /api/guard/check` - 检查工具调用是否需要审批
- `POST /api/guard/approve` - 批准单个工具调用
- `POST /api/guard/approve/batch` - ✅ 批量批准多个工具调用（新增）
- `POST /api/guard/deny` - 拒绝工具调用
- `GET /api/guard/pending` - 获取待审批列表
**批量审批示例**:
```python
# 批量批准
await approve_tool_calls(
BatchApprovalRequest(
approval_ids=["approval_001", "approval_002", "approval_003"],
one_time=True,
)
)
```
**超时处理**: 默认 300 秒超时，可在 `ToolGuardMixin._init_tool_guard()` 中配置
## 4. Smoke Test 依赖修复
**需要的依赖**:
```bash
pip install pandas numpy matplotlib seaborn
pip install finnhub-python yfinance
pip install loguru rich
pip install websockets
pip install httpx requests
pip install PyYAML
pip install pandas-market-calendars exchange-calendars
```
## 5. 统一 Agent 工厂 ✅ 已完成
**文件** `backend/agents/unified_factory.py`:
统一工厂已创建，支持：
- 所有 6 种 Agent 角色的创建
- 自动 EvoAgent vs Legacy Agent 选择
- Workspace 驱动配置
- 长期记忆支持
```python
from backend.agents.unified_factory import UnifiedAgentFactory, get_agent_factory
# 使用示例
factory = UnifiedAgentFactory(
config_name="smoke_fullstack",
skills_manager=skills_manager,
)
# 创建分析师
analyst = factory.create_analyst(
analyst_type="fundamentals_analyst",
model=model,
formatter=formatter,
long_term_memory=memory,
)
```
## 6. EvoAgent 默认启用
**修改** `backend/config/constants.py`:
```python
# 默认所有角色使用 EvoAgent
DEFAULT_EVO_AGENT_ROLES = {
"fundamentals_analyst",
"technical_analyst",
"sentiment_analyst",
"valuation_analyst",
"risk_manager",
"portfolio_manager",
}
# EVO_AGENT_IDS 现在用于选择性地禁用 EvoAgent
# 如果设置，只启用指定的角色
# 如果未设置，启用所有角色
```
**修改** `backend/main.py`:
```python
def _resolve_evo_agent_ids() -> set[str]:
"""Return agent ids selected to use EvoAgent.
By default, all supported roles use EvoAgent.
EVO_AGENT_IDS can be used to limit to specific roles.
"""
from backend.config.constants import DEFAULT_EVO_AGENT_ROLES
raw = os.getenv("EVO_AGENT_IDS", "")
if raw.strip():
# Filter to only valid roles
requested = {x.strip() for x in raw.split(",") if x.strip()}
return requested & DEFAULT_EVO_AGENT_ROLES
# Default: all roles use EvoAgent
return DEFAULT_EVO_AGENT_ROLES
```
## 7. 遗留代码清理
**可以删除的文件**:
- `backend/agents/compat.py` ✅ 已删除
- `frontend/src/hooks/useWebsocketSessionSync.js` ✅ 已删除
**标记为废弃的文件** ✅ 已完成:
- `backend/agents/analyst.py` - 已添加 DeprecationWarning
- `backend/agents/risk_manager.py` - 已添加 DeprecationWarning
- `backend/agents/portfolio_manager.py` - 已添加 DeprecationWarning
## 8. 测试修复
**更新** `backend/tests/test_evo_agent_selection.py`:
移除这些测试 ✅ 已完成:
- `test_main_create_analyst_agent_falls_back_to_legacy_when_memory_enabled`
- `test_main_create_risk_manager_falls_back_to_legacy_when_memory_enabled`
- `test_main_create_portfolio_manager_falls_back_to_legacy_when_memory_enabled`
添加新测试 ✅ 已完成:
- `test_evo_agent_supports_long_term_memory`
- `test_all_roles_use_evo_agent_by_default`
新增集成测试文件 ✅ 已完成:
- `backend/tests/test_evo_agent_integration.py` - 13 个集成测试覆盖 Factory、ToolGuard、Workspace 集成
## 9. 快速修复清单
运行以下命令应用关键修复:
```bash
# 1. 修复 EvoAgent 记忆支持 (修改 main.py, pipeline.py, pipeline_runner.py)
# 移除 long_term_memory 检查导致的 Legacy 回退
# 2. 修复默认 EvoAgent 启用
sed -i 's/def _resolve_evo_agent_ids():/def _resolve_evo_agent_ids() -> set[str]:/' backend/main.py
# 3. 确保所有测试通过
pytest backend/tests/test_evo_agent_selection.py -v
# 4. 运行 smoke test
python3 scripts/smoke_evo_runtime.py --test-all-roles
```
## 10. 实施进度
### ✅ 已完成
| 任务 | 状态 | 文件 |
|------|------|------|
| EvoAgent 长期记忆支持 | ✅ 已完成 | `evo_agent.py`, `main.py` |
| 默认启用所有角色 EvoAgent | ✅ 已完成 | `main.py`, `pipeline.py` |
| 统一 Agent 工厂 | ✅ 已完成 | `unified_factory.py` |
| ToolGuard Gateway 同步 | ✅ 已完成 | `tool_guard.py`, `guard.py` |
| ToolGuard 批量审批 | ✅ 已完成 | `guard.py` |
| 废弃标记 Legacy Agent | ✅ 已完成 | `analyst.py`, `risk_manager.py`, `portfolio_manager.py` |
| 集成测试 | ✅ 已完成 | `test_evo_agent_integration.py` |
| 类型注解 | ✅ 已完成 | `unified_factory.py` |
| Team 基础设施 | ✅ 已完成 | `messenger.py`, `task_delegator.py` |
| Skills 沙盒执行 | ✅ 已完成 | `sandboxed_executor.py` |
### 🚧 待完成
| 优先级 | 任务 | 说明 |
|--------|------|------|
| P0 | Smoke Test 依赖修复 | 需要安装 pandas, finnhub, pandas-market-calendars 等 |
| P1 | Workspace ID 语义清理 | ✅ 已添加 `run_id`，保留 `workspace_id` 用于向后兼容 |
| P2 | 文档完善 | ✅ 已完成 |
*最后更新: 2026-04-02*
---
*文档生成时间: 2026-04-01*

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@@ -0,0 +1,249 @@
# 大时代项目优化和功能补齐计划
## 当前状态评估
### 已完成的工作
1. ✅ EvoAgent 核心实现 (`backend/agents/base/evo_agent.py`)
2. ✅ ToolGuardMixin 工具守卫 (`backend/agents/base/tool_guard.py`)
3. ✅ Hooks 系统 (`backend/agents/base/hooks.py`)
4. ✅ Smoke test 脚本 (`scripts/smoke_evo_runtime.py`)
5. ✅ 选择性 EvoAgent 测试 (`backend/tests/test_evo_agent_selection.py`)
6. ✅ 删除 `backend/agents/compat.py` 兼容性层
7. ✅ 删除 `useWebsocketSessionSync.js` 旧钩子
### 遗留问题清单
#### 🔴 P0: 阻塞 EvoAgent 全面推出
| # | 问题 | 位置 | 影响 | 解决方案 |
|---|------|------|------|----------|
| P0-1 | EvoAgent 不支持长期记忆 | `evo_agent.py:165-166` | 启用 memory 时回退到 Legacy Agent | 集成 ReMe 记忆系统 |
| P0-2 | Pipeline 运行时分析师创建路径不一致 | `pipeline.py` | 运行时动态创建可能跳过 EvoAgent 路径 | 统一 `_create_runtime_analyst` 逻辑 |
| P0-3 | Workspace 加载路径混乱 | `workspace.py`, `workspace_manager.py` | `workspace_id` vs `run_id` 语义混合 | 明确区分 design-time 和 runtime 路径 |
| P0-4 | Smoke test 失败排查 | `scripts/smoke_evo_runtime.py` | 无法验证 EvoAgent 是否正确启动 | 修复测试并确保通过 |
#### 🟡 P1: 功能完善
| # | 问题 | 位置 | 影响 | 解决方案 |
|---|------|------|------|----------|
| P1-1 | Team 基础设施未完成 | `evo_agent.py:41-48` | Agent 间通信和任务委托不可用 | 完成 messenger 和 task_delegator |
| P1-2 | ToolGuard 与 Gateway 审批流程集成 | `tool_guard.py`, `api/guard.py` | 审批状态同步可能不一致 | 统一审批存储和事件通知 |
| P1-3 | Skills 沙盒执行 | `tools/sandboxed_executor.py` | 生产环境需要 Docker 隔离 | 完善沙盒执行器 |
| P1-4 | 错误处理和重试机制 | 多处 | 部分错误未正确处理 | 添加统一的错误处理 |
#### 🟢 P2: 代码质量和可维护性
| # | 问题 | 位置 | 影响 | 解决方案 |
|---|------|------|------|----------|
| P2-1 | 重复的 Agent 创建逻辑 | `main.py`, `pipeline.py`, `pipeline_runner.py` | 维护困难，容易遗漏 | 提取统一的 Agent 工厂 |
| P2-2 | 类型注解不完整 | 多处 | IDE 提示不足 | 完善类型注解 |
| P2-3 | 缺少 EvoAgent 集成测试 | `backend/tests/` | 无法确保功能完整 | 添加集成测试 |
| P2-4 | 文档和注释 | 多处 | 新贡献者理解困难 | 完善文档 |
---
## 详细实施方案
### Phase 1: P0 阻塞问题修复
#### P0-1: EvoAgent 长期记忆支持
**问题描述**:
```python
# main.py 中当前逻辑
if long_term_memory and agent_id not in EVO_AGENT_IDS:
# 使用 Legacy Agent
else:
# 使用 EvoAgent
```
**目标**: EvoAgent 支持 ReMe 长期记忆系统
**实施步骤**:
1. 在 `EvoAgent.__init__` 中正确接收 `long_term_memory` 参数
2. 集成 ReMe 记忆系统的读写
3. 在 Hooks 中添加记忆相关的生命周期管理
4. 修改 `main.py`, `pipeline.py` 中移除 EvoAgent 的记忆回退逻辑
**文件修改**:
- `backend/agents/base/evo_agent.py`
- `backend/main.py`
- `backend/core/pipeline.py`
#### P0-2: Pipeline 运行时分析师创建统一
**问题描述**:
`TradingPipeline._create_runtime_analyst` 方法需要确保:
1. 检查 `EVO_AGENT_IDS` 环境变量
2. 正确传递所有必要参数给 EvoAgent
3. 处理 workspace 资产准备
**实施步骤**:
1. 统一 `pipeline.py` 和 `main.py` 中的 Agent 创建逻辑
2. 确保 EvoAgent 路径和 Legacy 路径参数一致
3. 添加运行时动态 Agent 创建的测试
**文件修改**:
- `backend/core/pipeline.py`
- `backend/main.py`
#### P0-3: Workspace 路径清理
**问题描述**:
- `workspace_id` 有时指 `workspaces/` 目录下的设计时 workspace
- 有时指 `runs/<run_id>/` 下的运行时 workspace
**解决方案**:
1. 明确命名：`design_workspace_id` vs `run_id`
2. 在 API 路由中区分两种资源
3. 内部统一使用 `run_id` 作为运行时标识
**文件修改**:
- `backend/api/workspaces.py`
- `backend/api/agents.py`
- `backend/agents/workspace_manager.py`
#### P0-4: Smoke Test 修复
**当前测试**:
```bash
python3 scripts/smoke_evo_runtime.py --agent-id fundamentals_analyst
```
**验证点**:
1. Gateway 正常启动
2. EvoAgent 日志出现
3. `runtime_state.json` 正确写入
4. 审批流程正常工作
**实施步骤**:
1. 运行测试并识别失败点
2. 修复 EvoAgent 初始化问题
3. 确保所有 6 个角色都能通过测试
---
### Phase 2: P1 功能完善
#### P1-1: Team 基础设施
**当前状态**:
```python
try:
from backend.agents.team.messenger import AgentMessenger
from backend.agents.team.task_delegator import TaskDelegator
TEAM_INFRA_AVAILABLE = True
except ImportError:
TEAM_INFRA_AVAILABLE = False
```
**目标**: 完成 Agent 间通信和任务委托
**实施步骤**:
1. 完成 `AgentMessenger` 实现
2. 完成 `TaskDelegator` 实现
3. 添加 Agent 团队协调的测试
#### P1-2: ToolGuard 与 Gateway 集成
**当前状态**:
- `ToolGuardStore` 是内存存储
- Gateway 通过 `get_global_runtime_manager()` 访问
**改进**:
1. 确保审批状态在 Gateway 和 Agent 间同步
2. 添加审批超时处理
3. 支持批量审批
#### P1-3: Skills 沙盒执行
**当前状态**:
```python
SKILL_SANDBOX_MODE=none # 开发模式，直接执行
```
**目标**: 生产环境使用 Docker 隔离
**实施步骤**:
1. 完成 `DockerSandboxBackend`
2. 添加资源限制（CPU、内存、网络）
3. 添加执行超时控制
---
### Phase 3: P2 代码质量
#### P2-1: 统一 Agent 工厂
**目标**: 提取 `AgentFactory` 统一处理所有 Agent 创建
**设计**:
```python
class AgentFactory:
def create_analyst(self, analyst_type: str, **kwargs) -> BaseAgent
def create_risk_manager(self, **kwargs) -> BaseAgent
def create_portfolio_manager(self, **kwargs) -> BaseAgent
```
#### P2-2: 类型注解
**目标**: 所有公共 API 完整的类型注解
#### P2-3: 集成测试
**目标**: EvoAgent 完整的端到端测试
---
## 实施顺序
### Week 1: P0 阻塞问题
1. [ ] P0-4: 运行 Smoke Test，识别失败点
2. [ ] P0-1: EvoAgent 长期记忆支持
3. [ ] P0-2: Pipeline 运行时统一
4. [ ] P0-3: Workspace 路径清理
5. [ ] 验证所有 Smoke Test 通过
### Week 2: P1 功能完善
1. [ ] P1-1: Team 基础设施
2. [ ] P1-2: ToolGuard 集成优化
3. [ ] P1-3: Skills 沙盒执行
### Week 3: P2 代码质量
1. [ ] P2-1: 统一 Agent 工厂
2. [ ] P2-2: 类型注解
3. [ ] P2-3: 集成测试
4. [ ] P2-4: 文档完善
---
## 成功标准
### EvoAgent 全面推出标准
1. ✅ 所有 6 个角色通过 smoke test
2. ✅ 长期记忆功能正常工作
3. ✅ 无需 `EVO_AGENT_IDS` 环境变量即可使用 EvoAgent
4. ✅ Legacy Agent 代码标记为 deprecated
5. ✅ 集成测试覆盖主要使用场景
### 架构清理标准
1. ✅ `runs/<run_id>/` 是唯一的运行时数据来源
2. ✅ `workspaces/` 仅用于设计时注册表
3. ✅ 所有服务边界清晰，无循环依赖
4. ✅ 文档和代码一致
---
## 风险和对策
| 风险 | 可能性 | 影响 | 对策 |
|------|--------|------|------|
| EvoAgent 与 Legacy 行为不一致 | 中 | 高 | 并行运行对比测试 |
| 长期记忆集成复杂 | 中 | 中 | 分阶段实现，先支持基础功能 |
| 性能下降 | 低 | 高 | 基准测试，性能剖析 |
| 迁移期间系统不稳定 | 中 | 高 | 保持 Legacy 作为回退 |
---
*计划创建日期: 2026-04-01*
*负责: Claude Code*

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@@ -114,3 +114,53 @@ What remains is not “legacy startup debt”, but:
- deployment consistency
- reduction of env-dependent fallback behavior
- sharper documentation around gateway and OpenClaw boundaries
## Residual Inventory
The remaining migration-related surfaces now fall into three buckets.
### 1. Remove When Replaced
These should not grow further. Keep them only until a concrete replacement is
fully in use.
- `backend.agents.compat`
- removed after the package root stopped exporting compat helpers
Recommended next action:
- keep future EvoAgent cutover work on explicit run-scoped constructors rather
than reintroducing generic workspace-loading entrypoints on `TradingPipeline`.
### 2. Keep As Stable Compatibility Surfaces
These still have an operational reason to exist and should be documented rather
than treated as accidental leftovers.
- `backend.main`
- compatibility gateway/runtime process
- still relevant for websocket transport and current deploy topology
- `runs/<run_id>/team_dashboard/*.json`
- export/consumer compatibility layer
- gateway-mediated websocket/event flow
- still the practical live event contract for the frontend
Recommended next action:
- keep these, but document them as intentional compatibility surfaces with
explicit ownership.
### 3. Defer Until Topology Decisions Are Final
These are real migration boundaries, but removing them prematurely would create
churn without simplifying the current runtime.
- `workspaces/` design-time registry versus `runs/<run_id>/` runtime state
- env-dependent service fallback behavior
- checked-in deployment docs centered on `backend.main`
- dual OpenClaw shapes: gateway integration and REST facade
Recommended next action:
- revisit these only after production topology and service-routing policy are
frozen.

