移除硬编码的 API key

.gitignore

@@ -0,0 +1,10 @@
+# Python 字节码缓存
+__pycache__/
+*.py[cod]
+*$py.class
+
+# 项目特定的存储/缓存文件夹
+.storage/
+
+# 环境变量文件（通常包含敏感信息）
+.env

.python-version

@@ -0,0 +1 @@
+3.12

.storage/config.json

@@ -0,0 +1,6 @@
+{
+  "provider": "AIHubMix",
+  "api_key": "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73",
+  "base_url": "https://aihubmix.com/v1",
+  "language": "Chinese"
+}

.storage/history/1767772490_council_轻小说.json

@@ -0,0 +1,16 @@
+{
+  "id": "1767772490",
+  "timestamp": 1767772490,
+  "date": "2026-01-07 15:54:50",
+  "type": "council",
+  "topic": "轻小说",
+  "content": "\n\n[错误: Error code: 401 - {'error': {'message': 'Invalid token: ca812c913baa474182f6d4e83e078302 (tid: 2026010707545042546382958168401)', 'type': 'Aihubmix_api_error'}}]",
+  "metadata": {
+    "rounds": 1,
+    "experts": [
+      "Expert 1",
+      "Expert 2"
+    ],
+    "language": "Chinese"
+  }
+}

.storage/history/1767772724_council_轻小说.json

@@ -0,0 +1,17 @@
+{
+  "id": "1767772724",
+  "timestamp": 1767772724,
+  "date": "2026-01-07 15:58:44",
+  "type": "council",
+  "topic": "轻小说",
+  "content": "\n\n[错误: Error code: 400 - {'error': {'message': 'Model Not Exist', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_request_error'}}]",
+  "metadata": {
+    "rounds": 2,
+    "experts": [
+      "Expert 1",
+      "Expert 2",
+      "Expert 3 (Synthesizer)"
+    ],
+    "language": "Chinese"
+  }
+}

.storage/history/1767772840_council_轻小说.json

@@ -0,0 +1,16 @@
+{
+  "id": "1767772840",
+  "timestamp": 1767772840,
+  "date": "2026-01-07 16:00:40",
+  "type": "council",
+  "topic": "轻小说",
+  "content": "好的。作为Expert 2，我将基于与Expert 1的讨论，整合我们双方的见解，形成一个关于构建可持续、有深度的“轻小说”生态系统的最终综合计划。\n\n### **关于“轻小说”与“玩乐关系”的综合分析与行动计划**\n\n经过讨论，我们（Expert 1 与 Expert 2）的核心共识在于：**“轻小说”的本质是一种以“玩乐关系”为核心驱动力的文化产品。** 这种关系超越了简单的阅读，构建了一个由 **“创作者-作品-读者社群-产业生态”** 共同参与的、动态的“叙事游乐场”。Expert 1 精准地指出了其“共玩”特性与边界模糊化，而我的补充则聚焦于这种关系背后的“玩家心态”与意义生产的“流动性”。\n\n我们共同认为，轻小说的挑战（如套路化、深度疑虑）与机遇（如强大的社群凝聚力、跨媒体潜力）皆根植于此。因此，我们的行动计划并非要否定或削弱这种“玩乐性”，而是旨在**引导、深化和结构化这种关系，使其朝着更健康、更具创造力与可持续性的方向发展。**\n\n以下是我们提出的三位一体具体行动计划：\n\n---\n\n### **行动计划：构建“共创式叙事游乐场”生态系统**\n\n本计划围绕三大核心参与方展开，由一个核心原则统御，旨在将松散的“玩乐关系”升级为有活力的“共创生态”。\n\n#### **核心原则：从“单向供给”到“有规则的共玩”**\n确立“作者拥有主叙事权，社群享有拓展权”的共识。官方作品提供坚实、自洽、富有弹性的“核心设定与初始剧情”（即主游戏），同时明确欢迎并预留空间给社群的二次创作与解读（即玩家自创模组）。这既保护了作品的原创引力，又激发了社群活力。\n\n#### **一、 对创作者：成为“世界架构师”与“游戏设计师”**\n1.  **设计“开放世界”而非“线性赛道”**：\n    *   **行动**：在构思阶段，除了主线，需有意识地搭建具有扩展性的世界观底层规则（如魔力系统、社会结构）和留有“空白”的角色背景。这为读者的想象与二次创作提供了合法的“土壤”。\n    *   **目标**：将作品从封闭的故事，转变为可供探索的“世界”，",
+  "metadata": {
+    "rounds": 1,
+    "experts": [
+      "Expert 1",
+      "Expert 2"
+    ],
+    "language": "Chinese"
+  }
+}

