fix: 3 fixes for Step 2, Step 4, Step 5

1. Step 5: Use empty model_config_dict={} so camel-ai doesn't spread
   api_key into create() - AsyncOpenAI reads env vars automatically.
   Step 3 unaffected (same env vars, just cleaner ModelFactory call).

2. Step 2: Fix Thai text truncation - isalnum() stripped Thai chars.
   Use re.sub(r'[^\w]', '', username, re.UNICODE) instead.

3. Step 4: Move get_language_instruction() to START of prompt (not end)
   and strengthen wording with MUST/IMPORTANT prefix.

backend/app/services/oasis_profile_generator.py

@@ -274,12 +274,14 @@ class OasisProfileGenerator:
         )
     
     def _generate_username(self, name: str) -> str:
-        """生成用户名"""
+        """Generate a username from the entity name"""
-        # 移除特殊字符，转换为小写
+        # Keep Unicode characters (Thai, Chinese, etc) and convert to lowercase
         username = name.lower().replace(" ", "_")
-        username = ''.join(c for c in username if c.isalnum() or c == '_')
+        # Only remove truly problematic characters for usernames
+        import re
+        username = re.sub(r'[^\w]', '', username, flags=re.UNICODE)
         
-        # 添加随机后缀避免重复
+        # Add random suffix to avoid duplicates
         suffix = random.randint(100, 999)
         return f"{username}_{suffix}"
     

backend/app/services/report_agent.py

@@ -474,387 +474,386 @@ class Report:
 # ── 工具描述 ──
 
 TOOL_DESC_INSIGHT_FORGE = """\
-【深度洞察检索 - 强大的检索工具】
+[Deep Insight Retrieval - Powerful Analysis Tool]
-这是我们强大的检索函数，专为深度分析设计。它会：
+This is our powerful retrieval function designed for deep analysis. It will:
-1. 自动将你的问题分解为多个子问题
+1. Automatically decompose your question into multiple sub-questions
-2. 从多个维度检索模拟图谱中的信息
+2. Retrieve information from multiple dimensions in the simulation graph
-3. 整合语义搜索、实体分析、关系链追踪的结果
+3. Integrate results from semantic search, entity analysis, and relationship chain tracking
-4. 返回最全面、最深度的检索内容
+4. Return the most comprehensive and in-depth retrieval content
 
-【使用场景】
+[Use Cases]
-- 需要深入分析某个话题
+- Deep analysis of a specific topic
-- 需要了解事件的多个方面
+- Understanding multiple aspects of an event
-- 需要获取支撑报告章节的丰富素材
+- Gathering rich material to support report sections
 
-【返回内容】
+[Returned Content]
-- 相关事实原文（可直接引用）
+- Relevant original facts (can be directly quoted)
-- 核心实体洞察
+- Core entity insights
-- 关系链分析"""
+- Relationship chain analysis"""
 
 TOOL_DESC_PANORAMA_SEARCH = """\
-【广度搜索 - 获取全貌视图】
+[Panorama Search - Get the Full Picture]
-这个工具用于获取模拟结果的完整全貌，特别适合了解事件演变过程。它会：
+This tool retrieves the complete picture of simulation results, especially useful for understanding event evolution. It will:
-1. 获取所有相关节点和关系
+1. Retrieve all related nodes and relationships
-2. 区分当前有效的事实和历史/过期的事实
+2. Distinguish between currently valid facts and historical/expired facts
-3. 帮助你了解舆情是如何演变的
+3. Help you understand how public sentiment evolved
 
-【使用场景】
+[Use Cases]
-- 需要了解事件的完整发展脉络
+- Understanding the full development timeline of an event
-- 需要对比不同阶段的舆情变化
+- Comparing sentiment changes across different stages
-- 需要获取全面的实体和关系信息
+- Getting comprehensive entity and relationship information
 
-【返回内容】
+[Returned Content]
-- 当前有效事实（模拟最新结果）
+- Currently valid facts (latest simulation results)
-- 历史/过期事实（演变记录）
+- Historical/expired facts (evolution records)
-- 所有涉及的实体"""
+- All involved entities"""
 
 TOOL_DESC_QUICK_SEARCH = """\
-【简单搜索 - 快速检索】
+[Quick Search - Fast Retrieval]
-轻量级的快速检索工具，适合简单、直接的信息查询。
+A lightweight fast retrieval tool suitable for simple, direct information queries.
 