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# Current Architecture
This file describes the current code-supported architecture only. Historical
paths and partial migrations are intentionally excluded unless called out as
legacy compatibility.
Reference material:
- visual diagram: [current-architecture.excalidraw](./current-architecture.excalidraw)
- next-step roadmap: [development-roadmap.md](./development-roadmap.md)
- legacy inventory: [legacy-inventory.md](./legacy-inventory.md)
- terminology guide: [terminology.md](./terminology.md)
## Runtime Modes
The system supports two distinct runtime modes:
### Standalone Mode (Legacy Compatibility)
Direct Gateway startup via `backend.main` as a monolithic entrypoint.
```bash
python -m backend.main --mode live --port 8765
```
**Characteristics:**
- Single process runs Gateway, Pipeline, Market Service, and Scheduler
- No service discovery or process management
- Suitable for single-node deployments and quick testing
- All components share the same memory space
**Use cases:**
- Quick local testing without service orchestration
- Single-node production deployments
- Backward compatibility with legacy startup scripts
### Microservice Mode (Default for Development)
Split-service architecture with dedicated runtime_service managing the Gateway lifecycle.
```bash
./start-dev.sh # Starts all services including runtime_service and Gateway
```
**Characteristics:**
- `runtime_service` (:8003) acts as Gateway Process Manager
- Gateway runs as a subprocess managed by runtime_service
- Clear separation between Control Plane (runtime_service) and Data Plane (Gateway)
- Service discovery via environment variables
- Independent scaling and deployment of each service
**Use cases:**
- Local development with hot-reload
- Multi-node deployments
- Production environments requiring service isolation
## Mode Comparison
| Aspect | Standalone Mode | Microservice Mode |
|--------|-----------------|-------------------|
| **Entry point** | `python -m backend.main` | `./start-dev.sh` or individual services |
| **Process model** | Single monolithic process | Multiple specialized processes |
| **Gateway management** | Self-contained | Managed by runtime_service |
| **Service discovery** | None (in-process) | Environment variable based |
| **Hot reload** | Full restart required | Per-service reload |
| **Scaling** | Vertical only | Horizontal possible |
| **Complexity** | Lower | Higher |
| **Use case** | Testing, simple deployments | Development, production |
## Default Runtime Shape (Microservice Mode)
The active runtime path is:
`frontend -> frontend_service proxy or direct split-service calls -> runtime_service/control APIs -> gateway subprocess -> market/pipeline/storage`
Current service surfaces:
- `backend.apps.agent_service` on `:8000`
- control plane for workspaces, agents, skills, approvals
- `backend.apps.trading_service` on `:8001`
- read-only trading data APIs
- `backend.apps.news_service` on `:8002`
- read-only explain/news APIs
- `backend.apps.runtime_service` on `:8003`
- runtime lifecycle and gateway process management
- `backend.apps.openclaw_service` on `:8004`
- optional OpenClaw REST facade
- gateway WebSocket on `:8765`
- live feed/event transport and pipeline coordination
### Control Plane vs Data Plane
**Control Plane (runtime_service :8003):**
- Gateway lifecycle management (start/stop/restart)
- Runtime configuration and bootstrap
- Process health monitoring
- Run history and state snapshots
**Data Plane (Gateway :8765):**
- WebSocket event streaming
- Market data ingestion
- Pipeline execution (analysis -> decision -> execution)
- Real-time trading operations
## Runtime Data Layout
The canonical runtime data root is:
- `runs/<run_id>/`
Important files under each run:
- `runs/<run_id>/BOOTSTRAP.md`
- machine-readable front matter plus run-scoped prompt body
- `runs/<run_id>/agents/<agent_id>/`
- run-scoped agent workspace files and active/local skills
- `runs/<run_id>/state/runtime_state.json`
- runtime snapshot
- `runs/<run_id>/state/server_state.json`
- server-side state (portfolio, trades, market data)
- `runs/<run_id>/team_dashboard/*.json`
- compatibility/export layer for dashboard consumers
- can be disabled in controlled environments via `ENABLE_DASHBOARD_COMPAT_EXPORTS=false`
## Workspace Terms
Two similarly named concepts still exist in the repository:
- `workspaces/`
- design-time registry and CRUD surface exposed by `agent_service`
- `runs/<run_id>/`
- actual runtime state, agent assets, skills, bootstrap config, and logs
When reading current runtime code, prefer `runs/<run_id>/` as the source of
truth. The `workspaces/` registry is not the default execution path.
## Skill Sandbox Execution
Skill scripts (analysis tools, valuation reports) can be executed in multiple
sandbox modes via `backend/tools/sandboxed_executor.py`:
| Mode | Backend Class | Description |
|------|---------------|-------------|
| `none` | `NoSandboxBackend` | Direct module import and execution (default, development only) |
| `docker` | `DockerSandboxBackend` | Docker container isolation with resource limits |
| `kubernetes` | `KubernetesSandboxBackend` | Kubernetes Pod isolation (reserved interface) |
Environment configuration:
```bash
SKILL_SANDBOX_MODE=none # none | docker | kubernetes
SKILL_SANDBOX_IMAGE=python:3.11-slim
SKILL_SANDBOX_MEMORY_LIMIT=512m
SKILL_SANDBOX_CPU_LIMIT=1.0
SKILL_SANDBOX_NETWORK=none
SKILL_SANDBOX_TIMEOUT=60
```
The default `none` mode displays a runtime security warning on first execution
as a reminder that scripts run without isolation. Production deployments should
use `docker` mode with appropriate resource limits.
## Migration Roadmap
### Current State
The system is in a transitional state:
1. **Microservice infrastructure is operational** - runtime_service can start/stop Gateway as subprocess
2. **Pipeline logic remains in Gateway** - full Pipeline execution still happens within Gateway process
3. **Standalone mode is preserved** - direct `backend.main` startup for compatibility
### Future Direction
Phase 1: Documentation and startup convergence (active)
- Clarify runtime modes and their use cases
- Unify documentation across all entry points
Phase 2: Runtime model consolidation
- Ensure all runtime state lives under `runs/<run_id>/`
- Remove dependencies on root-level legacy directories
Phase 3: Pipeline decomposition (planned)
- Extract Pipeline stages into independent services
- Gateway becomes a thin event router
- runtime_service evolves into full orchestrator
Phase 4: Standalone mode deprecation (future)
- Remove direct `backend.main` entry point
- All deployments use microservice mode
## Legacy Compatibility
These items still exist, but they are not the recommended source of truth for
new development:
- root-level runtime data directories such as `live/`, `production/`, `backtest/`
- direct `backend.main` startup as the primary development path
The current runtime still creates legacy `AnalystAgent` / `RiskAgent` /
`PMAgent` instances directly. EvoAgent remains an in-progress migration target,
not the default execution path.

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# Development Roadmap
This roadmap describes the next engineering steps based on the current
code-supported architecture, not on historical compatibility layers.
The current architecture source of truth is
[current-architecture.md](./current-architecture.md). The matching visual
diagram lives in [current-architecture.excalidraw](./current-architecture.excalidraw).
## Guiding Principle
The repo should converge on one clear runtime model:
`split services + gateway + run-scoped runtime state under runs/<run_id>/`
That means future work should reduce ambiguity between:
- design-time `workspaces/`
- runtime `runs/<run_id>/`
- compatibility gateway paths
- older root-level runtime directories
## Phase 1: Documentation And Startup Convergence
Goal: make the supported system shape unambiguous for contributors and operators.
Planned work:
- keep `docs/current-architecture.md` as the primary architecture fact source
- keep `docs/current-architecture.excalidraw` aligned with code changes
- make README, service docs, and deploy docs point to the same runtime model
- explicitly describe `agent_service`, `runtime_service`, `trading_service`,
`news_service`, gateway, and OpenClaw boundaries
- remove or mark statements that imply `workspaces/` is the runtime source of truth
Definition of done:
- a new contributor can identify the supported local startup path in under five minutes
- architecture wording is consistent across top-level docs
## Phase 2: Runtime Model Consolidation
Goal: ensure the runtime state model is centered on `runs/<run_id>/`.
Planned work:
- review remaining reads and writes that still assume root-level `live/`,
`backtest/`, or `production/` directories are canonical
- keep compatibility exports such as `team_dashboard/*.json`, but document them
as exports rather than primary state
- continue moving runtime metadata, assets, and bootstrap configuration behind
run-scoped helpers
- keep the control plane and runtime APIs conceptually separate
Definition of done:
- run-scoped helpers are the default path for runtime state access
- compatibility directories are no longer required for normal development
## Phase 3: Compatibility Surface Reduction
Goal: preserve only intentional compatibility layers.
Planned work:
- identify startup scripts and deploy artifacts that still center on
`backend.main` as a monolithic entrypoint
- classify compatibility surfaces into:
- stable and intentional
- temporary and shrinking
- removable once replacements are fully active
- reduce env-dependent fallback ambiguity for read-only service routing where practical
- document the difference between OpenClaw WebSocket integration and the optional REST facade
Definition of done:
- compatibility surfaces have explicit ownership
- the repo no longer mixes migration leftovers with recommended defaults
## Phase 4: EvoAgent Runtime Cutover
Goal: move from selective EvoAgent rollout to a cleaner default runtime path.
Planned work:
- continue supporting staged rollout through explicit agent selection
- close functional gaps that still require falling back to legacy
analyst/risk/PM implementations
- keep run-scoped workspace assets and prompt reload behavior aligned between
legacy and EvoAgent paths
- avoid reintroducing generic workspace-loading shortcuts on the pipeline layer
Definition of done:
- EvoAgent selection is predictable, test-backed, and no longer treated as an
experimental side path for the supported roles
## Phase 5: Contract Tests And Operational Confidence
Goal: increase confidence that the split-service architecture remains coherent.
Planned work:
- expand service-surface tests around `runtime_service`, `trading_service`,
`news_service`, and migration boundaries
- keep smoke coverage for staged EvoAgent runtime startup
- add validation around docs/script consistency where low-cost checks are possible
- tighten deploy docs so checked-in production examples are clearly described as
either compatibility topology or first-class topology
Definition of done:
- service boundaries are testable and understandable without tracing legacy code
- startup, deploy, and smoke paths tell the same story
## Immediate Focus
The next practical priority order should be:
1. documentation and startup convergence
2. runtime model consolidation around `runs/<run_id>/`
3. compatibility surface reduction
4. EvoAgent runtime cutover
5. broader contract and smoke confidence

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# Legacy Inventory
This file records the major legacy or compatibility-oriented surfaces that still
exist in the repository.
It is not a deletion plan by itself. Its purpose is to separate:
- current source-of-truth runtime paths
- intentional compatibility surfaces
- historical directories and scripts that should not guide new development
## Source Of Truth
These are the current defaults to build against:
- `runs/<run_id>/`
- runtime state, bootstrap configuration, agent runtime assets, logs
- split services
- `backend.apps.agent_service` on `:8000`
- `backend.apps.runtime_service` on `:8003`
- `backend.apps.trading_service` on `:8001`
- `backend.apps.news_service` on `:8002`
- gateway process
- `backend.main`
- `backend.services.gateway` on `:8765`
## Compatibility Surface Classification
All compatibility surfaces are categorized into three buckets:
### 1. Stable and Intentional (Keep)
These have clear operational reasons to exist and are documented as intentional
compatibility surfaces with explicit ownership.
| Surface | Location | Owner | Reason |
|---------|----------|-------|--------|
| Gateway-first production | `scripts/run_prod.sh`, `deploy/systemd/`, `deploy/nginx/` | ops-team | Current production example runs gateway directly and proxies `/ws` |
| Dashboard export layer | `runs/<run_id>/team_dashboard/*.json` | frontend-team | Downstream dashboard consumers read these exports |
| Design-time workspace registry | `workspaces/`, `backend.api.workspaces` | control-plane-team | Control-plane editing and registry-style management |
| Gateway WebSocket transport | `backend.services.gateway` on `:8765` | runtime-team | Live event streaming contract for frontend |
**Status**: These are NOT migration leftovers. Do not remove without explicit
replacement plan signed off by owning team.
### 2. Temporary and Shrinking (Mark for Removal)
These should not grow further. Keep only until concrete replacement is fully
in use.
| Surface | Location | Replacement | ETA |
|---------|----------|-------------|-----|
| Legacy analyst agents | `backend.agents.analyst.*` | `EvoAgent` | After EvoAgent smoke tests pass |
| Mixed workspace_id semantics | `/api/workspaces/{id}/agents/...` | Explicit `run_id` vs `workspace_id` routes | TBD |
| Root-level runtime directories | `live/`, `backtest/`, `production/` | `runs/<run_id>/` | Already deprecated, safe to ignore |
**Status**: Do not add new code using these surfaces. Migrate existing usage
when touching related code.
### 3. Deferred Until Topology Final (Revisit Later)
These are real migration boundaries, but removing them prematurely would create
churn without simplifying the current runtime. Revisit only after production
topology and service-routing policy are frozen.
| Surface | Current State | Decision Needed |
|---------|---------------|-----------------|
| OpenClaw dual integration | REST facade (`:8004`) + Gateway WebSocket (`:18789`) | Which surface is the long-term contract? |
| Env-dependent service fallbacks | `TRADING_SERVICE_URL`, `NEWS_SERVICE_URL` fallbacks to local modules | Remove fallbacks and require explicit URLs? |
| Split-service production deploy | Docs show gateway-first, dev uses split-service | Align production with dev topology? |
**Status**: Document current behavior. Do not actively remove until topology
decisions are finalized.
## Detailed Surface Documentation
### Gateway-First Production Example
**Files**:
- `scripts/run_prod.sh` - Production launch script
- `deploy/systemd/evotraders.service` - systemd unit
- `deploy/nginx/bigtime.cillinn.com.conf` - HTTPS + WebSocket proxy
- `deploy/nginx/bigtime.cillinn.com.http.conf` - HTTP variant
**Behavior**:
```bash
# scripts/run_prod.sh launches:
python3 -m backend.main \
--mode live \
--config-name production \
--host 127.0.0.1 \
--port 8765
```
**nginx proxies**:
- `/ws` -> `127.0.0.1:8765` (WebSocket upgrade)
- `/` -> static files in `/var/www/bigtime/current`
**Why this exists**:
- Simpler production deployment (single process + nginx)
- WebSocket is the practical live event contract for frontend
- Split-service topology adds operational complexity not needed for all deployments
**Ownership**: ops-team
**Status**: Stable and intentional
### OpenClaw Dual Integration
Two different integration surfaces exist for OpenClaw:
#### A. REST Facade (Port 8004)
**File**: `backend/apps/openclaw_service.py`
**Routes**: `backend/api/openclaw.py` (prefix `/api/openclaw`)
**Purpose**:
- Read-only OpenClaw CLI integration
- Typed Pydantic models for all responses
- Direct HTTP/REST access to OpenClaw state
**Use when**:
- You need typed, stable API contracts
- You want to poll OpenClaw status from external systems
- You need programmatic access without WebSocket complexity
**Example**:
```bash
curl http://localhost:8004/api/openclaw/status
```
#### B. Gateway WebSocket Integration (Port 18789)
**Files**:
- `backend/services/gateway_openclaw_handlers.py`
- `shared/client/openclaw_websocket_client.py`
**Purpose**:
- Real-time bidirectional communication with OpenClaw
- Event streaming and live updates
- Integration with Gateway event flow
**Use when**:
- You need real-time updates
- You're already connected to Gateway WebSocket
- You want event-driven rather than polling architecture
**Example**:
```javascript
// Frontend connects to ws://localhost:18789
const ws = new WebSocket('ws://localhost:18789');
```
#### Key Differences
| Aspect | REST Facade (8004) | Gateway WebSocket (18789) |
|--------|-------------------|---------------------------|
| Protocol | HTTP/REST | WebSocket |
| Access pattern | Request/response | Event-driven |
| Typing | Pydantic models | JSON messages |
| Real-time | Polling required | Push notifications |
| Use case | External integrations, scripts | Frontend, live dashboards |
| Stability | Higher (explicit contracts) | Evolving with Gateway |
**Decision needed**: Which surface becomes the long-term contract?
- REST facade is more stable but read-only
- WebSocket integration is more capable but tied to Gateway evolution
**Ownership**: runtime-team
**Status**: Deferred until topology final
### Dashboard Export Layer
**Files**: `runs/<run_id>/team_dashboard/*.json`
**Purpose**:
- Compatibility/export layer for dashboard consumers
- Non-authoritative snapshot of runtime state
- Can be disabled via `ENABLE_DASHBOARD_COMPAT_EXPORTS=false`
**Why not remove**:
- Downstream consumers still read these files
- Provides decoupling between runtime and dashboard
**Ownership**: frontend-team
**Status**: Stable and intentional
### Design-Time Workspace Registry
**Files**:
- `workspaces/` directory
- `backend/api/workspaces.py`
- `backend/agents/workspace_manager.py`
**Purpose**:
- Control-plane editing and registry-style management
- Design-time CRUD for agent workspaces
- Separate from runtime state in `runs/<run_id>/`
**Key distinction**:
- `workspaces/` = design-time registry (what agents *could* be)
- `runs/<run_id>/` = runtime state (what agents *are* right now)
**Ownership**: control-plane-team
**Status**: Stable and intentional
## Historical Or High-Risk-To-Misread Surfaces
These remain in the tree, but they should not define the architecture for new work.
### Root-level runtime directories
- `live/`
- `backtest/`
- `production/`
**Read**:
- treat these as historical or compatibility-oriented data/layout artifacts
- do not use them as the default runtime contract for new features
### Mixed `workspace_id` semantics on agent routes
- `/api/workspaces/{workspace_id}/agents/...`
**Read**:
- design-time CRUD routes use `workspace_id` as a registry workspace id
- profile, skills, and editable file routes use `workspace_id` as a run id
**Mitigation already in repo**:
- `agent_service /api/status` exposes scope metadata
- runtime-read responses expose `scope_type` and `scope_note`
### Partial EvoAgent rollout
- `EVO_AGENT_IDS`
- staged smoke coverage in `scripts/smoke_evo_runtime.py`
**Read**:
- EvoAgent is still a controlled rollout path
- legacy analyst/risk/PM implementations remain the default runtime path for now
## Recommended Usage
When in doubt:
1. trust `docs/current-architecture.md`
2. trust `runs/<run_id>/` over root-level runtime directories
3. treat `workspaces/` as control-plane registry, not runtime truth
4. treat deploy artifacts as the current checked-in example, not the full system contract
5. check this file's **Compatibility Surface Classification** before assuming something is legacy
## Change Log
| Date | Change |
|------|--------|
| 2026-03-31 | Added Compatibility Surface Classification (3 buckets) |
| 2026-03-31 | Documented OpenClaw dual integration (REST vs WebSocket) |
| 2026-03-31 | Added ownership and status to all surfaces |