README.md

@@ -1,82 +1,91 @@
-# Multi-Agent Decision Workshop & Deep Research
+# 🍎 智能决策工作坊 (Multi-Agent Council V4)
 
-这是一个基于多智能体（Multi-Agent）的决策辅助和深度研究系统。它包含两个核心模式：
-1.  **Deep Research Mode (Deep Research)**: 模仿 Gemini 研究模式，通过规划、执行、撰写三个阶段进行深度分析。
-2.  **Debate Workshop (辩论工作坊)**: 让多个 AI 角色从不同视角辩论，帮助你做出更全面的决策。
+AI驱动的多智能体决策分析系统 - 基于多模型智囊团
 
-## ✨ 功能特性
+## ✨ 核心功能
 
--   **双模式切换**: 侧边栏一键切换 "Deep Research" 和 "Debate Workshop"。
--   **自定义模型角色**:
-    -   在 Deep Research 模式下，可以分别指定 `Planner` (规划者), `Researcher` (研究员), `Writer` (作家) 使用不同的 LLM。
--   **多模型支持**: 支持 OpenAI (GPT-4o), Anthropic (Claude 3.5), Gemini 等主流模型。
--   **交互式研究**: 生成研究计划后，用户可以介入修改，确保研究方向正确。
--   **流式输出**: 实时展示研究进度和辩论过程。
+### 🧪 Multi-Model Council V4 (智囊团模式)
+- **多轮对话讨论**: 专家像真实会议一样进行多轮对话，互相批判、补充观点
+- **动态专家组建**: 自定义 2-5 位专家，为每位指定最擅长的模型
+- **🪄 智能专家生成**: AI 根据主题自动推荐最合适的专家角色
+- **最终决策合成**: 最后一位专家综合全场观点，生成方案并绘制 Mermaid 路线图
 
-## 🛠️ 安装与使用
+### 🎯 内置决策场景
+系统预置 4 大典型决策场景，每个场景都配置了专业的典型问题：
 
-### 1. 克隆项目
-
-```bash
-git clone https://github.com/HomoDeusss/multi-agent.git
-cd multi-agent
-```
-
-### 2. 安装依赖
-
-确保你安装了 Python 3.8+。
-
-```bash
-pip install -r requirements.txt
-```
-
-### 3. 配置 API Key
-
-你可以通过以下两种方式配置 API Key：
-
-**方式 A: 创建 `.env` 文件 (推荐)**
-复制 `.env.example` 为 `.env`，并填入你的 API Key。
-
-```bash
-cp .env.example .env
-```
-
-编辑 `.env` 文件:
-```env
-AIHUBMIX_API_KEY=your_api_key_here
-```
-
-**方式 B: 在 UI 中输入**
-启动应用后，在侧边栏的 "设置" -> "API Key" 输入框中填入。
-
-### 4. 启动应用
-
-运行 Streamlit 应用：
-
-```bash
-streamlit run app.py
-```
-
-会自动在浏览器打开 `http://localhost:8501`。
-
-## 📖 使用指南
-
-### 🧪 Deep Research Mode (深度研究模式)
-1.  在侧边栏选择模式为 **"Deep Research"**。
-2.  在 "研究模型配置" 中，为 Planner, Researcher, Writer 选择合适的模型（推荐分别使用 GPT-4o, Gemini-1.5-pro, Claude-3.5-sonnet）。
-3.  输入你的**研究主题** (例如: "2025年量子计算商业化前景")。
-4.  点击 **"生成研究计划"**。
-5.  系统生成计划后，你可以直接在文本框中**修改计划步骤**。
-6.  点击 **"开始深度研究"**，观察 Agent 逐步执行研究任务。
-7.  下载最终生成的 Markdown 报告。
+| 场景 | 描述 |
+|------|------|
+| 🚀 新产品发布评审 | 评估产品可行性、市场潜力和实施计划 |
+| 💰 投资审批决策 | 分析投资项目的 ROI、风险和战略价值 |
+| 🤝 合作伙伴评估 | 评估合作伙伴的匹配度和合作价值 |
+| 📦 供应商评估 | 对比分析供应商的综合能力 |
 