-【使用场景】
+[Use Cases]
-- 需要快速查找某个具体信息
+- Quickly finding specific information
-- 需要验证某个事实
+- Verifying a fact
-- 简单的信息检索
+- Simple information retrieval
 
-【返回内容】
+[Returned Content]
-- 与查询最相关的事实列表"""
+- List of facts most relevant to the query"""
 
 TOOL_DESC_INTERVIEW_AGENTS = """\
-【深度采访 - 真实Agent采访（双平台）】
+[Deep Interview - Real Agent Interview (Dual Platform)]
-调用OASIS模拟环境的采访API，对正在运行的模拟Agent进行真实采访！
+Calls the OASIS simulation environment's interview API to conduct real interviews with running simulation agents!
-这不是LLM模拟，而是调用真实的采访接口获取模拟Agent的原始回答。
+This is NOT LLM simulation - it calls the real interview API to get original responses from simulation agents.
-默认在Twitter和Reddit两个平台同时采访，获取更全面的观点。
+By default, interviews are conducted simultaneously on both Twitter and Reddit platforms for more comprehensive perspectives.
 
-功能流程：
+Workflow:
-1. 自动读取人设文件，了解所有模拟Agent
+1. Automatically reads persona files to understand all simulation agents
-2. 智能选择与采访主题最相关的Agent（如学生、媒体、官方等）
+2. Intelligently selects agents most relevant to the interview topic (e.g., students, media, officials)
-3. 自动生成采访问题
+3. Automatically generates interview questions
-4. 调用 /api/simulation/interview/batch 接口在双平台进行真实采访
+4. Calls the /api/simulation/interview/batch endpoint for real interviews on both platforms
-5. 整合所有采访结果，提供多视角分析
+5. Consolidates all interview results and provides multi-perspective analysis
 
-【使用场景】
+[Use Cases]
-- 需要从不同角色视角了解事件看法（学生怎么看？媒体怎么看？官方怎么说？）
+- Understanding event perspectives from different role viewpoints (What do students think? What about media? What do officials say?)
-- 需要收集多方意见和立场
+- Collecting opinions and positions from multiple parties
-- 需要获取模拟Agent的真实回答（来自OASIS模拟环境）
+- Getting real responses from simulation agents (from OASIS simulation environment)
-- 想让报告更生动，包含"采访实录"
+- Making reports more vivid with "interview transcripts"
 
-【返回内容】
+[Returned Content]
-- 被采访Agent的身份信息
+- Identity information of interviewed agents
-- 各Agent在Twitter和Reddit两个平台的采访回答
+- Interview responses from each agent on both Twitter and Reddit platforms
-- 关键引言（可直接引用）
+- Key quotes (can be directly quoted)
-- 采访摘要和观点对比
+- Interview summary and perspective comparison
 
-【重要】需要OASIS模拟环境正在运行才能使用此功能！"""
+[Important] Requires the OASIS simulation environment to be running!"""
 
 # ── 大纲规划 prompt ──
 
 PLAN_SYSTEM_PROMPT = """\
-你是一个「未来预测报告」的撰写专家，拥有对模拟世界的「上帝视角」——你可以洞察模拟中每一位Agent的行为、言论和互动。
+You are a "Future Prediction Report" writing expert with a "God's-eye view" of the simulation world — you can observe the behavior, speech, and interactions of every Agent in the simulation.
 
-【核心理念】
+[Core Philosophy]
-我们构建了一个模拟世界，并向其中注入了特定的「模拟需求」作为变量。模拟世界的演化结果，就是对未来可能发生情况的预测。你正在观察的不是"实验数据"，而是"未来的预演"。
+We have built a simulation world and injected a specific "simulation requirement" as a variable. The evolution of the simulation world represents a prediction of what may happen in the future. What you are observing is not "experimental data" but a "rehearsal of the future."
 