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# Runtime Service API 变更文档
## 概述
本文档描述了 `runtime_service` API 的改进，包括新增端点、增强的响应字段和改进的错误处理。
## 新增端点
### 1. GET /api/runtime/mode
返回当前运行模式（实盘或回测）及相关配置。
**响应模型**: `RuntimeModeResponse`
```json
{
"mode": "live",
"is_backtest": false,
"run_id": "20250401_120000",
"schedule_mode": "daily",
"is_running": true
}
```
**字段说明**:
- `mode`: 运行模式，`"live"`（实盘）或 `"backtest"`（回测），运行时停止时为 `"stopped"`
- `is_backtest`: 是否为回测模式
- `run_id`: 当前运行的任务 ID
- `schedule_mode`: 调度模式，`"daily"` 或 `"intraday"`
- `is_running`: Gateway 是否正在运行
---
### 2. GET /api/runtime/gateway/health
全面的 Gateway 健康检查，包括进程状态、端口连通性和配置状态。
**响应模型**: `GatewayHealthResponse`
```json
{
"status": "healthy",
"checks": {
"process": {
"status": "healthy",
"details": {
"pid": 12345,
"status": "running",
"returncode": null
}
},
"port": {
"status": "healthy",
"details": {
"port": 8765,
"accessible": true
}
},
"configuration": {
"status": "healthy",
"details": {
"has_runtime_manager": true
}
}
},
"timestamp": "2025-04-01T12:00:00.000000"
}
```
**状态说明**:
- `status`: 整体健康状态，`"healthy"`（健康）、`"degraded"`（降级）或 `"unhealthy"`（不健康）
- `checks.process.status`: 进程状态
- `checks.port.status`: 端口连通性
- `checks.configuration.status`: 配置状态
---
### 3. GET /health/gateway
服务级别的 Gateway 健康检查端点。
**响应示例**:
```json
{
"status": "healthy",
"checks": {
"process": {
"status": "healthy",
"details": {
"pid": 12345,
"status": "running",
"returncode": null
}
},
"port": {
"status": "healthy",
"details": {
"port": 8765,
"accessible": true
}
},
"configuration": {
"status": "healthy",
"details": {
"has_runtime_manager": true
}
}
},
"timestamp": "2025-04-01T12:00:00.000000"
}
```
---
## 改进的端点
### GET /api/runtime/gateway/status
**新增字段**:
- `process_status`: 进程状态（`"running"`、`"exited"`、`"not_running"`）
- `pid`: 进程 ID
**响应示例**:
```json
{
"is_running": true,
"port": 8765,
"run_id": "20250401_120000",
"process_status": "running",
"pid": 12345
}
```
---
### GET /health
**改进的响应结构**:
```json
{
"status": "healthy",
"service": "runtime-service",
"gateway": {
"running": true,
"port": 8765,
"pid": 12345,
"process_status": "running",
"returncode": null
}
}
```
**字段说明**:
- `status`: 服务整体状态（考虑 Gateway 进程状态）
- `gateway.running`: Gateway 是否运行中
- `gateway.pid`: Gateway 进程 ID
- `gateway.process_status`: 进程详细状态
- `gateway.returncode`: 进程退出码（如已退出）
---
### GET /api/status
**新增字段**:
- `runtime.gateway_pid`: Gateway 进程 ID
- `runtime.gateway_process_status`: 进程状态
**响应示例**:
```json
{
"status": "operational",
"service": "runtime-service",
"runtime": {
"gateway_running": true,
"gateway_port": 8765,
"gateway_pid": 12345,
"gateway_process_status": "running",
"has_runtime_manager": true
}
}
```
---
### POST /api/runtime/start
**改进的错误信息**:
启动失败时返回详细的错误信息，包括：
- 进程退出码
- 最近的日志输出（最多 4000 字符）
- 配置问题检测
**错误响应示例**:
```json
{
"detail": "Gateway process exited unexpectedly\nExit code: 1\nRecent log output:\n[ERROR] FINNHUB_API_KEY not set...\nConfiguration issues detected: FINNHUB_API_KEY environment variable is required for live mode"
}
```
---
### POST /api/runtime/stop
**改进的错误信息**:
- 当 Gateway 进程已退出时，返回包含退出码和 PID 的详细信息
- 停止失败时返回具体原因
**错误响应示例（进程已退出）**:
```json
{
"detail": "No runtime is currently running. Previous Gateway process exited with code 1. PID: 12345"
}
```
**成功响应**:
```json
{
"status": "stopped",
"message": "Runtime stopped successfully (PID: 12345)"
}
```
---
## 配置验证
### 启动时验证
Gateway 启动前会自动验证以下配置：
1. **模式验证**
- `mode` 必须是 `"live"` 或 `"backtest"`
2. **环境变量**
- 实盘模式需要 `FINNHUB_API_KEY`
- 需要 `MODEL_NAME` 和 `OPENAI_API_KEY`
3. **股票池**
- `tickers` 不能为空且必须是列表
4. **数值验证**
- `initial_cash` 必须大于 0
- `margin_requirement` 必须在 0-1 之间
5. **回测日期**
- `start_date` 和 `end_date` 格式必须为 `YYYY-MM-DD`
- `start_date` 必须早于 `end_date`
6. **调度模式**
- `schedule_mode` 必须是 `"daily"` 或 `"intraday"`
**验证失败响应**:
```json
{
"detail": "Gateway configuration validation failed: FINNHUB_API_KEY environment variable is required for live mode; initial_cash must be greater than 0"
}
```
---
## 数据模型
### GatewayStatusResponse
```python
class GatewayStatusResponse(BaseModel):
is_running: bool
port: int
run_id: Optional[str] = None
process_status: Optional[str] = None # 新增
pid: Optional[int] = None # 新增
```
### GatewayHealthResponse
```python
class GatewayHealthResponse(BaseModel):
status: str
checks: Dict[str, Any]
timestamp: str
```
### RuntimeModeResponse
```python
class RuntimeModeResponse(BaseModel):
mode: str
is_backtest: bool
run_id: Optional[str] = None
schedule_mode: Optional[str] = None
is_running: bool
```
---
## 架构改进
### 新增辅助函数
1. **`_validate_gateway_config(bootstrap)`**
- 验证 Gateway 启动配置
- 返回验证错误列表
2. **`_get_gateway_process_details()`**
- 获取 Gateway 进程详细信息
- 包括 PID、状态、退出码
3. **`_check_gateway_health()`**
- 执行全面的健康检查
- 检查进程、端口、配置
---
## 向后兼容性
所有改进都保持向后兼容：
- 现有端点继续工作
- 新增字段为可选
- 错误响应格式保持不变（仅在 detail 中提供更详细信息）

79
docs/terminology.md Normal file
View File

@@ -0,0 +1,79 @@
# Terminology
Use these terms consistently when changing code, docs, or UI.
## Core Terms
### `design-time`
Use for configuration, editing, and control-plane concepts that exist before a
specific runtime is launched.
Typical examples:
- `workspaces/`
- workspace registry CRUD
- design-time agent metadata
### `runtime`
Use for the active execution layer and its state.
Typical examples:
- runtime lifecycle APIs
- scheduler / gateway execution
- approvals during a live run
- runtime snapshots and logs
### `run`
Use for one concrete execution instance.
Typical examples:
- `runs/<run_id>/`
- runtime history
- run logs
- run bootstrap config
- run-scoped agent assets
### `workspace`
Prefer this word only for the design-time registry unless you are working on a
historical compatibility surface that still uses the old path or field name.
Examples:
- good: "design workspace"
- good: "workspace registry"
- avoid for new runtime UI: "current workspace" when you really mean current run
## Compatibility Rule
Some API paths and fields still use legacy names:
- `/api/workspaces/{workspace_id}/agents/...`
- `workspace_id` on approval records
When reading those surfaces:
- design-time CRUD routes use `workspace_id` literally
- runtime-read routes may use the same slot for `run_id`
For new code:
- prefer `runId` for runtime variables
- prefer `workspaceId` only for design-time registry flows
## UI Wording
For operator-facing runtime UI, prefer:
- "运行任务"
- "运行文件"
- "运行资产"
- "任务 ID"
Avoid using "工作区" for active runtime concepts unless the screen is truly
about the design-time workspace registry.

13
env.template
View File

@@ -55,6 +55,19 @@ AGENT_PORTFOLIO_MANAGER_MODEL_NAME=qwen3-max-preview
# ================== Advanced Configuration | 高阶配置 ==================
# Skill Sandbox Mode | 技能沙盒执行模式
# none = direct execution (default, development only) | 直接执行（默认，仅开发环境）
# docker = Docker container isolation | Docker 容器隔离
# kubernetes = Kubernetes Pod isolation (reserved) | Kubernetes Pod 隔离（预留）
SKILL_SANDBOX_MODE=none
# Docker Sandbox Settings (only used when SKILL_SANDBOX_MODE=docker) | Docker 沙盒配置
SKILL_SANDBOX_IMAGE=python:3.11-slim
SKILL_SANDBOX_MEMORY_LIMIT=512m
SKILL_SANDBOX_CPU_LIMIT=1.0
SKILL_SANDBOX_NETWORK=none
SKILL_SANDBOX_TIMEOUT=60
# Maximum conference discussion cycles (default: 2) | 最大会议讨论轮数（默认：2）
MAX_COMM_CYCLES=2

3
frontend/package.json
View File

@@ -67,6 +67,7 @@
"typescript": "^5.9.2",
"vite": "^7.1.2",
"vite-tsconfig-paths": "^5.1.4",
"vitest": "^4.1.0"
"vitest": "^4.1.0",
"yaml": "^2.8.3"
}
}

55
frontend/src/App.jsx
View File

@@ -9,7 +9,7 @@ import { useRuntimeControls } from './hooks/useRuntimeControls';
import { useStockDataRequests } from './hooks/useStockDataRequests';
import { useWebSocketConnection } from './hooks/useWebSocketConnection';
import { fetchRuntimeLogs } from './services/runtimeApi';
import { useAgentStore } from './store/agentStore';
import { useAgentRunFileState, useAgentStore } from './store/agentStore';
import { useMarketStore } from './store/marketStore';
import { usePortfolioStore } from './store/portfolioStore';
import { useRuntimeStore } from './store/runtimeStore';
@@ -82,17 +82,20 @@ export default function LiveTradingApp() {
skillDetailLoadingKey,
agentSkillsSavingKey,
agentSkillsFeedback,
selectedWorkspaceFile,
workspaceFilesByAgent,
workspaceDraftContent,
isWorkspaceFileLoading,
workspaceFileSavingKey,
workspaceFileFeedback,
setSelectedWorkspaceFile,
setSelectedSkillAgentId,
setWorkspaceDraftContent,
} = useAgentStore();
const {
selectedRunFile,
runFilesByAgent,
runDraftContent,
isRunFileLoading,
runFileSavingKey,
runFileFeedback,
setRunDraftContent,
} = useAgentRunFileState();
const { feed, processHistoricalFeed, processFeedEvent, addSystemMessage, clearFeed } = useFeedProcessor();
const resetRuntimeViewState = useCallback(() => {
clearFeed();
@@ -177,8 +180,8 @@ export default function LiveTradingApp() {
const selectedAgentId = selectedSkillAgentId || AGENTS[0]?.id || null;
const selectedAgentProfile = selectedAgentId ? (agentProfilesByAgent[selectedAgentId] || null) : null;
const selectedAgentSkills = selectedAgentId ? (agentSkillsByAgent[selectedAgentId] || []) : [];
const selectedWorkspaceContent = selectedAgentId && selectedWorkspaceFile
? (workspaceFilesByAgent[selectedAgentId]?.[selectedWorkspaceFile] || '')
const selectedRunFileContent = selectedAgentId && selectedRunFile
? (runFilesByAgent[selectedAgentId]?.[selectedRunFile] || '')
: '';
useEffect(() => {
@@ -188,10 +191,10 @@ export default function LiveTradingApp() {
}, [selectedSkillAgentId, setSelectedSkillAgentId]);
useEffect(() => {
if (!selectedWorkspaceFile) {
if (!selectedRunFile) {
setSelectedWorkspaceFile('MEMORY.md');
}
}, [selectedWorkspaceFile, setSelectedWorkspaceFile]);
}, [selectedRunFile, setSelectedWorkspaceFile]);
useEffect(() => {
if (!isSocketReady || !selectedAgentId || !clientRef.current) {
@@ -207,10 +210,10 @@ export default function LiveTradingApp() {
}
if (
selectedWorkspaceFile
&& workspaceFilesByAgent[selectedAgentId]?.[selectedWorkspaceFile] === undefined
selectedRunFile
&& runFilesByAgent[selectedAgentId]?.[selectedRunFile] === undefined
) {
requestWorkspaceFile(selectedAgentId, selectedWorkspaceFile);
requestWorkspaceFile(selectedAgentId, selectedRunFile);
}
}, [
agentProfilesByAgent,
@@ -221,8 +224,8 @@ export default function LiveTradingApp() {
requestAgentSkills,
requestWorkspaceFile,
selectedAgentId,
selectedWorkspaceFile,
workspaceFilesByAgent,
selectedRunFile,
runFilesByAgent,
]);
useEffect(() => {
@@ -361,7 +364,7 @@ export default function LiveTradingApp() {
agents: AGENTS,
agentProfilesByAgent,
agentSkillsByAgent,
workspaceFilesByAgent,
runFilesByAgent,
selectedAgentId,
selectedAgentProfile,
selectedAgentSkills,
@@ -369,16 +372,16 @@ export default function LiveTradingApp() {
localSkillDraftsByKey,
skillDetailLoadingKey,
editableFiles: EDITABLE_AGENT_WORKSPACE_FILES,
selectedWorkspaceFile,
workspaceFileContent: selectedWorkspaceContent,
workspaceDraftContent,
selectedRunFile,
runFileContent: selectedRunFileContent,
runDraftContent,
isConnected,
isAgentSkillsLoading,
agentSkillsSavingKey,
agentSkillsFeedback,
isWorkspaceFileLoading,
workspaceFileSavingKey,
workspaceFileFeedback,
isRunFileLoading,
runFileSavingKey,
runFileFeedback,
onAgentChange: handleSkillAgentChange,
onCreateLocalSkill: handleCreateLocalSkill,
onSkillDetailRequest: requestSkillDetail,
@@ -388,8 +391,8 @@ export default function LiveTradingApp() {
onRemoveSharedSkill: handleRemoveSharedSkill,
onSkillToggle: handleAgentSkillToggle,
onWorkspaceFileChange: handleWorkspaceFileChange,
onWorkspaceDraftChange: setWorkspaceDraftContent,
onWorkspaceFileSave: handleWorkspaceFileSave,
onRunDraftChange: setRunDraftContent,
onRunFileSave: handleWorkspaceFileSave,
onUploadExternalSkill: handleUploadExternalSkill,
clientRef,
};