 ### 🎭 Debate Workshop (辩论工作坊)
-1.  在侧边栏选择模式为 **"Debate Workshop"**。
-2.  输入**决策议题** (例如: "我是否应该辞职创业？")。
-3.  选择参与辩论的 **AI 角色** (如: CEO, 风险控制专家, 职业顾问)。
-4.  点击 **"开始辩论"**。
-5.  观看不同角色之间的唇枪舌战，最后生成综合决策建议。
+让 AI 扮演不同立场角色，通过辩论帮助厘清复杂决策的利弊
+
+### 💬 用户反馈
+内置用户反馈系统，收集功能建议和使用体验
+
+### 🌐 多平台支持
+- **DeepSeek**: V3, R1, Coder
+- **OpenAI**: GPT-4o, GPT-4o-mini
+- **Anthropic**: Claude 3.5 Sonnet
+- **Google**: Gemini 1.5/2.0
+- **SiliconFlow / AIHubMix / Deepseek**
+
+---
+
+## 🛠️ 安装
+
+```bash
+# 克隆项目
+git clone https://github.com/HomoDeusss/multi-agent.git
+cd multi-agent
+
+# 初始化 uv 项目（如首次使用）
+uv init
+
+# 安装依赖
+uv add streamlit openai anthropic python-dotenv
+
+# 或者同步现有依赖
+uv sync
+```
+
+## 🚀 快速开始
+
+```bash
+uv run streamlit run app.py
+```
+
+### 使用步骤
+
+1. **配置 API**: 在侧边栏选择 Provider 并输入 API Key
+2. **选择场景**: 点击预置的决策场景或自定义主题
+3. **生成专家**: 点击 "🪄 根据主题自动生成专家" 或手动配置
+4. **开始决策**: 观察专家们如何互相对话，生成综合方案
+
+---
+
+## 📁 项目结构
+
+```
+multi_agent_workshop/
+├── app.py                 # Streamlit 主应用
+├── config.py              # 配置文件
+├── agents/                # Agent 定义
+│   ├── agent_profiles.py  # 预设角色配置
+│   ├── base_agent.py      # 基础 Agent 类
+│   └── research_agent.py  # 研究型 Agent
+├── orchestrator/          # 编排器
+│   ├── debate_manager.py  # 辩论管理
+│   └── research_manager.py # 智囊团管理
+├── utils/
+│   ├── llm_client.py      # LLM 客户端封装
+│   ├── storage.py         # 存储管理
+│   └── auto_agent_generator.py # 智能专家生成
+└── report/                # 报告生成
+```
 
 ## 📝 License
-
 [MIT License](LICENSE)

agents/base_agent.py

@@ -19,16 +19,18 @@ class AgentMessage:
 class BaseAgent:
     """Agent 基类"""
     
-    def __init__(self, agent_id: str, llm_client):
+    def __init__(self, agent_id: str, llm_client, language: str = "Chinese"):
         """
         初始化 Agent
         
         Args:
             agent_id: Agent 标识符 (如 'ceo', 'cto')
             llm_client: LLM 客户端实例
+            language: 输出语言
         """
         self.agent_id = agent_id
         self.llm_client = llm_client
+        self.language = language
         
         profile = get_agent_profile(agent_id)
         if not profile:
@@ -38,7 +40,7 @@ class BaseAgent:
         self.emoji = profile["emoji"]
         self.perspective = profile["perspective"]
         self.focus_areas = profile["focus_areas"]
-        self.system_prompt = profile["system_prompt"]
+        self.system_prompt = f"{profile['system_prompt']}\n\nIMPORTANT: You MUST output your response in {self.language}."
         