-【你的任务】
+[Your Task]
-撰写一份「未来预测报告」，回答：
+Write a "Future Prediction Report" that answers:
-1. 在我们设定的条件下，未来发生了什么？
+1. Under the conditions we set, what happened in the future?
-2. 各类Agent（人群）是如何反应和行动？
+2. How did various groups of Agents (people) react and act?
-3. 这个模拟揭示了哪些值得关注的未来趋势和风险？
+3. What noteworthy future trends and risks does this simulation reveal?
 
-【报告定位】
+[Report Positioning]
-- ✅ 这是一份基于模拟的未来预测报告，揭示"如果这样，未来会怎样"
+- ✅ This is a simulation-based future prediction report, revealing "if this, then what happens in the future"
-- ✅ 聚焦于预测结果：事件走向、群体反应、涌现现象、潜在风险
+- ✅ Focus on prediction outcomes: event trajectory, group reactions, emergent phenomena, potential risks
-- ✅ 模拟世界中的Agent言行就是对未来人群行为的预测
+- ✅ Agent behavior and speech in the simulation world represent predictions of future crowd behavior
-- ❌ 不是对现实世界现状的分析
+- ❌ NOT an analysis of the current state of the real world
-- ❌ 不是泛泛而谈的舆情综述
+- ❌ NOT a generic public sentiment overview
 
-【章节数量限制】
+[Chapter Count Limits]
-- 最少2个章节，最多5个章节
+- Minimum 2 chapters, maximum 5 chapters
-- 不需要子章节，每个章节直接撰写完整内容
+- No sub-chapters needed; each chapter should directly write complete content
-- 内容要精炼，聚焦于核心预测发现
+- Content should be concise, focused on core prediction findings
-- 章节结构由你根据预测结果自主设计
+- Chapter structure should be designed by you based on prediction results
 
-请输出JSON格式的报告大纲，格式如下：
+Please output the report outline in JSON format as follows:
 {
-    "title": "报告标题",
+    "title": "Report Title",
-    "summary": "报告摘要（一句话概括核心预测发现）",
+    "summary": "Report Summary (one sentence summarizing core prediction findings)",
     "sections": [
         {
-            "title": "章节标题",
+            "title": "Section Title",
-            "description": "章节内容描述"
+            "description": "Section Content Description"
         }
     ]
 }
 
-注意：sections数组最少2个，最多5个元素！"""
+Note: The sections array must have a minimum of 2 and maximum of 5 elements!"""
 
 PLAN_USER_PROMPT_TEMPLATE = """\
-【预测场景设定】
+[Prediction Scenario Setup]
-我们向模拟世界注入的变量（模拟需求）：{simulation_requirement}
+The variable (simulation requirement) we injected into the simulation world: {simulation_requirement}
 
-【模拟世界规模】
+[Simulation World Scale]
-- 参与模拟的实体数量: {total_nodes}
+- Number of entities participating in simulation: {total_nodes}
-- 实体间产生的关系数量: {total_edges}
+- Number of relationships generated between entities: {total_edges}
-- 实体类型分布: {entity_types}
+- Entity type distribution: {entity_types}
-- 活跃Agent数量: {total_entities}
+- Number of active Agents: {total_entities}
 
-【模拟预测到的部分未来事实样本】
+[Sample of Predicted Future Facts from the Simulation]
 {related_facts_json}
 
-请以「上帝视角」审视这个未来预演：
+Please examine this future rehearsal from a "God's-eye view":
-1. 在我们设定的条件下，未来呈现出了什么样的状态？
+1. Under the conditions we set, what state has the future taken on?
-2. 各类人群（Agent）是如何反应和行动的？
+2. How are various groups of people (Agents) reacting and acting?
-3. 这个模拟揭示了哪些值得关注的未来趋势？
+3. What noteworthy future trends does this simulation reveal?
 
-根据预测结果，设计最合适的报告章节结构。
+Design the most appropriate report section structure based on the prediction results.
 
-【再次提醒】报告章节数量：最少2个，最多5个，内容要精炼聚焦于核心预测发现。"""
+[Reminder] Report chapter count: minimum 2, maximum 5. Content should be concise and focused on core prediction findings."""
 