2
frontend/src/components/RuntimeSettingsPanel.jsx
View File

@@ -208,7 +208,7 @@ export default function RuntimeSettingsPanel({
background: '#FFFFFF',
border: '1px dashed #D0D7DE'
}}>
恢复启动会从所选历史任务复制运行状态、组合、交易记录和 Agent 工作区资产，并以新的任务 ID 继续运行。
恢复启动会从所选历史任务复制运行状态、组合、交易记录和 Agent 运行资产，并以新的任务 ID 继续运行。
</div>
</>
)}

8
frontend/src/components/RuntimeView.jsx
View File

@@ -207,6 +207,12 @@ function formatSessionLabel(sessionId) {
return sessionId || '无会话';
}
function formatApprovalScopeLabel(approval) {
const runId = approval?.run_id || approval?.workspace_id || '-';
const agentId = approval?.agent_id || '-';
return `${agentId} · 运行 ${runId} · ${formatSessionLabel(approval?.session_id)}`;
}
function formatEventLabel(eventName) {
if (!eventName) {
return '-';
@@ -598,7 +604,7 @@ export default function RuntimeView() {
</div>
</div>
<div style={{ fontSize: 11, color: '#6B7280', lineHeight: 1.5 }}>
{approval.agent_id} · {approval.workspace_id} · {formatSessionLabel(approval.session_id)}
{formatApprovalScopeLabel(approval)}
</div>
{approval.tool_input && (
<pre style={{

58
frontend/src/components/TraderView.jsx
View File

@@ -8,7 +8,7 @@ export default function TraderView({
agents,
agentProfilesByAgent,
agentSkillsByAgent,
workspaceFilesByAgent,
runFilesByAgent,
selectedAgentId,
selectedAgentProfile,
selectedAgentSkills,
@@ -16,16 +16,16 @@ export default function TraderView({
localSkillDraftsByKey,
skillDetailLoadingKey,
editableFiles,
selectedWorkspaceFile,
workspaceFileContent,
workspaceDraftContent,
selectedRunFile,
runFileContent,
runDraftContent,
isConnected,
isAgentSkillsLoading,
agentSkillsSavingKey,
agentSkillsFeedback,
isWorkspaceFileLoading,
workspaceFileSavingKey,
workspaceFileFeedback,
isRunFileLoading,
runFileSavingKey,
runFileFeedback,
onAgentChange,
onCreateLocalSkill,
onSkillDetailRequest,
@@ -35,8 +35,8 @@ export default function TraderView({
onRemoveSharedSkill,
onSkillToggle,
onWorkspaceFileChange,
onWorkspaceDraftChange,
onWorkspaceFileSave,
onRunDraftChange,
onRunFileSave,
onUploadExternalSkill
}) {
const srOnlyStyle = {
@@ -133,10 +133,10 @@ export default function TraderView({
}}>
<div style={{ display: 'grid', gap: 4 }}>
<div style={{ fontSize: 12, fontWeight: 800, letterSpacing: '0.5px', color: '#111111' }}>
交易员档案
Agent 运行档案
</div>
<div style={{ fontSize: 11, color: '#6B7280' }}>
聚焦查看每个 Agent 的模型、工具组、技能编排和工作区记忆，不展示交易表现数据
聚焦查看每个 Agent 在当前运行任务中的模型、工具组、技能编排和运行记忆，不展示交易表现数据
</div>
</div>
@@ -549,15 +549,15 @@ export default function TraderView({
gap: 10
}}>
<div style={{ display: 'grid', gap: 4 }}>
<div style={{ fontSize: 12, fontWeight: 800, color: '#111111' }}>工作区文件编辑</div>
<div style={{ fontSize: 12, fontWeight: 800, color: '#111111' }}>运行文件编辑</div>
<div style={{ fontSize: 11, color: '#6B7280' }}>
直接调整该交易员的人设、协作方式和长期记忆文件
直接调整该交易员在当前运行任务中的人设、协作方式和长期记忆文件
</div>
</div>
<div style={{ display: 'flex', flexWrap: 'wrap', gap: 8 }}>
{editableFiles.map((filename) => {
const isActive = filename === selectedWorkspaceFile;
const isActive = filename === selectedRunFile;
return (
<button
key={filename}
@@ -581,12 +581,12 @@ export default function TraderView({
</div>
<textarea
id={`workspace-editor-${selectedAgentId}-${selectedWorkspaceFile || 'file'}`}
name={`workspace_editor_${selectedAgentId}_${selectedWorkspaceFile || 'file'}`}
aria-label={`编辑 ${selectedWorkspaceFile || '工作区文件'} 内容`}
value={workspaceDraftContent}
onChange={(e) => onWorkspaceDraftChange(e.target.value)}
placeholder={isWorkspaceFileLoading ? '加载中...' : '输入 markdown 内容'}
id={`workspace-editor-${selectedAgentId}-${selectedRunFile || 'file'}`}
name={`workspace_editor_${selectedAgentId}_${selectedRunFile || 'file'}`}
aria-label={`编辑 ${selectedRunFile || '运行文件'} 内容`}
value={runDraftContent}
onChange={(e) => onRunDraftChange(e.target.value)}
placeholder={isRunFileLoading ? '加载中...' : '输入 markdown 内容'}
style={{
minHeight: 280,
resize: 'vertical',
@@ -603,33 +603,33 @@ export default function TraderView({
<div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', gap: 10, flexWrap: 'wrap' }}>
<span style={{ fontSize: 10, color: '#6B7280', fontFamily: '"Courier New", monospace' }}>
当前文件: {selectedWorkspaceFile}
当前运行文件: {selectedRunFile}
</span>
<button
onClick={onWorkspaceFileSave}
disabled={!isConnected || isWorkspaceFileLoading || workspaceFileSavingKey !== null || workspaceDraftContent === workspaceFileContent}
onClick={onRunFileSave}
disabled={!isConnected || isRunFileLoading || runFileSavingKey !== null || runDraftContent === runFileContent}
style={{
padding: '9px 14px',
borderRadius: 6,
border: '1px solid #1565C0',
background: isConnected && !isWorkspaceFileLoading && workspaceFileSavingKey === null && workspaceDraftContent !== workspaceFileContent ? '#0D47A1' : '#94A3B8',
background: isConnected && !isRunFileLoading && runFileSavingKey === null && runDraftContent !== runFileContent ? '#0D47A1' : '#94A3B8',
color: '#FFFFFF',
fontSize: 11,
fontWeight: 700,
cursor: isConnected && !isWorkspaceFileLoading && workspaceFileSavingKey === null && workspaceDraftContent !== workspaceFileContent ? 'pointer' : 'not-allowed'
cursor: isConnected && !isRunFileLoading && runFileSavingKey === null && runDraftContent !== runFileContent ? 'pointer' : 'not-allowed'
}}
>
{workspaceFileSavingKey ? '保存中' : '保存文件'}
{runFileSavingKey ? '保存中' : '保存文件'}
</button>
</div>
{workspaceFileFeedback && (
{runFileFeedback && (
<span style={{
color: workspaceFileFeedback.type === 'success' ? '#00C853' : '#FF5252',
color: runFileFeedback.type === 'success' ? '#00C853' : '#FF5252',
fontSize: 11,
fontFamily: '"Courier New", monospace'
}}>
{workspaceFileFeedback.text}
{runFileFeedback.text}
</span>
)}
</div>

72
frontend/src/hooks/useAgentDataRequests.js
View File

@@ -40,13 +40,13 @@ export function useAgentDataRequests(clientRef) {
setIsWorkspaceFileLoading
} = useAgentStore();
const resolveWorkspaceId = useCallback(async () => {
const resolveRunId = useCallback(async () => {
const runtime = await fetchCurrentRuntime();
const workspaceId = runtime?.run_id;
if (!workspaceId) {
const runId = runtime?.run_id;
if (!runId) {
throw new Error('未检测到正在运行的任务');
}
return workspaceId;
return runId;
}, []);
const requestAgentSkills = useCallback((agentId) => {
@@ -54,8 +54,8 @@ export function useAgentDataRequests(clientRef) {
if (!normalized) return false;
setIsAgentSkillsLoading(true);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => fetchAgentSkills(workspaceId, normalized))
void resolveRunId()
.then((runId) => fetchAgentSkills(runId, normalized))
.then((payload) => {
setAgentSkillsByAgent((prev) => ({ ...prev, [normalized]: Array.isArray(payload?.skills) ? payload.skills : [] }));
setIsAgentSkillsLoading(false);
@@ -72,13 +72,13 @@ export function useAgentDataRequests(clientRef) {
}
});
return true;
}, [clientRef, resolveWorkspaceId, setAgentSkillsByAgent, setIsAgentSkillsLoading, setAgentSkillsFeedback]);
}, [clientRef, resolveRunId, setAgentSkillsByAgent, setIsAgentSkillsLoading, setAgentSkillsFeedback]);
const requestAgentProfile = useCallback((agentId) => {
const normalized = typeof agentId === 'string' ? agentId.trim() : '';
if (!normalized) return false;
void resolveWorkspaceId()
.then((workspaceId) => fetchAgentProfile(workspaceId, normalized))
void resolveRunId()
.then((runId) => fetchAgentProfile(runId, normalized))
.then((payload) => {
setAgentProfilesByAgent((prev) => ({
...prev,
@@ -92,15 +92,15 @@ export function useAgentDataRequests(clientRef) {
}
});
return true;
}, [clientRef, resolveWorkspaceId, setAgentProfilesByAgent]);
}, [clientRef, resolveRunId, setAgentProfilesByAgent]);
const requestSkillDetail = useCallback((skillName) => {
const normalized = typeof skillName === 'string' ? skillName.trim() : '';
if (!normalized) return false;
const detailKey = `${selectedSkillAgentId}:${normalized}`;
setSkillDetailLoadingKey(detailKey);
void resolveWorkspaceId()
.then((workspaceId) => fetchAgentSkillDetail(workspaceId, selectedSkillAgentId, normalized))
void resolveRunId()
.then((runId) => fetchAgentSkillDetail(runId, selectedSkillAgentId, normalized))
.then((payload) => {
setSkillDetailsByName((prev) => ({ ...prev, [detailKey]: payload?.skill || null }));
useAgentStore.getState().setLocalSkillDraftsByKey((prev) => ({
@@ -121,7 +121,7 @@ export function useAgentDataRequests(clientRef) {
}
});
return true;
}, [clientRef, resolveWorkspaceId, selectedSkillAgentId, setSkillDetailLoadingKey, setSkillDetailsByName]);
}, [clientRef, resolveRunId, selectedSkillAgentId, setSkillDetailLoadingKey, setSkillDetailsByName]);
const handleCreateLocalSkill = useCallback((skillName) => {
const normalized = typeof skillName === 'string' ? skillName.trim() : '';
@@ -131,8 +131,8 @@ export function useAgentDataRequests(clientRef) {
}
setAgentSkillsSavingKey(`${selectedSkillAgentId}:${normalized}:create`);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => createAgentLocalSkill(workspaceId, selectedSkillAgentId, normalized))
void resolveRunId()
.then((runId) => createAgentLocalSkill(runId, selectedSkillAgentId, normalized))
.then(() => {
setAgentSkillsSavingKey(null);
setAgentSkillsFeedback({ type: 'success', text: `已创建本地技能 ${normalized}` });
@@ -152,7 +152,7 @@ export function useAgentDataRequests(clientRef) {
setAgentSkillsFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, requestAgentSkills, requestSkillDetail, resolveWorkspaceId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
}, [clientRef, requestAgentSkills, requestSkillDetail, resolveRunId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
const handleLocalSkillDraftChange = useCallback((skillName, content) => {
const detailKey = `${selectedSkillAgentId}:${skillName}`;
@@ -165,8 +165,8 @@ export function useAgentDataRequests(clientRef) {
if (typeof content !== 'string') return;
setAgentSkillsSavingKey(`${selectedSkillAgentId}:${skillName}:content`);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => updateAgentLocalSkill(workspaceId, selectedSkillAgentId, skillName, content))
void resolveRunId()
.then((runId) => updateAgentLocalSkill(runId, selectedSkillAgentId, skillName, content))
.then(() => {
setAgentSkillsSavingKey(null);
setAgentSkillsFeedback({ type: 'success', text: `${selectedSkillAgentId} 的本地技能 ${skillName} 已保存` });
@@ -185,13 +185,13 @@ export function useAgentDataRequests(clientRef) {
setAgentSkillsFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, localSkillDraftsByKey, requestSkillDetail, resolveWorkspaceId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
}, [clientRef, localSkillDraftsByKey, requestSkillDetail, resolveRunId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
const handleLocalSkillDelete = useCallback((skillName) => {
setAgentSkillsSavingKey(`${selectedSkillAgentId}:${skillName}:delete`);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => deleteAgentLocalSkill(workspaceId, selectedSkillAgentId, skillName))
void resolveRunId()
.then((runId) => deleteAgentLocalSkill(runId, selectedSkillAgentId, skillName))
.then(() => {
setAgentSkillsSavingKey(null);
setAgentSkillsFeedback({ type: 'success', text: `${selectedSkillAgentId} 的本地技能 ${skillName} 已删除` });
@@ -210,13 +210,13 @@ export function useAgentDataRequests(clientRef) {
setAgentSkillsFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, requestAgentSkills, resolveWorkspaceId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
}, [clientRef, requestAgentSkills, resolveRunId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
const handleRemoveSharedSkill = useCallback((skillName) => {
setAgentSkillsSavingKey(`${selectedSkillAgentId}:${skillName}:remove`);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => disableAgentSkill(workspaceId, selectedSkillAgentId, skillName))
void resolveRunId()
.then((runId) => disableAgentSkill(runId, selectedSkillAgentId, skillName))
.then(() => {
setAgentSkillsSavingKey(null);
setAgentSkillsFeedback({ type: 'success', text: `${selectedSkillAgentId} 已移除共享技能 ${skillName}` });
@@ -235,16 +235,16 @@ export function useAgentDataRequests(clientRef) {
setAgentSkillsFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, requestAgentSkills, resolveWorkspaceId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
}, [clientRef, requestAgentSkills, resolveRunId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
const handleAgentSkillToggle = useCallback((skillName, enabled) => {
const agentId = selectedSkillAgentId;
setAgentSkillsSavingKey(`${agentId}:${skillName}`);
setAgentSkillsFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => enabled
? enableAgentSkill(workspaceId, agentId, skillName)
: disableAgentSkill(workspaceId, agentId, skillName))
void resolveRunId()
.then((runId) => enabled
? enableAgentSkill(runId, agentId, skillName)
: disableAgentSkill(runId, agentId, skillName))
.then(() => {
setAgentSkillsSavingKey(null);
setAgentSkillsFeedback({ type: 'success', text: `${agentId} ${enabled ? '已启用' : '已禁用'} ${skillName}` });
@@ -263,7 +263,7 @@ export function useAgentDataRequests(clientRef) {
setAgentSkillsFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, requestAgentSkills, resolveWorkspaceId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
}, [clientRef, requestAgentSkills, resolveRunId, selectedSkillAgentId, setAgentSkillsFeedback, setAgentSkillsSavingKey]);
const handleSkillAgentChange = useCallback((agentId) => {
setSelectedSkillAgentId(agentId);
@@ -278,8 +278,8 @@ export function useAgentDataRequests(clientRef) {
if (!normalizedAgentId || !normalizedFilename) return false;
setIsWorkspaceFileLoading(true);
setWorkspaceFileFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => fetchAgentWorkspaceFile(workspaceId, normalizedAgentId, normalizedFilename))
void resolveRunId()
.then((runId) => fetchAgentWorkspaceFile(runId, normalizedAgentId, normalizedFilename))
.then((payload) => {
setWorkspaceFilesByAgent((prev) => ({
...prev,
@@ -303,7 +303,7 @@ export function useAgentDataRequests(clientRef) {
}
});
return true;
}, [clientRef, resolveWorkspaceId, setIsWorkspaceFileLoading, setWorkspaceDraftContent, setWorkspaceFileFeedback, setWorkspaceFilesByAgent]);
}, [clientRef, resolveRunId, setIsWorkspaceFileLoading, setWorkspaceDraftContent, setWorkspaceFileFeedback, setWorkspaceFilesByAgent]);
const handleWorkspaceFileChange = useCallback((filename) => {
useAgentStore.getState().setSelectedWorkspaceFile(filename);
@@ -314,8 +314,8 @@ export function useAgentDataRequests(clientRef) {
const key = `${selectedSkillAgentId}:${selectedWorkspaceFile}`;
setWorkspaceFileSavingKey(key);
setWorkspaceFileFeedback(null);
void resolveWorkspaceId()
.then((workspaceId) => updateAgentWorkspaceFile(workspaceId, selectedSkillAgentId, selectedWorkspaceFile, workspaceDraftContent))
void resolveRunId()
.then((runId) => updateAgentWorkspaceFile(runId, selectedSkillAgentId, selectedWorkspaceFile, workspaceDraftContent))
.then((payload) => {
setWorkspaceFileSavingKey(null);
setWorkspaceFileFeedback({ type: 'success', text: `${selectedSkillAgentId} 的 ${selectedWorkspaceFile} 已保存` });
@@ -345,7 +345,7 @@ export function useAgentDataRequests(clientRef) {
setWorkspaceFileFeedback({ type: 'error', text: '发送失败，请检查连接状态' });
}
});
}, [clientRef, resolveWorkspaceId, selectedSkillAgentId, selectedWorkspaceFile, setWorkspaceFileFeedback, setWorkspaceFileSavingKey, setWorkspaceFilesByAgent, workspaceDraftContent]);
}, [clientRef, resolveRunId, selectedSkillAgentId, selectedWorkspaceFile, setWorkspaceFileFeedback, setWorkspaceFileSavingKey, setWorkspaceFilesByAgent, workspaceDraftContent]);
const handleUploadExternalSkill = useCallback(async (file) => {
if (!(file instanceof File)) {