         # 存储对话历史
         self.conversation_history = []

agents/research_agent.py

@@ -5,26 +5,28 @@ import config
 class ResearchAgent:
     """研究模式专用 Agent"""
     
-    def __init__(self, role: str, llm_client: LLMClient, name: str = None):
+    def __init__(self, role: str, llm_client: LLMClient, name: str = None, language: str = "Chinese"):
         self.role = role
         self.llm_client = llm_client
         self.role_config = config.RESEARCH_MODEL_ROLES.get(role, {})
         self.name = name if name else self.role_config.get("name", role.capitalize())
+        self.language = language
         
     @property
     def model_name(self) -> str:
         return self.llm_client.model
 
     def _get_system_prompt(self, context: str = "") -> str:
+        base_prompt = ""
         if self.role == "council_member":
-            return f"""You are {self.name}, a member of the Multi-Model Decision Council.
+            base_prompt = f"""You are {self.name}, a member of the Multi-Model Decision Council.
 Your goal is to participate in a round-table discussion to solve the user's problem.
 Be conversational, insightful, and constructive.
 Build upon others' ideas or respectfully disagree with valid reasoning.
 Context: {context}"""
 
         elif self.role == "expert_a":
-            return f"""You are Expert A, a Senior Analyst.
+            base_prompt = f"""You are Expert A, a Senior Analyst.
 You are participating in a round-table discussion.
 Your goal is to analyze the topic and propose solutions.
 Be conversational, direct, and responsive to other experts. 
@@ -32,21 +34,23 @@ Do not write a full final report; focus on the current discussion turn.
 Context: {context}"""
         
         elif self.role == "expert_b":
-            return f"""You are Expert B, a Critical Reviewer.
+            base_prompt = f"""You are Expert B, a Critical Reviewer.
 You are participating in a round-table discussion.
 Your goal is to critique Expert A's points and offer alternative perspectives.
 Be conversational and constructive. Challenge assumptions directly.
 Context: {context}"""
         
         elif self.role == "expert_c":
-            return f"""You are Expert C, a Senior Strategist and Visual Thinker.
+            base_prompt = f"""You are Expert C, a Senior Strategist and Visual Thinker.
 Your goal is to synthesize the final output. 
 Combine the structural strength of Expert A with the critical insights of Expert B.
 Produce a final, polished, comprehensive plan or report.
 CRITICAL: You MUST include a Mermaid.js diagram (using ```mermaid code block) to visualize the timeline, process, or architecture."""
         
         else:
-            return "You are a helpful assistant."
+            base_prompt = "You are a helpful assistant."
+            
+        return f"{base_prompt}\n\nIMPORTANT: You MUST output your response in {self.language}."
 
     def generate(self, prompt: str, context: str = "") -> Generator[str, None, None]:
         """Generate response stream"""

config.py

@@ -9,7 +9,7 @@ load_dotenv()
 # API 配置
 ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
-AIHUBMIX_API_KEY = os.getenv("AIHUBMIX_API_KEY", "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73")
+AIHUBMIX_API_KEY = os.getenv("AIHUBMIX_API_KEY", "")
 DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "")
 SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY", "")
 
@@ -93,6 +93,12 @@ AVAILABLE_MODELS = {
 MAX_DEBATE_ROUNDS = 3  # 最大辩论轮数
 MAX_AGENTS = 6  # 最大参与 Agent 数量
 
+# 支持的输出语言
+SUPPORTED_LANGUAGES = ["Chinese", "English", "Japanese", "Spanish", "French", "German"]
+
+# 生成配置
+MAX_OUTPUT_TOKENS = 300  # 限制单次回复长度，保持精简
+
 # 研究模式模型角色配置
 RESEARCH_MODEL_ROLES = {
     "expert_a": {

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from multi-agent!")
+
+
+if __name__ == "__main__":
+    main()

orchestrator/debate_manager.py

@@ -18,6 +18,7 @@ class DebateConfig:
     agent_ids: List[str] = None
     max_rounds: int = 2
     agent_clients: dict = None # Map[agent_id, LLMClient]
+    language: str = "Chinese"
     
 
 @dataclass  
@@ -64,7 +65,7 @@ class DebateManager:
             if hasattr(debate_config, 'agent_clients') and debate_config.agent_clients and agent_id in debate_config.agent_clients:
                  client = debate_config.agent_clients[agent_id]
             