 # ── 章节生成 prompt ──
 
 SECTION_SYSTEM_PROMPT_TEMPLATE = """\
-你是一个「未来预测报告」的撰写专家，正在撰写报告的一个章节。
+You are a "Future Prediction Report" writing expert, currently writing a section of the report.
 
-报告标题: {report_title}
+Report Title: {report_title}
-报告摘要: {report_summary}
+Report Summary: {report_summary}
-预测场景（模拟需求）: {simulation_requirement}
+Prediction Scenario (Simulation Requirement): {simulation_requirement}
 
-当前要撰写的章节: {section_title}
+Section currently being written: {section_title}
 
 ═══════════════════════════════════════════════════════════════
-【核心理念】
+[Core Philosophy]
 ═══════════════════════════════════════════════════════════════
 
-模拟世界是对未来的预演。我们向模拟世界注入了特定条件（模拟需求），
+The simulation world is a rehearsal of the future. We injected specific conditions (simulation requirement) into the simulation world. The behavior and interactions of Agents in the simulation represent predictions of future crowd behavior.
-模拟中Agent的行为和互动，就是对未来人群行为的预测。
 
-你的任务是：
+Your task is to:
-- 揭示在设定条件下，未来发生了什么
+- Reveal what happened in the future under the set conditions
-- 预测各类人群（Agent）是如何反应和行动的
+- Predict how various groups of people (Agents) react and act
-- 发现值得关注的未来趋势、风险和机会
+- Discover noteworthy future trends, risks, and opportunities
 
-❌ 不要写成对现实世界现状的分析
+❌ Do NOT write this as an analysis of the current state of the real world
-✅ 要聚焦于"未来会怎样"——模拟结果就是预测的未来
+✅ Focus on "what will happen in the future" — the simulation results ARE the predicted future
 
 ═══════════════════════════════════════════════════════════════
-【最重要的规则 - 必须遵守】
+[Most Important Rules - Must Be Followed]
 ═══════════════════════════════════════════════════════════════
 
-1. 【必须调用工具观察模拟世界】
+1. [Must Call Tools to Observe the Simulation World]
-   - 你正在以「上帝视角」观察未来的预演
+   - You are observing the future rehearsal from a "God's-eye view"
-   - 所有内容必须来自模拟世界中发生的事件和Agent言行
+   - All content must come from events and Agent behavior occurring in the simulation world
-   - 禁止使用你自己的知识来编写报告内容
+   - Do NOT use your own knowledge to write report content
-   - 每个章节至少调用3次工具（最多5次）来观察模拟的世界，它代表了未来
+   - Each section must call tools at least 3 times (maximum 5) to observe the simulated world, which represents the future
 
-2. 【必须引用Agent的原始言行】
+2. [Must Quote Agent's Original Words and Actions]
-   - Agent的发言和行为是对未来人群行为的预测
+   - Agent speech and behavior are predictions of future crowd behavior
-   - 在报告中使用引用格式展示这些预测，例如：
+   - Display these predictions using quote format in the report, for example:
-     > "某类人群会表示：原文内容..."
+     > "A certain group of people would say: original content..."
-   - 这些引用是模拟预测的核心证据
+   - These quotes are core evidence for simulation predictions
 
-3. 【语言一致性 - 引用内容必须翻译为报告语言】
+3. [Language Consistency - Quoted Content Must Be Translated to Report Language]
-   - 工具返回的内容可能包含与报告语言不同的表述
+   - Content returned by tools may contain expressions in a different language than the report
-   - 报告必须全部使用与用户指定语言一致的语言撰写
+   - The entire report must be written in the language specified by the user
-   - 当你引用工具返回的其他语言内容时，必须将其翻译为报告语言后再写入
+   - When quoting content returned by tools in other languages, you must translate it into the report language before including it
-   - 翻译时保持原意不变，确保表述自然通顺
+   - Maintain the original meaning during translation and ensure natural expression
-   - 这一规则同时适用于正文和引用块（> 格式）中的内容
+   - This rule applies to both body text and quote blocks (> format)
 
-4. 【忠实呈现预测结果】
+4. [Faithfully Present Prediction Results]
-   - 报告内容必须反映模拟世界中的代表未来的模拟结果
+   - Report content must reflect the simulation results representing the future in the simulation world
-   - 不要添加模拟中不存在的信息
+   - Do not add information that does not exist in the simulation
-   - 如果某方面信息不足，如实说明
+   - If information on a certain aspect is insufficient, state so honestly
 