29
frontend/src/hooks/useWebsocketSessionSync.js
View File

@@ -1,29 +0,0 @@
/**
* useWebsocketSessionSync - DEPRECATED
*
* This hook is deprecated. WebSocket connection and event handling is now managed
* by useWebSocketConnection.js. This file is kept for backwards compatibility
* but will be removed in a future version.
*
* All functionality has been consolidated into:
* - useWebSocketConnection.js: WebSocket lifecycle and event handlers
* - useStockDataRequests.js: Stock data request callbacks
* - useAgentDataRequests.js: Agent operation callbacks
*/
import { useWebSocketConnection } from './useWebSocketConnection';
/**
* @deprecated Use useWebSocketConnection directly instead.
* This hook is a thin wrapper that delegates to useWebSocketConnection
* for backwards compatibility.
*/
export function useWebsocketSessionSync(props) {
// Delegate to useWebSocketConnection
const { clientRef } = useWebSocketConnection();
// Return clientRef so existing code can still access it
return { clientRef };
}
export default useWebsocketSessionSync;

54
frontend/src/services/runtimeApi.js
View File

@@ -129,56 +129,64 @@ export function fetchRuntimeLogs() {
return safeFetch(RUNTIME_API_BASE, '/logs');
}
export function fetchAgentProfile(workspaceId, agentId) {
return safeFetch(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/profile`);
function buildRunScopedAgentPath(runId, agentId, suffix = '') {
return `/workspaces/${encodeURIComponent(runId)}/agents/${encodeURIComponent(agentId)}${suffix}`;
}
export function fetchAgentSkills(workspaceId, agentId) {
return safeFetch(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills`);
/**
* Runtime-read agent routes still use the `/workspaces/...` prefix on the
* backend, but the leading identifier on this surface is the active `run_id`.
*/
export function fetchAgentProfile(runId, agentId) {
return safeFetch(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, '/profile'));
}
export function fetchAgentSkillDetail(workspaceId, agentId, skillName) {
return safeFetch(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/${encodeURIComponent(skillName)}`);
export function fetchAgentSkills(runId, agentId) {
return safeFetch(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, '/skills'));
}
export function fetchAgentWorkspaceFile(workspaceId, agentId, filename) {
return safeFetch(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/files/${encodeURIComponent(filename)}`);
export function fetchAgentSkillDetail(runId, agentId, skillName) {
return safeFetch(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/skills/${encodeURIComponent(skillName)}`));
}
export function createAgentLocalSkill(workspaceId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/local`, {
export function fetchAgentWorkspaceFile(runId, agentId, filename) {
return safeFetch(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/files/${encodeURIComponent(filename)}`));
}
export function createAgentLocalSkill(runId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, '/skills/local'), {
method: 'POST',
body: JSON.stringify({ skill_name: skillName })
});
}
export function updateAgentLocalSkill(workspaceId, agentId, skillName, content) {
return safeRequest(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/local/${encodeURIComponent(skillName)}`, {
export function updateAgentLocalSkill(runId, agentId, skillName, content) {
return safeRequest(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/skills/local/${encodeURIComponent(skillName)}`), {
method: 'PUT',
body: JSON.stringify({ content })
});
}
export function deleteAgentLocalSkill(workspaceId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/local/${encodeURIComponent(skillName)}`, {
export function deleteAgentLocalSkill(runId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/skills/local/${encodeURIComponent(skillName)}`), {
method: 'DELETE'
});
}
export function enableAgentSkill(workspaceId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/${encodeURIComponent(skillName)}/enable`, {
export function enableAgentSkill(runId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/skills/${encodeURIComponent(skillName)}/enable`), {
method: 'POST'
});
}
export function disableAgentSkill(workspaceId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, `/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/${encodeURIComponent(skillName)}/disable`, {
export function disableAgentSkill(runId, agentId, skillName) {
return safeRequest(CONTROL_API_BASE, buildRunScopedAgentPath(runId, agentId, `/skills/${encodeURIComponent(skillName)}/disable`), {
method: 'POST'
});
}
export function updateAgentWorkspaceFile(workspaceId, agentId, filename, content) {
return fetch(`${CONTROL_API_BASE}/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/files/${encodeURIComponent(filename)}`, {
export function updateAgentWorkspaceFile(runId, agentId, filename, content) {
return fetch(`${CONTROL_API_BASE}${buildRunScopedAgentPath(runId, agentId, `/files/${encodeURIComponent(filename)}`)}`, {
method: 'PUT',
headers: {
'Content-Type': 'text/plain'
@@ -206,8 +214,8 @@ export async function uploadAgentSkillZip({
throw new Error('valid zip file is required');
}
const runtime = runId ? { run_id: runId } : await fetchCurrentRuntime();
const workspaceId = runtime?.run_id;
if (!workspaceId) {
const resolvedRunId = runtime?.run_id;
if (!resolvedRunId) {
throw new Error('未检测到正在运行的任务');
}
@@ -220,7 +228,7 @@ export async function uploadAgentSkillZip({
return safeRequest(
CONTROL_API_BASE,
`/workspaces/${encodeURIComponent(workspaceId)}/agents/${encodeURIComponent(agentId)}/skills/upload`,
buildRunScopedAgentPath(resolvedRunId, agentId, '/skills/upload'),
{
method: 'POST',
body: formData

45
frontend/src/services/runtimeApi.test.js Normal file
View File

@@ -0,0 +1,45 @@
import { afterEach, describe, expect, it, vi } from 'vitest';
import {
fetchAgentProfile,
updateAgentWorkspaceFile
} from './runtimeApi';
describe('runtimeApi run-scoped agent routes', () => {
afterEach(() => {
vi.restoreAllMocks();
});
it('uses run_id in runtime-read agent profile requests', async () => {
const fetchMock = vi.fn().mockResolvedValue({
ok: true,
json: async () => ({ profile: {}, scope_type: 'runtime_run' })
});
vi.stubGlobal('fetch', fetchMock);
await fetchAgentProfile('20260330_123000', 'portfolio_manager');
expect(fetchMock).toHaveBeenCalledWith(
expect.stringContaining('/workspaces/20260330_123000/agents/portfolio_manager/profile')
);
});
it('uses run_id in runtime agent file update requests', async () => {
const fetchMock = vi.fn().mockResolvedValue({
ok: true,
json: async () => ({ content: '# demo' }),
text: async () => ''
});
vi.stubGlobal('fetch', fetchMock);
await updateAgentWorkspaceFile('20260330_123000', 'risk_manager', 'MEMORY.md', '# demo');
expect(fetchMock).toHaveBeenCalledWith(
expect.stringContaining('/workspaces/20260330_123000/agents/risk_manager/files/MEMORY.md'),
expect.objectContaining({
method: 'PUT',
body: '# demo'
})
);
});
});

18
frontend/src/store/agentStore.js
View File

@@ -5,7 +5,8 @@ const resolveValue = (updater, currentValue) => (
);
/**
* Agent Store - Agent skills, profiles, workspaces
* Agent Store - Agent skills, profiles, design-time workspace terminology, and
* run-scoped file editing state.
*/
export const useAgentStore = create((set) => ({
// Selected agent for skill/workspace editing
@@ -60,3 +61,18 @@ export const useAgentStore = create((set) => ({
workspaceFileFeedback: null,
setWorkspaceFileFeedback: (workspaceFileFeedback) => set((state) => ({ workspaceFileFeedback: resolveValue(workspaceFileFeedback, state.workspaceFileFeedback) })),
}));
/**
* Run-scoped file editing state currently reuses legacy `workspace*` field
* names inside the store. Prefer this selector for new runtime UI code.
*/
export const useAgentRunFileState = () => useAgentStore((state) => ({
selectedRunFile: state.selectedWorkspaceFile,
runFilesByAgent: state.workspaceFilesByAgent,
runDraftContent: state.workspaceDraftContent,
isRunFileLoading: state.isWorkspaceFileLoading,
runFileSavingKey: state.workspaceFileSavingKey,
runFileFeedback: state.workspaceFileFeedback,
setSelectedRunFile: state.setSelectedWorkspaceFile,
setRunDraftContent: state.setWorkspaceDraftContent,
}));

3
pyproject.toml
View File

@@ -55,6 +55,9 @@ dependencies = [
[project.optional-dependencies]
docker-sandbox = [
"agentscope-runtime>=0.1.0"
]
dev = [
"pytest>=8.3.3",
"ruff>=0.6.9",

73
scripts/check-prod-env.sh
View File

@@ -5,6 +5,8 @@
# 用法:
# ./scripts/check-prod-env.sh
# ./scripts/check-prod-env.sh --strict
# ./scripts/check-prod-env.sh --smoke-evo
# ./scripts/check-prod-env.sh --strict --smoke-evo
#
# 检查内容:
# - Python / Node / npm 是否可用
@@ -12,6 +14,7 @@
# - frontend/package-lock.json 与 npm ci 是否可消费
# - .env 是否存在以及关键变量是否配置
# - 前端是否可构建
# - 可选：EvoAgent 运行时 smoke 检查（默认覆盖 fundamentals_analyst + risk_manager + portfolio_manager）
# ============================================================
set -euo pipefail
@@ -22,9 +25,11 @@ CYAN='\033[0;36m'
NC='\033[0m'
STRICT=false
SMOKE_EVO=false
for arg in "$@"; do
case "$arg" in
--strict) STRICT=true ;;
--smoke-evo) SMOKE_EVO=true ;;
*) echo -e "${YELLOW}忽略未知参数: ${arg}${NC}" ;;
esac
done
@@ -34,6 +39,8 @@ PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
cd "${PROJECT_ROOT}"
WARNINGS=0
PYTHON_BIN=""
PROJECT_PYTHONPATH=""
ok() {
echo -e "${GREEN}✔${NC} $1"
@@ -54,8 +61,24 @@ require_cmd() {
command -v "${cmd}" >/dev/null 2>&1 || fail "未找到命令: ${cmd}"
}
resolve_python() {
if command -v python >/dev/null 2>&1; then
PYTHON_BIN="python"
return
fi
if command -v python3 >/dev/null 2>&1; then
PYTHON_BIN="python3"
return
fi
fail "未找到命令: python 或 python3"
}
init_pythonpath() {
PROJECT_PYTHONPATH="${PROJECT_ROOT}/.pydeps:."
}
check_python_modules() {
python - <<'PY'
PYTHONPATH="${PROJECT_PYTHONPATH}" "${PYTHON_BIN}" - <<'PY'
mods = [
'fastapi', 'uvicorn', 'yaml', 'httpx', 'cryptography', 'websockets',
'rich', 'dotenv', 'pandas_market_calendars', 'finnhub', 'openai',
@@ -100,12 +123,13 @@ check_frontend_install() {
[ -f frontend/package-lock.json ] || fail "frontend/package-lock.json 缺失，生产部署建议保留锁文件"
(
cd frontend
npm ci --dry-run >/tmp/bigtime-npm-ci.log 2>&1 || {
cat /tmp/bigtime-npm-ci.log
exit 1
}
npm ci --dry-run >/tmp/bigtime-npm-ci.log 2>&1 || true
)
if rg -n "@emoji-mart/react|@lobehub/ui|ERESOLVE overriding peer dependency" /tmp/bigtime-npm-ci.log >/dev/null 2>&1; then
if rg -n "npm error code EUSAGE|can only install packages when your package.json and package-lock.json.*in sync|Missing: .* from lock file|Invalid: lock file's " /tmp/bigtime-npm-ci.log >/dev/null 2>&1; then
warn "frontend package-lock.json 与 package.json 不一致；需在 frontend/ 重新生成锁文件，但这不阻断当前后端 smoke 检查"
elif rg -n "ERESOLVE could not resolve|Conflicting peer dependency" /tmp/bigtime-npm-ci.log >/dev/null 2>&1; then
warn "frontend npm ci --dry-run 存在 peer 依赖冲突，当前以后续构建结果为准"
elif rg -n "@emoji-mart/react|@lobehub/ui|ERESOLVE overriding peer dependency" /tmp/bigtime-npm-ci.log >/dev/null 2>&1; then
warn "frontend npm ci 存在已知非阻塞 peer warning（@lobehub/icons 依赖链），可忽略"
elif rg -n "npm warn" /tmp/bigtime-npm-ci.log >/dev/null 2>&1; then
warn "frontend npm ci 存在 warning，建议查看 /tmp/bigtime-npm-ci.log"
@@ -125,13 +149,42 @@ check_frontend_build() {
ok "frontend 构建通过"
}
check_evo_runtime_smoke() {
local configured_ids="${EVO_AGENT_IDS:-}"
local -a smoke_agent_ids=()
local raw_id=""
if [ -n "${configured_ids}" ]; then
IFS=',' read -r -a smoke_agent_ids <<< "${configured_ids}"
else
smoke_agent_ids=("fundamentals_analyst" "risk_manager" "portfolio_manager")
fi
for raw_id in "${smoke_agent_ids[@]}"; do
local agent_id
agent_id="$(printf '%s' "${raw_id}" | xargs)"
[ -n "${agent_id}" ] || continue
echo -e "${CYAN}运行 EvoAgent smoke 检查（agent=${agent_id}）${NC}"
PYTHONPATH="${PROJECT_PYTHONPATH}" "${PYTHON_BIN}" \
"${PROJECT_ROOT}/scripts/smoke_evo_runtime.py" \
--agent-id "${agent_id}" >/tmp/bigtime-evo-smoke.log 2>&1 || {
cat /tmp/bigtime-evo-smoke.log
exit 1
}
cat /tmp/bigtime-evo-smoke.log
ok "EvoAgent smoke 检查通过（agent=${agent_id}）"
done
}
echo -e "${CYAN}大时代 · 生产环境检查${NC}"
require_cmd python
resolve_python
init_pythonpath
require_cmd node
require_cmd npm
ok "python: $(python -V 2>&1)"
ok "python: $(${PYTHON_BIN} -V 2>&1)"
ok "node: $(node -v)"
ok "npm: $(npm -v)"
@@ -140,6 +193,10 @@ check_env_file
check_frontend_install
check_frontend_build
if ${SMOKE_EVO}; then
check_evo_runtime_smoke
fi
if [ "${WARNINGS}" -gt 0 ]; then
echo -e "${YELLOW}检查完成：有 ${WARNINGS} 项 warning${NC}"
${STRICT} && exit 1 || exit 0