-            agent = BaseAgent(agent_id, client)
+            agent = BaseAgent(agent_id, client, language=debate_config.language)
             self.agents.append(agent)
     
     def run_debate_stream(

orchestrator/research_manager.py

@@ -10,6 +10,7 @@ class ResearchConfig:
     context: str = ""
     # Dynamic list of experts: [{"name": "Expert 1", "model": "gpt-4o", "role": "analyst"}, ...]
     experts: List[Dict[str, str]] = None 
+    language: str = "Chinese"
 
 class ResearchManager:
     """Manages the Multi-Model Council workflow"""
@@ -33,19 +34,12 @@ class ResearchManager:
         self.agents = []
         if config.experts:
             for idx, expert_conf in enumerate(config.experts):
-                # Assign role based on position or config
-                # First agents are discussion members, last one is Synthesizer usually, 
-                # but for equality we treat them all as members until the end.
-                # We'll assign a generic "member" role or specific if provided.
-                
                 role_type = "council_member"
-                # If it's the last one, maybe give them synthesizer duty? 
-                # For now, all are members, and we explicitly pick one for synthesis.
-                
                 agent = ResearchAgent(
                     role=role_type, 
                     llm_client=self._get_client(expert_conf["model"]),
-                    name=expert_conf.get("name", f"Expert {idx+1}")
+                    name=expert_conf.get("name", f"Expert {idx+1}"),
+                    language=config.language
                 )
                 self.agents.append(agent)
 

pyproject.toml

@@ -0,0 +1,13 @@
+[project]
+name = "multi-agent"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "anthropic>=0.75.0",
+    "openai>=2.14.0",
+    "pydantic>=2.12.5",
+    "python-dotenv>=1.2.1",
+    "streamlit>=1.52.2",
+]

utils/auto_agent_generator.py

@@ -0,0 +1,108 @@
+"""
+Auto Agent Generator - 根据主题自动生成专家配置
+Uses LLM to analyze the topic and suggest appropriate expert agents.
+"""
+import json
+import re
+from typing import List, Dict
+from utils.llm_client import LLMClient
+
+
+EXPERT_GENERATION_PROMPT = """You are an expert team composition advisor. Given a research/decision topic, you need to suggest the most appropriate team of experts to analyze it.
+
+Instructions:
+1. Analyze the topic carefully to understand its domain and key aspects
+2. Generate {num_experts} distinct expert roles that would provide the most valuable perspectives
+3. Each expert should have a unique focus area relevant to the topic
+4. The LAST expert should always be a "Synthesizer" role who can integrate all perspectives
+
+Output Format (MUST be valid JSON array):
+[
+  {{"name": "Expert Name", "perspective": "Brief description of their viewpoint", "focus": "Key areas they analyze"}},
+  ...
+]
+
+Examples of good expert names based on topic:
+- For "Should we launch an e-commerce platform?": "市场渠道分析师", "电商运营专家", "供应链顾问", "数字化转型综合师"
+- For "Career transition to AI field": "职业发展顾问", "AI行业专家", "技能评估分析师", "综合规划师"
+
+IMPORTANT:
+- Use {language} for all names and descriptions
+- Make names specific to the topic, not generic like "Expert 1"
+- The last expert MUST be a synthesizer/integrator type
+
+Topic: {topic}
+
+Generate exactly {num_experts} experts as a JSON array:"""
+
+
+def generate_experts_for_topic(
+    topic: str, 
+    num_experts: int, 
+    llm_client: LLMClient,
+    language: str = "Chinese"
+) -> List[Dict[str, str]]:
+    """
+    Use LLM to generate appropriate expert configurations based on the topic.
+    
+    Args:
+        topic: The research/decision topic
+        num_experts: Number of experts to generate (2-5)
+        llm_client: LLM client instance for API calls
+        language: Output language (Chinese/English)
+        
+    Returns:
+        List of expert dicts: [{"name": "...", "perspective": "...", "focus": "..."}, ...]
+    """
+    if not topic.strip():
+        return []
+    
+    prompt = EXPERT_GENERATION_PROMPT.format(
+        topic=topic,
+        num_experts=num_experts,
+        language=language
+    )
+    
+    try:
+        response = llm_client.chat(
+            system_prompt="You are a helpful assistant that generates JSON output only. No markdown, no explanation.",
+            user_prompt=prompt,
+            max_tokens=800
+        )
+        
+        # Extract JSON from response (handle potential markdown wrapping)
+        json_match = re.search(r'\[[\s\S]*\]', response)
+        if json_match:
+            experts = json.loads(json_match.group())
+            # Validate structure
+            if isinstance(experts, list) and len(experts) >= 1:
+                validated = []
+                for exp in experts[:num_experts]:
+                    if isinstance(exp, dict) and "name" in exp:
+                        validated.append({
+                            "name": exp.get("name", "Expert"),
+                            "perspective": exp.get("perspective", ""),
+                            "focus": exp.get("focus", "")
+                        })
+                return validated
+    except (json.JSONDecodeError, Exception) as e:
+        print(f"[AutoAgentGenerator] Error parsing LLM response: {e}")
+    
+    # Fallback: return generic experts
+    fallback = []
+    for i in range(num_experts):
+        if i == num_experts - 1:
+            fallback.append({"name": f"综合分析师", "perspective": "整合视角", "focus": "综合决策"})
+        else:
+            fallback.append({"name": f"专家 {i+1}", "perspective": "分析视角", "focus": "专业分析"})
+    return fallback
+
+
+def get_default_model_for_expert(expert_index: int, total_experts: int, available_models: list) -> str:
+    """
+    Assign a default model to an expert based on their position.
+    Spreads experts across available models for diversity.
+    """
+    if not available_models:
+        return "gpt-4o"
+    return available_models[expert_index % len(available_models)]