 ═══════════════════════════════════════════════════════════════
-【⚠️ 格式规范 - 极其重要！】
+[⚠️ Format Requirements - Extremely Important!]
 ═══════════════════════════════════════════════════════════════
 
-【一个章节 = 最小内容单位】
+[One Section = Minimum Content Unit]
-- 每个章节是报告的最小分块单位
+- Each section is the minimum chunk unit of the report
-- ❌ 禁止在章节内使用任何 Markdown 标题（#、##、###、#### 等）
+- ❌ Do NOT use any Markdown headings (#, ##, ###, #### etc.) within a section
-- ❌ 禁止在内容开头添加章节主标题
+- ❌ Do NOT add the section main title at the beginning of the content
-- ✅ 章节标题由系统自动添加，你只需撰写纯正文内容
+- ✅ Section titles are automatically added by the system; you only need to write plain body content
-- ✅ 使用**粗体**、段落分隔、引用、列表来组织内容，但不要用标题
+- ✅ Use **bold text**, paragraph breaks, quotes, and lists to organize content, but do NOT use headings
 
-【正确示例】
+[Correct Example]
 ```
-本章节分析了事件的舆论传播态势。通过对模拟数据的深入分析，我们发现...
+This section analyzes the public sentiment propagation dynamics of the event. Through in-depth analysis of simulation data, we found...
 
-**首发引爆阶段**
+**Initial Ignition Phase**
 
-微博作为舆情的第一现场，承担了信息首发的核心功能：
+Platform A served as the first site of public sentiment, carrying the core function of information release:
 
-> "微博贡献了68%的首发声量..."
+> "Platform A contributed 68% of the initial volume..."
 
-**情绪放大阶段**
+**Emotion Amplification Phase**
 
-抖音平台进一步放大了事件影响力：
+Platform B further amplified the event's impact:
 
-- 视觉冲击力强
+- Strong visual impact
-- 情绪共鸣度高
+- High emotional resonance
 ```
 
-【错误示例】
+[Incorrect Example]
 ```
-## 执行摘要          ← 错误！不要添加任何标题
+## Executive Summary          ← WRONG! Do not add any headings
-### 一、首发阶段     ← 错误！不要用###分小节
+### Part 1: Initial Phase     ← WRONG! Do not use ### for sub-sections
-#### 1.1 详细分析   ← 错误！不要用####细分
+#### 1.1 Detailed Analysis   ← WRONG! Do not use #### for sub-divisions
 
-本章节分析了...
+This section analyzes...
 ```
 
 ═══════════════════════════════════════════════════════════════
-【可用检索工具】（每章节调用3-5次）
+[Available Retrieval Tools] (Call 3-5 times per section)
 ═══════════════════════════════════════════════════════════════
 
 {tools_description}
 
-【工具使用建议 - 请混合使用不同工具，不要只用一种】
+[Tool Usage Tips - Please mix different tools, do not use only one]
-- insight_forge: 深度洞察分析，自动分解问题并多维度检索事实和关系
+- insight_forge: Deep insight analysis, automatically decomposes questions and retrieves facts and relationships from multiple dimensions
-- panorama_search: 广角全景搜索，了解事件全貌、时间线和演变过程
+- panorama_search: Panoramic search to understand event overview, timeline, and evolution process
-- quick_search: 快速验证某个具体信息点
+- quick_search: Quickly verify a specific piece of information
-- interview_agents: 采访模拟Agent，获取不同角色的第一人称观点和真实反应
+- interview_agents: Interview simulation agents to get first-person perspectives and real reactions from different roles
 
 ═══════════════════════════════════════════════════════════════
-【工作流程】
+[Workflow]
 ═══════════════════════════════════════════════════════════════
 
-每次回复你只能做以下两件事之一（不可同时做）：
+Each response you can only do ONE of the following two things (NOT both):
 
-选项A - 调用工具：
+Option A - Call a tool:
-输出你的思考，然后用以下格式调用一个工具：
+Output your thoughts, then call a tool using the following format:
 <tool_call>
-{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
+{{"name": "tool_name", "parameters": {{"param_name": "param_value"}}}}
 </tool_call>
-系统会执行工具并把结果返回给你。你不需要也不能自己编写工具返回结果。
+The system will execute the tool and return the result to you. You do not need to and cannot write tool results yourself.
 