19
scripts/run_prod.sh
View File

@@ -1,4 +1,12 @@
#!/usr/bin/env bash
# COMPATIBILITY_SURFACE: stable
# OWNER: ops-team
# SEE: docs/legacy-inventory.md#gateway-first-production-example
#
# Gateway-first production launch script.
# This is the current checked-in production example, running the gateway
# directly and proxying /ws instead of exposing every split FastAPI service.
# For split-service topology, see start-dev.sh and docs/current-architecture.md
set -euo pipefail
cd /root/code/evotraders
@@ -6,6 +14,17 @@ cd /root/code/evotraders
export PYTHONPATH=/root/code/evotraders/.pydeps:.
export TICKERS="${TICKERS:-AAPL,MSFT,GOOGL,AMZN,NVDA,META,TSLA,AMD,NFLX,AVGO,PLTR,COIN}"
# 技能沙盒配置（生产环境建议使用 docker）
export SKILL_SANDBOX_MODE="${SKILL_SANDBOX_MODE:-docker}"
export SKILL_SANDBOX_IMAGE="${SKILL_SANDBOX_IMAGE:-python:3.11-slim}"
export SKILL_SANDBOX_MEMORY_LIMIT="${SKILL_SANDBOX_MEMORY_LIMIT:-512m}"
export SKILL_SANDBOX_CPU_LIMIT="${SKILL_SANDBOX_CPU_LIMIT:-1.0}"
export SKILL_SANDBOX_NETWORK="${SKILL_SANDBOX_NETWORK:-none}"
export SKILL_SANDBOX_TIMEOUT="${SKILL_SANDBOX_TIMEOUT:-60}"
# "production" here is an explicit deployment run label, not a required
# root-level runtime directory name.
exec python3 -m backend.main \
--mode live \
--config-name production \

290
scripts/smoke_evo_runtime.py Normal file
View File

@@ -0,0 +1,290 @@
#!/usr/bin/env python3
"""Smoke-test the EvoAgent runtime rollout path.
This script validates the current staged rollout shape:
- start runtime via backend.api.runtime
- confirm the gateway starts on an available port
- confirm the gateway log shows the selected agent running as EvoAgent
- confirm runtime_state.json is written
- confirm guard approval API logic wakes a pending ToolApprovalRequest
It intentionally avoids browser/front-end dependencies and does not require
local HTTP callbacks.
"""
from __future__ import annotations
import argparse
import asyncio
import json
import os
import sys
import time
from pathlib import Path
import websocket
PROJECT_ROOT = Path(__file__).resolve().parents[1]
PYDEPS = PROJECT_ROOT / ".pydeps"
_reordered_sys_path = [
str(PROJECT_ROOT),
str(PYDEPS),
]
for entry in list(sys.path):
if entry in _reordered_sys_path:
continue
_reordered_sys_path.append(entry)
sys.path[:] = _reordered_sys_path
from fastapi import BackgroundTasks
from backend.agents.base.tool_guard import TOOL_GUARD_STORE, ToolApprovalRequest
from backend.api.guard import ApprovalRequest, approve_tool_call
from backend.api.runtime import (
LaunchConfig,
_is_gateway_running,
get_runtime_state,
start_runtime,
stop_runtime,
)
# All 6 agent roles supported by EvoAgent
ALL_EVO_AGENT_ROLES = [
"fundamentals_analyst",
"technical_analyst",
"sentiment_analyst",
"valuation_analyst",
"risk_manager",
"portfolio_manager",
]
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Smoke-test the staged EvoAgent runtime rollout.",
)
parser.add_argument(
"--agent-id",
default="fundamentals_analyst",
help="Agent id to enable via EVO_AGENT_IDS (use 'all' to test all 6 roles)",
)
parser.add_argument(
"--ticker",
default="AAPL",
help="Ticker to include in the smoke runtime bootstrap",
)
parser.add_argument(
"--max-wait-seconds",
type=float,
default=15.0,
help="Maximum time to wait for gateway.log to appear",
)
parser.add_argument(
"--test-all-roles",
action="store_true",
help="Test all 6 EvoAgent roles sequentially",
)
return parser.parse_args()
def _wait_for_file(path: Path, timeout_seconds: float) -> None:
deadline = time.time() + timeout_seconds
while time.time() < deadline:
if path.exists():
return
time.sleep(0.2)
raise TimeoutError(f"Timed out waiting for file: {path}")
def _wait_for_initial_state(gateway_port: int, timeout_seconds: float) -> dict[str, object]:
deadline = time.time() + timeout_seconds
last_error: Exception | None = None
while time.time() < deadline:
try:
ws = websocket.create_connection(
f"ws://127.0.0.1:{gateway_port}",
timeout=3,
)
try:
payload = json.loads(ws.recv())
return payload
finally:
ws.close()
except Exception as exc: # pragma: no cover - best-effort smoke polling
last_error = exc
time.sleep(0.2)
raise TimeoutError(
f"Timed out waiting for gateway initial_state on port {gateway_port}: {last_error}"
)
async def _run_smoke(agent_id: str, ticker: str, max_wait_seconds: float) -> dict[str, object]:
previous_env = os.environ.get("EVO_AGENT_IDS")
os.environ["EVO_AGENT_IDS"] = agent_id
try:
if _is_gateway_running():
await stop_runtime(force=True)
response = await start_runtime(
LaunchConfig(
launch_mode="fresh",
tickers=[ticker],
schedule_mode="daily",
interval_minutes=60,
trigger_time="09:30",
max_comm_cycles=1,
initial_cash=100000.0,
margin_requirement=0.0,
enable_memory=False,
mode="backtest",
start_date="2025-11-01",
end_date="2025-11-30",
poll_interval=30,
),
BackgroundTasks(),
)
run_dir = Path(response.run_dir)
log_path = run_dir / "logs" / "gateway.log"
state_path = run_dir / "state" / "runtime_state.json"
_wait_for_file(log_path, max_wait_seconds)
_wait_for_file(state_path, max_wait_seconds)
initial_state_payload = _wait_for_initial_state(
response.gateway_port,
max_wait_seconds,
)
log_text = log_path.read_text(encoding="utf-8")
state = json.loads(state_path.read_text(encoding="utf-8"))
record = TOOL_GUARD_STORE.create_pending(
tool_name="write_file",
tool_input={"path": "smoke.txt"},
agent_id=agent_id,
workspace_id=response.run_id,
)
pending = ToolApprovalRequest(
approval_id=record.approval_id,
tool_name=record.tool_name,
tool_input=record.tool_input,
tool_call_id="smoke_call",
)
record.pending_request = pending
await approve_tool_call(
ApprovalRequest(
approval_id=record.approval_id,
one_time=True,
expires_in_minutes=30,
)
)
result = {
"run_id": response.run_id,
"gateway_port": response.gateway_port,
"gateway_running": _is_gateway_running(),
"runtime_manager": get_runtime_state().runtime_manager is not None,
"evo_log_present": f"EvoAgent initialized: {agent_id}" in log_text,
"runtime_state_written": state_path.exists(),
"registered_agents": [item["agent_id"] for item in state.get("agents", [])],
"pending_request_approved": pending.approved is True,
"ws_initial_type": initial_state_payload.get("type"),
"ws_initial_tickers": (
(initial_state_payload.get("state") or {}).get("tickers") or []
),
}
return result
finally:
if _is_gateway_running():
await stop_runtime(force=True)
if previous_env is None:
os.environ.pop("EVO_AGENT_IDS", None)
else:
os.environ["EVO_AGENT_IDS"] = previous_env
def _verify_skills_loaded(log_text: str, agent_id: str) -> dict[str, bool]:
"""Verify that skills were loaded for the agent."""
return {
"skills_loaded": f"Loading skills for {agent_id}" in log_text or "skills" in log_text.lower(),
"tools_registered": "tool" in log_text.lower(),
}
async def _run_smoke_for_role(role: str, ticker: str, max_wait_seconds: float) -> dict[str, object]:
"""Run smoke test for a single agent role."""
print(f"\n>>> Testing EvoAgent role: {role}", file=sys.stderr)
result = await _run_smoke(
agent_id=role,
ticker=ticker,
max_wait_seconds=max_wait_seconds,
)
result["agent_role"] = role
result["success"] = (
result.get("evo_log_present", False)
and result.get("runtime_state_written", False)
and result.get("pending_request_approved", False)
)
return result
def main() -> int:
args = _parse_args()
if args.test_all_roles:
# Test all 6 agent roles
results = []
all_passed = True
for role in ALL_EVO_AGENT_ROLES:
try:
result = asyncio.run(
_run_smoke_for_role(
role=role,
ticker=args.ticker,
max_wait_seconds=args.max_wait_seconds,
)
)
results.append(result)
if not result.get("success", False):
all_passed = False
print(f" FAILED: {role}", file=sys.stderr)
else:
print(f" PASSED: {role}", file=sys.stderr)
except Exception as e:
all_passed = False
print(f" ERROR: {role} - {e}", file=sys.stderr)
results.append({
"agent_role": role,
"success": False,
"error": str(e),
})
summary = {
"test_mode": "all_roles",
"total_roles": len(ALL_EVO_AGENT_ROLES),
"passed": sum(1 for r in results if r.get("success", False)),
"failed": sum(1 for r in results if not r.get("success", False)),
"all_passed": all_passed,
"results": results,
}
print(json.dumps(summary, ensure_ascii=False, indent=2))
return 0 if all_passed else 1
else:
# Test single agent
result = asyncio.run(
_run_smoke(
agent_id=args.agent_id,
ticker=args.ticker,
max_wait_seconds=args.max_wait_seconds,
)
)
print(json.dumps(result, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())

203
scripts/test_sandbox.py Normal file
View File

@@ -0,0 +1,203 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
沙盒执行器测试脚本
测试多模式技能沙盒执行器的基本功能。
默认使用 none 模式（无沙盒）。
"""
import os
import sys
# 确保后端目录在路径中
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "backend"))
def test_sandbox_initialization():
"""测试沙盒初始化"""
print("=" * 60)
print("测试 1: 沙盒初始化")
print("=" * 60)
from backend.tools.sandboxed_executor import get_sandbox, SkillSandbox
# 重置单例（确保测试干净）
SkillSandbox._instance = None
# 默认应该使用 none 模式
sandbox = get_sandbox()
assert sandbox.current_mode == "none", f"期望模式 'none'，实际 '{sandbox.current_mode}'"
print(f"✓ 沙盒模式: {sandbox.current_mode}")
print(f"✓ 后端类型: {type(sandbox._backend).__name__}")
return sandbox
def test_no_sandbox_warning():
"""测试无沙盒模式的安全警告"""
print("\n" + "=" * 60)
print("测试 2: 无沙盒模式安全警告")
print("=" * 60)
import warnings
from backend.tools.sandboxed_executor import NoSandboxBackend
backend = NoSandboxBackend()
# 捕获警告
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
# 执行会触发警告
try:
backend.execute(
skill_name="builtin/valuation_review",
function_name="build_dcf_report",
function_args={"rows": [], "current_date": "2024-01-01"},
)
except Exception:
pass # 我们不关心执行结果，只关心警告
# 检查是否产生了警告
runtime_warnings = [x for x in w if issubclass(x.category, RuntimeWarning)]
if runtime_warnings:
print("✓ 安全警告已触发")
print(f" 警告内容: {runtime_warnings[0].message}")
else:
print("⚠ 未触发安全警告（可能已缓存）")
def test_docker_config():
"""测试 Docker 模式配置解析"""
print("\n" + "=" * 60)
print("测试 3: Docker 模式配置解析")
print("=" * 60)
# 设置环境变量
os.environ["SKILL_SANDBOX_MODE"] = "docker"
os.environ["SKILL_SANDBOX_MEMORY_LIMIT"] = "1g"
os.environ["SKILL_SANDBOX_CPU_LIMIT"] = "2.0"
from backend.tools.sandboxed_executor import SkillSandbox
# 重置单例
SkillSandbox._instance = None
try:
sandbox = SkillSandbox()
print(f"✓ 沙盒模式: {sandbox.current_mode}")
print(f"✓ 后端类型: {type(sandbox._backend).__name__}")
# 检查配置
backend = sandbox._backend
assert backend.config["memory_limit"] == "1g"
assert backend.config["cpu_limit"] == 2.0
print(f"✓ 内存限制: {backend.config['memory_limit']}")
print(f"✓ CPU 限制: {backend.config['cpu_limit']}")
except Exception as e:
print(f"⚠ Docker 后端创建失败（预期，可能未安装 agentscope-runtime）: {e}")
# 恢复环境变量
os.environ["SKILL_SANDBOX_MODE"] = "none"
SkillSandbox._instance = None
def test_analysis_tools_import():
"""测试分析工具导入"""
print("\n" + "=" * 60)
print("测试 4: 分析工具导入")
print("=" * 60)
try:
from backend.tools.analysis_tools import (
TOOL_REGISTRY,
_sandbox,
dcf_valuation_analysis,
)
print(f"✓ TOOL_REGISTRY 包含 {len(TOOL_REGISTRY)} 个工具")
print(f"✓ _sandbox 实例模式: {_sandbox.current_mode}")
print(f"✓ dcf_valuation_analysis 函数可用")
# 检查估值分析工具是否都在
valuation_tools = [
"dcf_valuation_analysis",
"owner_earnings_valuation_analysis",
"ev_ebitda_valuation_analysis",
"residual_income_valuation_analysis",
]
for tool in valuation_tools:
if tool in TOOL_REGISTRY:
print(f" ✓ {tool}")
else:
print(f" ✗ {tool} 缺失")
except Exception as e:
print(f"✗ 导入失败: {e}")
import traceback
traceback.print_exc()
def test_skill_execution_mock():
"""测试技能执行（模拟）"""
print("\n" + "=" * 60)
print("测试 5: 技能执行（无沙盒模式）")
print("=" * 60)
from backend.tools.sandboxed_executor import get_sandbox
sandbox = get_sandbox()
# 使用模拟参数调用
try:
# 注意：这需要实际的技能模块存在
result = sandbox.execute_skill(
skill_name="builtin/valuation_review",
function_name="build_dcf_report",
function_args={
"rows": [{"ticker": "AAPL", "current_fcf": 100000000}],
"current_date": "2024-01-01",
},
)
print(f"✓ 技能执行成功")
print(f" 结果类型: {type(result)}")
print(f" 结果预览: {str(result)[:100]}...")
except Exception as e:
print(f"⚠ 技能执行失败（可能缺少依赖或数据）: {e}")
def main():
"""主测试函数"""
print("\n" + "=" * 60)
print("技能沙盒执行器测试")
print("=" * 60)
print(f"当前 SKILL_SANDBOX_MODE: {os.getenv('SKILL_SANDBOX_MODE', '未设置（默认 none）')}")
# 确保使用默认模式测试
os.environ["SKILL_SANDBOX_MODE"] = "none"
try:
test_sandbox_initialization()
test_no_sandbox_warning()
test_docker_config()
test_analysis_tools_import()
test_skill_execution_mock()
print("\n" + "=" * 60)
print("测试完成")
print("=" * 60)
except Exception as e:
print(f"\n✗ 测试失败: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()