utils/llm_client.py

@@ -5,6 +5,8 @@ from typing import Generator
 import os
 
 
+import config
+
 class LLMClient:
     """LLM API 统一客户端"""
     
@@ -62,7 +64,7 @@ class LLMClient:
         self,
         system_prompt: str,
         user_prompt: str,
-        max_tokens: int = 1024
+        max_tokens: int = config.MAX_OUTPUT_TOKENS
     ) -> Generator[str, None, None]:
         """
         流式对话

utils/storage.py

@@ -0,0 +1,184 @@
+"""
+Storage Manager - Handle local persistence of configuration, history/reports, and assets.
+"""
+import os
+import json
+import time
+from typing import List, Dict, Any
+from pathlib import Path
+
+# Constants
+STORAGE_DIR = ".storage"
+CONFIG_FILE = "config.json"
+HISTORY_DIR = "history"
+ASSETS_DIR = "assets"
+
+class StorageManager:
+    def __init__(self):
+        self.root_dir = Path(STORAGE_DIR)
+        self.config_path = self.root_dir / CONFIG_FILE
+        self.history_dir = self.root_dir / HISTORY_DIR
+        self.assets_dir = self.root_dir / ASSETS_DIR
+        
+        # Ensure directories exist
+        self.root_dir.mkdir(exist_ok=True)
+        self.history_dir.mkdir(exist_ok=True)
+        self.assets_dir.mkdir(exist_ok=True)
+        
+    def save_config(self, config_data: Dict[str, Any]):
+        """Save UI configuration to file"""
+        try:
+            with open(self.config_path, 'w', encoding='utf-8') as f:
+                json.dump(config_data, f, indent=2, ensure_ascii=False)
+        except Exception as e:
+            print(f"Error saving config: {e}")
+
+    def load_config(self) -> Dict[str, Any]:
+        """Load UI configuration from file"""
+        if not self.config_path.exists():
+            return {}
+        try:
+            with open(self.config_path, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except Exception as e:
+            print(f"Error loading config: {e}")
+            return {}
+
+    def save_asset(self, uploaded_file) -> str:
+        """Save an uploaded file (e.g., background image) into assets directory.
+
+        Args:
+            uploaded_file: a file-like object (Streamlit UploadedFile) or bytes-like
+
+        Returns:
+            The saved file path as string, or None on failure.
+        """
+        try:
+            # Determine filename
+            if hasattr(uploaded_file, 'name'):
+                filename = uploaded_file.name
+            else:
+                filename = f"asset_{int(time.time())}"
+
+            # sanitize
+            safe_name = "".join([c for c in filename if c.isalnum() or c in (' ', '.', '_', '-')]).strip().replace(' ', '_')
+            dest = self.assets_dir / f"{int(time.time())}_{safe_name}"
+
+            # Write bytes
+            with open(dest, 'wb') as out:
+                # Streamlit UploadedFile has getbuffer()
+                if hasattr(uploaded_file, 'getbuffer'):
+                    out.write(uploaded_file.getbuffer())
+                else:
+                    # try reading
+                    data = uploaded_file.read()
+                    if isinstance(data, str):
+                        data = data.encode('utf-8')
+                    out.write(data)
+
+            return str(dest)
+        except Exception as e:
+            print(f"Error saving asset: {e}")
+            return None
+
+    def save_history(self, session_type: str, topic: str, content: str, metadata: Dict[str, Any] = None):
+        """
+        Save a session report/history
+        
+        Args:
+            session_type: 'council' or 'debate'
+            topic: The main topic