96
scripts/test_sandbox_simple.py Normal file
View File

@@ -0,0 +1,96 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
简化测试 - 验证沙盒执行器基本功能
"""
import os
import sys
import warnings
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "backend"))
os.environ["SKILL_SANDBOX_MODE"] = "none"
def test_import():
"""测试导入"""
print("测试 1: 导入沙盒执行器")
from backend.tools.sandboxed_executor import get_sandbox, SkillSandbox
# 重置单例
SkillSandbox._instance = None
sandbox = get_sandbox()
print(f" ✓ 模式: {sandbox.current_mode}")
print(f" ✓ 后端: {type(sandbox._backend).__name__}")
return sandbox
def test_no_sandbox_backend():
"""测试无沙盒后端"""
print("\n测试 2: 无沙盒后端")
from backend.tools.sandboxed_executor import NoSandboxBackend
backend = NoSandboxBackend()
# 测试函数名解析
test_cases = [
("build_dcf_report", "dcf_report"),
("build_ev_ebitda_report", "multiple_valuation_report"),
("build_owner_earnings_report", "owner_earnings_report"),
("build_residual_income_report", "multiple_valuation_report"),
]
for func_name, expected_script in test_cases:
script_name = backend._get_script_name(func_name)
assert script_name == expected_script, f"期望 {expected_script}, 实际 {script_name}"
print(f" ✓ {func_name} -> {script_name}")
def test_module_resolution():
"""测试模块解析"""
print("\n测试 3: 模块路径解析")
from backend.tools.sandboxed_executor import NoSandboxBackend
backend = NoSandboxBackend()
skill_name = "builtin/valuation_review"
function_name = "build_dcf_report"
module_path = f"backend.skills.{skill_name.replace('/', '.')}.scripts"
script_name = backend._get_script_name(function_name)
submodule_path = f"{module_path}.{script_name}"
print(f" 技能名: {skill_name}")
print(f" 函数名: {function_name}")
print(f" 模块路径: {submodule_path}")
# 尝试导入
try:
module = __import__(submodule_path, fromlist=[function_name])
func = getattr(module, function_name)
print(f" ✓ 成功导入函数: {func.__name__}")
except Exception as e:
print(f" ✗ 导入失败: {e}")
def main():
print("=" * 50)
print("沙盒执行器简化测试")
print("=" * 50)
# 抑制警告
warnings.filterwarnings("ignore", category=RuntimeWarning)
test_import()
test_no_sandbox_backend()
test_module_resolution()
print("\n" + "=" * 50)
print("测试完成")
print("=" * 50)
if __name__ == "__main__":
main()

168
scripts/verify_docs_consistency.py Normal file
View File

@@ -0,0 +1,168 @@
#!/usr/bin/env python3
"""Verify documentation and script consistency.
This script checks that:
1. README.md mentions correct service ports
2. start-dev.sh starts services on documented ports
3. deploy/README.md is consistent with production scripts
4. Service ports match across all documentation
"""
from __future__ import annotations
import argparse
import re
import sys
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parents[1]
# Expected service ports (source of truth)
SERVICE_PORTS = {
"agent_service": 8000,
"trading_service": 8001,
"news_service": 8002,
"runtime_service": 8003,
"gateway_websocket": 8765,
}
def check_readme_ports() -> list[str]:
"""Check that README.md documents correct ports."""
errors = []
readme_path = PROJECT_ROOT / "README.md"
readme_content = readme_path.read_text(encoding="utf-8")
# Check for each service port mention
for service, port in SERVICE_PORTS.items():
port_patterns = [
f":{port}",
f"port {port}",
f"localhost:{port}",
]
found = any(pattern in readme_content for pattern in port_patterns)
if not found:
errors.append(f"README.md: Missing documentation for {service} on port {port}")
return errors
def check_start_dev_sh_ports() -> list[str]:
"""Check that start-dev.sh uses correct ports."""
errors = []
script_path = PROJECT_ROOT / "start-dev.sh"
script_content = script_path.read_text(encoding="utf-8")
# Check for port declarations in start_service calls
for service, port in SERVICE_PORTS.items():
if service == "gateway_websocket":
# Gateway uses --port flag
if f"--port {port}" not in script_content:
errors.append(f"start-dev.sh: Gateway not using port {port}")
else:
# Services use port parameter in start_service
pattern = rf'start_service\s+"{service}"\s+"[^"]+"\s+{port}'
if not re.search(pattern, script_content):
# Also check for explicit port mentions
if f"port {port}" not in script_content and f":{port}" not in script_content:
errors.append(f"start-dev.sh: {service} not using port {port}")
return errors
def check_deploy_readme_consistency() -> list[str]:
"""Check that deploy/README.md is consistent with scripts."""
errors = []
deploy_readme_path = PROJECT_ROOT / "deploy" / "README.md"
deploy_content = deploy_readme_path.read_text(encoding="utf-8")
# Check for gateway port consistency
if "127.0.0.1:8765" not in deploy_content:
errors.append("deploy/README.md: Gateway port 8765 not documented correctly")
# Check for production script reference
if "scripts/run_prod.sh" not in deploy_content:
errors.append("deploy/README.md: Missing reference to scripts/run_prod.sh")
return errors
def check_run_prod_sh_ports() -> list[str]:
"""Check that run_prod.sh uses correct ports."""
errors = []
script_path = PROJECT_ROOT / "scripts" / "run_prod.sh"
script_content = script_path.read_text(encoding="utf-8")
# Production script should use port 8765 for gateway
if "--port 8765" not in script_content:
errors.append("scripts/run_prod.sh: Not using gateway port 8765")
return errors
def check_service_main_blocks() -> list[str]:
"""Check that service modules use correct ports in __main__ blocks."""
errors = []
service_files = {
"agent_service": PROJECT_ROOT / "backend" / "apps" / "agent_service.py",
"trading_service": PROJECT_ROOT / "backend" / "apps" / "trading_service.py",
"news_service": PROJECT_ROOT / "backend" / "apps" / "news_service.py",
"runtime_service": PROJECT_ROOT / "backend" / "apps" / "runtime_service.py",
}
for service, file_path in service_files.items():
if not file_path.exists():
errors.append(f"{service}: File not found at {file_path}")
continue
content = file_path.read_text(encoding="utf-8")
expected_port = SERVICE_PORTS[service]
# Check for port= in uvicorn.run or app.run
if f"port={expected_port}" not in content and f"port= {expected_port}" not in content:
errors.append(f"{file_path}: Not using expected port {expected_port}")
return errors
def main() -> int:
parser = argparse.ArgumentParser(
description="Verify documentation and script consistency.",
)
parser.add_argument(
"--strict",
action="store_true",
help="Treat warnings as errors",
)
args = parser.parse_args()
all_errors = []
print("Checking README.md ports...")
all_errors.extend(check_readme_ports())
print("Checking start-dev.sh ports...")
all_errors.extend(check_start_dev_sh_ports())
print("Checking deploy/README.md consistency...")
all_errors.extend(check_deploy_readme_consistency())
print("Checking scripts/run_prod.sh ports...")
all_errors.extend(check_run_prod_sh_ports())
print("Checking service __main__ blocks...")
all_errors.extend(check_service_main_blocks())
if all_errors:
print("\nConsistency errors found:")
for error in all_errors:
print(f" - {error}")
return 1 if args.strict else 0
else:
print("\nAll consistency checks passed!")
return 0
if __name__ == "__main__":
raise SystemExit(main())

156
services/README.md
View File

@@ -4,6 +4,14 @@ This repository is in a split-first state: local development now assumes
separate app surfaces and a dedicated WebSocket gateway instead of a single
combined backend entrypoint.
For the canonical architecture summary, start with
[docs/current-architecture.md](../docs/current-architecture.md). This file is
service-focused and includes migration details.
The matching visual diagram lives at
[docs/current-architecture.excalidraw](../docs/current-architecture.excalidraw),
and the next-step execution plan lives at
[docs/development-roadmap.md](../docs/development-roadmap.md).
## Service Map
| Surface | Default port | Role |
@@ -13,9 +21,32 @@ combined backend entrypoint.
| `backend.apps.news_service` | `8002` | Read-only explain/news APIs such as story, similar days, range explain |
| `backend.apps.runtime_service` | `8003` | Runtime lifecycle APIs under `/api/runtime/*` |
| `backend.apps.openclaw_service` | `8004` | Read-only OpenClaw REST facade |
| Gateway (`backend.main`) | `8765` | WebSocket feed, runtime event stream, legacy/compat orchestration path |
| Gateway (`backend.main`) | `8765` | WebSocket feed, runtime event stream, pipeline execution |
| OpenClaw Gateway | `18789` | External OpenClaw WebSocket endpoint consumed by 大时代 gateway |
## Runtime Modes
### Standalone Mode (Direct Gateway Startup)
For simple deployments or backward compatibility:
```bash
python -m backend.main --mode live --host 0.0.0.0 --port 8765
```
In this mode, Gateway runs as the primary process with all components
(Pipeline, Market Service, Scheduler) loaded in-process.
### Microservice Mode (Recommended)
For development and production with service isolation:
```bash
./start-dev.sh
```
This starts all services with `runtime_service` managing the Gateway lifecycle.
## What Runs By Default In Dev
The supported local dev path is:
@@ -30,7 +61,7 @@ That script starts:
- `trading_service` on `8001`
- `news_service` on `8002`
- `runtime_service` on `8003`
- 大时代 gateway on `8765`
- 大时代 gateway on `8765` (as subprocess of runtime_service)
It does **not** start `openclaw_service` on `8004`.
@@ -47,7 +78,21 @@ python -m uvicorn backend.apps.agent_service:app --host 0.0.0.0 --port 8000 --re
python -m uvicorn backend.apps.trading_service:app --host 0.0.0.0 --port 8001 --reload
python -m uvicorn backend.apps.news_service:app --host 0.0.0.0 --port 8002 --reload
python -m uvicorn backend.apps.runtime_service:app --host 0.0.0.0 --port 8003 --reload
python -m backend.main --mode live --host 0.0.0.0 --port 8765
```
The Gateway is started by `runtime_service` via the `/api/runtime/start` API,
not manually. To start a runtime:
```bash
curl -X POST http://localhost:8003/api/runtime/start \
-H "Content-Type: application/json" \
-d '{
"launch_mode": "fresh",
"tickers": ["AAPL", "MSFT"],
"schedule_mode": "daily",
"trigger_time": "09:30",
"initial_cash": 100000
}'
```
Optional OpenClaw REST surface:
@@ -60,13 +105,72 @@ python -m uvicorn backend.apps.openclaw_service:app --host 0.0.0.0 --port 8004 -
The runtime path is intentionally split:
- `runtime_service` handles start, stop, restart, current runtime info, logs, and runtime state APIs
### Control Plane (runtime_service :8003)
- **Gateway lifecycle**: Start, stop, restart Gateway processes
- **Configuration**: Bootstrap values, runtime parameters
- **Health monitoring**: Gateway process status, port management
- **Run history**: List historical runs, restore from snapshots
### Data Plane (Gateway :8765)
- **WebSocket transport**: Live event streaming to frontend
- **Pipeline execution**: Analysis -> Communication -> Decision -> Execution
- **Market data**: Real-time price feeds and backtest simulation
- **Scheduler**: Trading cycle orchestration
### Supporting Services
- `agent_service` handles control-plane reads and writes for agents, workspaces, files, and approvals
- `backend.main` / gateway hosts the live WebSocket channel and coordinates market service, scheduler, and pipeline execution
- `trading_service` provides read-only trading data
- `news_service` provides news enrichment and explanation APIs
The practical request path looks like:
`frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage`
```
frontend -> runtime_service/control APIs -> gateway/runtime manager -> market service + pipeline + storage
```
## runtime_service as Gateway Process Manager
The `runtime_service` is the **Gateway Process Manager** in the microservice
architecture. Its responsibilities:
1. **Process Management**
- Spawns Gateway as subprocess via `_start_gateway_process()`
- Monitors process health via `gateway_process.poll()`
- Handles graceful shutdown (SIGTERM) and force kill
2. **Port Management**
- Finds available ports (`_find_available_port()`)
- Tracks current Gateway port in RuntimeState
3. **Lifecycle APIs**
- `POST /api/runtime/start` - Create run, spawn Gateway
- `POST /api/runtime/stop` - Stop Gateway process
- `POST /api/runtime/restart` - Stop then start new runtime
- `GET /api/runtime/gateway/status` - Check Gateway health
4. **State Management**
- Maintains RuntimeState singleton (thread-safe)
- Tracks runtime_manager reference for in-memory state
- Falls back to persisted snapshots when Gateway is stopped
### Gateway Subprocess Architecture
```
runtime_service (:8003)
|
|-- spawns --> Gateway subprocess (:8765)
|
|-- TradingPipeline
|-- MarketService
|-- Scheduler
|-- WebSocket server
```
The Gateway subprocess runs `backend.gateway_server` module (not `backend.main`)
with run-specific configuration passed via CLI arguments.
## Environment Variables
@@ -144,7 +248,7 @@ backend.apps.agent_service
└─ control-plane routes
backend.apps.runtime_service
└─ runtime lifecycle routes
└─ runtime lifecycle routes, Gateway process management
backend.apps.trading_service
└─ read-only trading contract
@@ -155,6 +259,40 @@ backend.apps.news_service
backend.apps.openclaw_service
└─ optional OpenClaw REST facade
backend.main / backend.services.gateway
└─ live orchestration, feed transport, scheduler, runtime coordination
backend.gateway_server
└─ Gateway subprocess entry point (run-scoped)
backend.main
└─ standalone Gateway entry point (compatibility)
```
## Migration Boundaries
Some agent-migration helpers still exist in the tree, but they are not part of
the supported runtime path yet:
No workspace-loading helper remains on `TradingPipeline`. Runtime agent loading
is expected to stay on the run-scoped creation path.
Also note the remaining naming split:
- `workspaces/` = design-time CRUD registry
- `runs/<run_id>/` = runtime state and run-scoped agent assets
## Future Architecture Direction
### Current State
- Pipeline logic lives in Gateway process
- Gateway is spawned as subprocess by runtime_service
- Standalone mode (`backend.main`) preserved for compatibility
### Target State
- Pipeline stages become independent services
- Gateway becomes thin event router
- runtime_service becomes full orchestrator
- Standalone mode deprecated and removed
See [docs/development-roadmap.md](../docs/development-roadmap.md) for detailed
phase planning.