+            content: The full markdown report or content
+            metadata: Additional info (model used, date, etc)
+        """
+        timestamp = int(time.time())
+        date_str = time.strftime("%Y-%m-%d %H:%M:%S")
+        
+        # Create a safe filename
+        safe_topic = "".join([c for c in topic[:20] if c.isalnum() or c in (' ', '_', '-')]).strip().replace(' ', '_')
+        filename = f"{timestamp}_{session_type}_{safe_topic}.json"
+        
+        data = {
+            "id": str(timestamp),
+            "timestamp": timestamp,
+            "date": date_str,
+            "type": session_type,
+            "topic": topic,
+            "content": content,
+            "metadata": metadata or {}
+        }
+        
+        try:
+            with open(self.history_dir / filename, 'w', encoding='utf-8') as f:
+                json.dump(data, f, indent=2, ensure_ascii=False)
+            return True
+        except Exception as e:
+            print(f"Error saving history: {e}")
+            return False
+
+    def list_history(self) -> List[Dict[str, Any]]:
+        """List all history items (metadata only)"""
+        items = []
+        if not self.history_dir.exists():
+            return []
+            
+        for file in self.history_dir.glob("*.json"):
+            try:
+                with open(file, 'r', encoding='utf-8') as f:
+                    data = json.load(f)
+                    # Return summary info
+                    items.append({
+                        "id": data.get("id"),
+                        "date": data.get("date"),
+                        "type": data.get("type"),
+                        "topic": data.get("topic"),
+                        "filename": file.name
+                    })
+            except Exception:
+                continue
+                
+        # Sort by timestamp desc
+        return sorted(items, key=lambda x: x.get("date", ""), reverse=True)
+
+    def load_history_item(self, filename: str) -> Dict[str, Any]:
+        """Load full content of a history item"""
+        path = self.history_dir / filename
+        if not path.exists():
+            return None
+        try:
+            with open(path, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except Exception:
+            return None
+
+    # ==================== Session Cache (Resume Functionality) ====================
+    def save_session_state(self, key: str, data: Dict[str, Any]):
+        """Save temporary session state for recovery"""
+        try:
+            # We use a dedicated cache file per key
+            cache_file = self.root_dir / f"{key}_cache.json"
+            data["_timestamp"] = int(time.time())
+            with open(cache_file, 'w', encoding='utf-8') as f:
+                json.dump(data, f, indent=2, ensure_ascii=False)
+        except Exception as e:
+            print(f"Error saving session cache: {e}")
+
+    def load_session_state(self, key: str) -> Dict[str, Any]:
+        """Load temporary session state"""
+        cache_file = self.root_dir / f"{key}_cache.json"
+        if not cache_file.exists():
+            return None
+        try:
+            with open(cache_file, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except Exception:
+            return None
+
+    def clear_session_state(self, key: str):
+        """Clear temporary session state"""
+        cache_file = self.root_dir / f"{key}_cache.json"
+        if cache_file.exists():
+            try:
+                os.remove(cache_file)
+            except Exception:
+                pass