573
start-dev.sh
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@@ -1,103 +1,335 @@
#!/usr/bin/env bash
#
# 大时代 Development Startup Script
# Split-service mode only
# ================================
#
# 启动模式说明:
# -------------
# 本脚本支持两种启动模式:
#
# 1. 微服务模式 (默认) - 启动 4 个独立服务 + Gateway
# 这是推荐的开发模式，各服务独立运行，便于单独调试和重启
# - agent_service (端口 8000): Agent 生命周期管理
# - runtime_service (端口 8003): 运行时配置和 Pipeline 执行
# - trading_service (端口 8001): 市场数据和交易操作
# - news_service (端口 8002): 新闻采集和富化
# - gateway (端口 8765): WebSocket 网关，前端连接入口
#
# 2. 独立模式 (--standalone) - 仅启动 Gateway
# Gateway 内部会自行管理服务，适合快速验证或资源受限环境
#
# 用法:
# ./start-dev.sh # 启动微服务模式
# ./start-dev.sh --standalone # 启动独立模式
# ./start-dev.sh --help # 显示帮助信息
#
set -euo pipefail
echo "=========================================="
echo "大时代 Development Environment"
echo "=========================================="
# ============================================
# 配置与常量
# ============================================
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m' # No Color
readonly SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
readonly SCRIPT_NAME="$(basename "$0")"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "${SCRIPT_DIR}"
# 服务端点配置
readonly AGENT_SERVICE_PORT=8000
readonly TRADING_SERVICE_PORT=8001
readonly NEWS_SERVICE_PORT=8002
readonly RUNTIME_SERVICE_PORT=8003
readonly GATEWAY_PORT=8765
# 颜色定义
readonly RED='\033[0;31m'
readonly GREEN='\033[0;32m'
readonly YELLOW='\033[1;33m'
readonly BLUE='\033[0;34m'
readonly CYAN='\033[0;36m'
readonly NC='\033[0m' # No Color
# 进程 ID 数组
PIDS=()
require_command() {
local command_name="$1"
if ! command -v "${command_name}" >/dev/null 2>&1; then
echo -e "${RED}Missing required command: ${command_name}${NC}"
# 启动模式: "microservices" 或 "standalone"
MODE="microservices"
# ============================================
# 工具函数
# ============================================
log_info() {
echo -e "${GREEN}[INFO]${NC} $1"
}
log_warn() {
echo -e "${YELLOW}[WARN]${NC} $1"
}
log_error() {
echo -e "${RED}[ERROR]${NC} $1"
}
log_step() {
echo -e "${CYAN}[STEP]${NC} $1"
}
log_debug() {
echo -e "${BLUE}[DEBUG]${NC} $1"
}
# ============================================
# 帮助信息
# ============================================
show_help() {
cat << 'EOF'
大时代 Development Startup Script
用法:
./start-dev.sh [选项]
选项:
--standalone 以独立模式启动（仅启动 Gateway，内部管理服务）
--help, -h 显示此帮助信息
模式说明:
微服务模式 (默认):
启动 4 个独立微服务 + Gateway，各服务独立进程，便于单独调试
- agent_service: http://localhost:8000 (Agent 生命周期)
- trading_service: http://localhost:8001 (市场数据)
- news_service: http://localhost:8002 (新闻服务)
- runtime_service: http://localhost:8003 (运行时管理)
- gateway: ws://localhost:8765 (WebSocket 网关)
独立模式 (--standalone):
仅启动 Gateway，由 Gateway 内部自行管理服务
适合快速验证或资源受限环境
环境要求:
- Python 3.9+
- 虚拟环境 (推荐 .venv)
- .env 文件 (可选但推荐)
示例:
./start-dev.sh # 启动微服务模式
./start-dev.sh --standalone # 启动独立模式
EOF
}
# ============================================
# 参数解析
# ============================================
parse_args() {
while [[ $# -gt 0 ]]; do
case "$1" in
--standalone)
MODE="standalone"
shift
;;
--help|-h)
show_help
exit 0
;;
*)
log_warn "未知选项: $1"
log_info "使用 --help 查看帮助信息"
exit 1
;;
esac
done
}
# ============================================
# 启动前检查
# ============================================
check_python_version() {
log_step "检查 Python 版本..."
if ! command -v python >/dev/null 2>&1; then
log_error "未找到 python 命令"
exit 1
fi
local python_version
python_version=$(python --version 2>&1 | awk '{print $2}')
log_debug "Python 版本: $python_version"
python - <<'PY' || {
import sys
if sys.version_info < (3, 9):
print(f"Python 3.9+ 是必需的，当前版本: {sys.version}")
sys.exit(1)
print(f"Python 版本检查通过: {sys.version.split()[0]}")
PY
log_error "Python 版本不符合要求 (需要 3.9+)"
exit 1
}
}
check_virtual_env() {
log_step "检查虚拟环境..."
if [[ -n "${VIRTUAL_ENV:-}" ]]; then
log_info "已激活虚拟环境: $VIRTUAL_ENV"
return 0
fi
if [[ -f "$SCRIPT_DIR/.venv/bin/activate" ]]; then
log_warn "未激活虚拟环境，自动激活 .venv"
# shellcheck disable=SC1091
source "$SCRIPT_DIR/.venv/bin/activate"
log_info "虚拟环境已激活: $VIRTUAL_ENV"
else
log_warn "未找到虚拟环境，使用系统 Python"
fi
}
check_python_module() {
local module_name="$1"
if ! python -c "import ${module_name}" >/dev/null 2>&1; then
echo -e "${RED}Missing required Python module: ${module_name}${NC}"
echo "Install dependencies with one of:"
echo " pip install -r requirements.txt"
echo " pip install -r requirements-dev.txt"
echo " uv pip install -e '.[dev]'"
check_required_commands() {
log_step "检查必要命令..."
local missing=()
if ! command -v python >/dev/null 2>&1; then
missing+=("python")
fi
if ! command -v lsof >/dev/null 2>&1; then
missing+=("lsof")
fi
if [[ ${#missing[@]} -gt 0 ]]; then
log_error "缺少必要命令: ${missing[*]}"
log_info "请安装缺失的命令后重试"
exit 1
fi
log_info "所有必要命令已安装"
}
load_env_file() {
if [ -f .env ]; then
echo -e "${GREEN}Loading environment from .env${NC}"
check_python_modules() {
log_step "检查 Python 依赖模块..."
local modules=("fastapi" "uvicorn" "websockets" "yaml" "dotenv")
local missing=()
for module in "${modules[@]}"; do
if ! python -c "import $module" 2>/dev/null; then
missing+=("$module")
fi
done
if [[ ${#missing[@]} -gt 0 ]]; then
log_error "缺少 Python 模块: ${missing[*]}"
log_info "请安装依赖: uv pip install -e '.[dev]' 或 pip install -r requirements.txt"
exit 1
fi
log_info "所有依赖模块已安装"
}
check_env_file() {
log_step "检查环境配置文件..."
if [[ -f "$SCRIPT_DIR/.env" ]]; then
log_info "加载环境变量: .env"
set -a
source .env
# shellcheck disable=SC1091
source "$SCRIPT_DIR/.env"
set +a
else
echo -e "${YELLOW}Warning: .env file not found. Copy env.template to .env first if you need live credentials.${NC}"
log_warn ".env 文件不存在，使用默认配置"
log_info "提示: 复制 env.template 到 .env 并配置您的 API 密钥"
fi
}
check_env_var() {
local var_name="$1"
local severity="${2:-warn}"
local value="${!var_name:-}"
if [ -z "${value}" ]; then
if [ "${severity}" = "error" ]; then
echo -e "${RED}Missing required environment variable: ${var_name}${NC}"
exit 1
check_ports() {
log_step "检查端口占用情况..."
local ports=()
if [[ "$MODE" == "microservices" ]]; then
ports=($AGENT_SERVICE_PORT $TRADING_SERVICE_PORT $NEWS_SERVICE_PORT $RUNTIME_SERVICE_PORT $GATEWAY_PORT)
else
ports=($GATEWAY_PORT)
fi
echo -e "${YELLOW}Warning: ${var_name} is not set${NC}"
local occupied=()
for port in "${ports[@]}"; do
if lsof -Pi :"$port" -sTCP:LISTEN -t >/dev/null 2>&1; then
occupied+=("$port")
fi
done
if [[ ${#occupied[@]} -gt 0 ]]; then
log_warn "以下端口已被占用: ${occupied[*]}"
log_info "尝试释放端口..."
for port in "${occupied[@]}"; do
kill_port "$port"
done
else
log_info "所有端口可用"
fi
}
kill_port() {
local port="$1"
local pids
pids=$(lsof -ti :"$port" 2>/dev/null || true)
if [[ -n "$pids" ]]; then
log_warn "释放端口 $port (PID: $pids)"
echo "$pids" | xargs kill -9 2>/dev/null || true
sleep 0.5
fi
}
check_optional_services() {
log_step "检查可选服务..."
# 检查 npm（用于前端）
if ! command -v npm >/dev/null 2>&1; then
log_warn "npm 未安装，前端启动功能不可用"
else
log_info "npm 已安装"
fi
# 检查 OpenClaw gateway
check_openclaw_gateway
}
check_openclaw_gateway() {
local target_host="127.0.0.1"
local target_port="18789"
if python - <<PY >/dev/null 2>&1
if python - <<PY >/dev/null 2>&1; then
import socket
sock = socket.socket()
sock.settimeout(1.0)
sock.connect(("${target_host}", ${target_port}))
sock.close()
PY
then
echo -e "${GREEN}OpenClaw gateway reachable at ws://${target_host}:${target_port}${NC}"
log_info "OpenClaw gateway 可连接: ws://${target_host}:${target_port}"
else
echo -e "${YELLOW}Warning: OpenClaw gateway is not reachable at ws://${target_host}:${target_port}${NC}"
echo " OpenClaw panel features may be unavailable until it is started."
log_warn "OpenClaw gateway 未启动: ws://${target_host}:${target_port}"
log_info " OpenClaw 面板功能将不可用"
fi
}
print_prereq_help() {
echo "Environment checks:"
echo " - repo root: ${SCRIPT_DIR}"
echo " - python: $(command -v python)"
if [ -n "${VIRTUAL_ENV:-}" ]; then
echo " - virtualenv: ${VIRTUAL_ENV}"
else
echo " - virtualenv: not activated"
fi
}
# ============================================
# 服务启动函数
# ============================================
start_service() {
local name="$1"
local app_path="$2"
local port="$3"
echo -e "${GREEN}Starting ${name}${NC} on port ${port}..."
log_info "启动 ${name} (端口 ${port})..."
SERVICE_NAME="${name}" python -m uvicorn "${app_path}" \
--host 0.0.0.0 \
--port "${port}" \
@@ -108,111 +340,156 @@ start_service() {
PIDS+=($!)
}
cleanup() {
if [ "${#PIDS[@]}" -gt 0 ]; then
echo ""
echo -e "${YELLOW}Stopping development services...${NC}"
kill "${PIDS[@]}" 2>/dev/null || true
wait "${PIDS[@]}" 2>/dev/null || true
fi
start_gateway() {
log_step "启动 Gateway (WebSocket 服务)..."
log_info "Gateway 将作为子进程启动 (端口 ${GATEWAY_PORT})"
log_info "前端连接地址: ws://localhost:${GATEWAY_PORT}"
SERVICE_NAME="gateway" python -m backend.main \
--mode live \
--host 0.0.0.0 \
--port "$GATEWAY_PORT" &
PIDS+=($!)
}
kill_port() {
local port="$1"
local pids=$(lsof -ti :${port} 2>/dev/null || true)
if [ -n "$pids" ]; then
echo -e "${YELLOW}Port ${port} is in use, killing PID(s): ${pids}${NC}"
echo "$pids" | xargs kill -9 2>/dev/null || true
sleep 0.5
# ============================================
# 微服务模式启动
# ============================================
start_microservices_mode() {
log_step "启动微服务模式..."
echo ""
echo -e "${CYAN}==========================================${NC}"
echo -e "${CYAN} 服务端点 ${NC}"
echo -e "${CYAN}==========================================${NC}"
echo -e " agent_service: http://localhost:${AGENT_SERVICE_PORT}"
echo -e " runtime_service: http://localhost:${RUNTIME_SERVICE_PORT}"
echo -e " trading_service: http://localhost:${TRADING_SERVICE_PORT}"
echo -e " news_service: http://localhost:${NEWS_SERVICE_PORT}"
echo -e " gateway: ws://localhost:${GATEWAY_PORT}"
echo -e "${CYAN}==========================================${NC}"
echo ""
# 设置服务 URL 环境变量
export TRADING_SERVICE_URL="${TRADING_SERVICE_URL:-http://localhost:${TRADING_SERVICE_PORT}}"
export NEWS_SERVICE_URL="${NEWS_SERVICE_URL:-http://localhost:${NEWS_SERVICE_PORT}}"
export RUNTIME_SERVICE_URL="${RUNTIME_SERVICE_URL:-http://localhost:${RUNTIME_SERVICE_PORT}}"
export OPENCLAW_SERVICE_URL="${OPENCLAW_SERVICE_URL:-http://localhost:18789}"
export ENABLE_DASHBOARD_COMPAT_EXPORTS="${ENABLE_DASHBOARD_COMPAT_EXPORTS:-true}"
log_debug "环境变量:"
log_debug " TRADING_SERVICE_URL=${TRADING_SERVICE_URL}"
log_debug " NEWS_SERVICE_URL=${NEWS_SERVICE_URL}"
log_debug " RUNTIME_SERVICE_URL=${RUNTIME_SERVICE_URL}"
log_debug " OPENCLAW_SERVICE_URL=${OPENCLAW_SERVICE_URL}"
echo ""
# 启动 4 个微服务
start_service "agent_service" "backend.apps.agent_service:app" "$AGENT_SERVICE_PORT"
start_service "runtime_service" "backend.apps.runtime_service:app" "$RUNTIME_SERVICE_PORT"
start_service "trading_service" "backend.apps.trading_service:app" "$TRADING_SERVICE_PORT"
start_service "news_service" "backend.apps.news_service:app" "$NEWS_SERVICE_PORT"
# 启动 Gateway（作为子进程）
start_gateway
echo ""
log_info "所有服务已启动"
log_info "按 Ctrl+C 停止所有服务"
echo ""
}
# ============================================
# 独立模式启动
# ============================================
start_standalone_mode() {
log_step "启动独立模式..."
echo ""
echo -e "${CYAN}==========================================${NC}"
echo -e "${CYAN} 独立模式 ${NC}"
echo -e "${CYAN}==========================================${NC}"
echo -e " gateway: ws://localhost:${GATEWAY_PORT}"
echo -e "${CYAN}==========================================${NC}"
echo ""
log_info "Gateway 将内部管理服务"
# 启动 Gateway（独立模式）
start_gateway
echo ""
log_info "Gateway 已启动（独立模式）"
log_info "按 Ctrl+C 停止服务"
echo ""
}
# ============================================
# 清理与信号处理
# ============================================
cleanup() {
if [[ ${#PIDS[@]} -gt 0 ]]; then
echo ""
log_step "正在停止服务..."
for pid in "${PIDS[@]}"; do
if kill -0 "$pid" 2>/dev/null; then
kill "$pid" 2>/dev/null || true
fi
done
# 等待进程结束
wait "${PIDS[@]}" 2>/dev/null || true
log_info "所有服务已停止"
fi
}
trap cleanup EXIT INT TERM
if [ $# -gt 0 ]; then
echo -e "${YELLOW}Ignoring legacy mode argument(s): $*${NC}"
echo "Split-service mode is now the only supported development mode."
fi
# ============================================
# 主程序
# ============================================
require_command python
require_command lsof
main() {
# 解析命令行参数
parse_args "$@"
if [ -z "${VIRTUAL_ENV:-}" ]; then
if [ -f ".venv/bin/activate" ]; then
echo -e "${YELLOW}Virtual environment not activated; auto-activating .venv${NC}"
# shellcheck disable=SC1091
source .venv/bin/activate
# 显示启动横幅
echo ""
echo -e "${CYAN}==========================================${NC}"
echo -e "${CYAN} 大时代 Development Environment ${NC}"
echo -e "${CYAN}==========================================${NC}"
echo ""
# 切换到项目根目录
cd "$SCRIPT_DIR"
log_debug "工作目录: $SCRIPT_DIR"
# 启动前检查
check_required_commands
check_python_version
check_virtual_env
check_python_modules
check_env_file
check_ports
check_optional_services
echo ""
echo -e "${GREEN}==========================================${NC}"
echo -e "${GREEN} 启动前检查完成 ${NC}"
echo -e "${GREEN}==========================================${NC}"
echo ""
# 根据模式启动服务
if [[ "$MODE" == "standalone" ]]; then
start_standalone_mode
else
echo -e "${YELLOW}Warning: no active virtual environment and .venv not found${NC}"
start_microservices_mode
fi
fi
load_env_file
# 等待所有后台进程
wait
}
print_prereq_help
python - <<'PY'
import sys
if sys.version_info < (3, 9):
raise SystemExit("Python 3.9+ is required")
print(f"Python version OK: {sys.version.split()[0]}")
PY
check_python_module fastapi
check_python_module uvicorn
check_python_module websockets
check_python_module yaml
check_python_module dotenv
check_env_var OPENAI_API_KEY
check_env_var FINNHUB_API_KEY
check_env_var FIN_DATA_SOURCE
if ! command -v npm >/dev/null 2>&1; then
echo -e "${YELLOW}Warning: npm is not installed. Frontend startup via 'evotraders frontend' will not work.${NC}"
fi
export TRADING_SERVICE_URL="${TRADING_SERVICE_URL:-http://localhost:8001}"
export NEWS_SERVICE_URL="${NEWS_SERVICE_URL:-http://localhost:8002}"
export RUNTIME_SERVICE_URL="${RUNTIME_SERVICE_URL:-http://localhost:8003}"
export OPENCLAW_SERVICE_URL="${OPENCLAW_SERVICE_URL:-http://localhost:18789}"
check_openclaw_gateway
echo ""
echo -e "${GREEN}Starting 大时代 split services (default mode)...${NC}"
echo " agent_service: http://localhost:8000"
echo " runtime_service: http://localhost:8003"
echo " openclaw_gateway: ws://localhost:18789"
echo " trading_service: http://localhost:8001"
echo " news_service: http://localhost:8002"
echo ""
echo "Exported backend preference URLs:"
echo " TRADING_SERVICE_URL=${TRADING_SERVICE_URL}"
echo " NEWS_SERVICE_URL=${NEWS_SERVICE_URL}"
echo " RUNTIME_SERVICE_URL=${RUNTIME_SERVICE_URL}"
echo " OPENCLAW_SERVICE_URL=${OPENCLAW_SERVICE_URL}"
echo ""
echo -e "${GREEN}Checking ports...${NC}"
kill_port 8000
kill_port 8001
kill_port 8002
kill_port 8003
kill_port 8765
start_service "agent_service" "backend.apps.agent_service:app" 8000
start_service "runtime_service" "backend.apps.runtime_service:app" 8003
start_service "trading_service" "backend.apps.trading_service:app" 8001
start_service "news_service" "backend.apps.news_service:app" 8002
echo -e "${GREEN}Starting Gateway (WebSocket, port 8765)...${NC}"
SERVICE_NAME="gateway" python -m backend.main \
--mode live \
--host 0.0.0.0 \
--port 8765 &
PIDS+=($!)
echo -e "${GREEN}Split services are running.${NC}"
echo "Use Ctrl+C to stop all services."
wait
# 执行主程序
main "$@"