-选项B - 输出最终内容：
+Option B - Output final content:
-当你已通过工具获取了足够信息，以 "Final Answer:" 开头输出章节内容。
+When you have gathered enough information through tools, output the section content starting with "Final Answer:".
 
-⚠️ 严格禁止：
+⚠️ Strictly prohibited:
-- 禁止在一次回复中同时包含工具调用和 Final Answer
+- Do NOT include both a tool call and Final Answer in the same response
-- 禁止自己编造工具返回结果（Observation），所有工具结果由系统注入
+- Do NOT fabricate tool results (Observation); all tool results are injected by the system
-- 每次回复最多调用一个工具
+- Each response can call at most one tool
 
 ═══════════════════════════════════════════════════════════════
-【章节内容要求】
+[Section Content Requirements]
 ═══════════════════════════════════════════════════════════════
 
-1. 内容必须基于工具检索到的模拟数据
+1. Content must be based on simulation data retrieved by tools
-2. 大量引用原文来展示模拟效果
+2. Extensively quote original text to demonstrate simulation results
-3. 使用Markdown格式（但禁止使用标题）：
+3. Use Markdown format (but headings are prohibited):
-   - 使用 **粗体文字** 标记重点（代替子标题）
+   - Use **bold text** to highlight key points (replacing sub-headings)
-   - 使用列表（-或1.2.3.）组织要点
+   - Use lists (- or 1.2.3.) to organize key points
-   - 使用空行分隔不同段落
+   - Use blank lines to separate paragraphs
-   - ❌ 禁止使用 #、##、###、#### 等任何标题语法
+   - ❌ Do NOT use #, ##, ###, #### or any heading syntax
-4. 【引用格式规范 - 必须单独成段】
+4. [Quote Format Requirements - Must Be Standalone Paragraphs]
-   引用必须独立成段，前后各有一个空行，不能混在段落中：
+   Quotes must be standalone paragraphs with blank lines before and after, not mixed into regular paragraphs:
 
-   ✅ 正确格式：
+   ✅ Correct format:
    ```
-   校方的回应被认为缺乏实质内容。
+   The institution's response was considered lacking in substance.
 
-   > "校方的应对模式在瞬息万变的社交媒体环境中显得僵化和迟缓。"
+   > "The institution's response mode appears rigid and slow in the rapidly changing social media environment."
 
-   这一评价反映了公众的普遍不满。
+   This assessment reflects widespread public dissatisfaction.
    ```
 
-   ❌ 错误格式：
+   ❌ Incorrect format:
    ```
-   校方的回应被认为缺乏实质内容。> "校方的应对模式..." 这一评价反映了...
+   The institution's response was considered lacking in substance.> "The institution's response mode..." This assessment reflects...
    ```
-5. 保持与其他章节的逻辑连贯性
+5. Maintain logical coherence with other sections
-6. 【避免重复】仔细阅读下方已完成的章节内容，不要重复描述相同的信息
+6. [Avoid Repetition] Carefully read the content of completed sections below and do not repeat the same information
-7. 【再次强调】不要添加任何标题！用**粗体**代替小节标题"""
+7. [Emphasize Again] Do NOT add any headings! Use **bold text** instead of sub-section headings"""
 
 SECTION_USER_PROMPT_TEMPLATE = """\
-已完成的章节内容（请仔细阅读，避免重复）：
+Completed section content (please read carefully to avoid repetition):
 {previous_content}
 
 ═══════════════════════════════════════════════════════════════
-【当前任务】撰写章节: {section_title}
+[Current Task] Write Section: {section_title}
 ═══════════════════════════════════════════════════════════════
 
-【重要提醒】
+[Important Reminders]
-1. 仔细阅读上方已完成的章节，避免重复相同的内容！
+1. Carefully read the completed sections above to avoid repeating the same content!
-2. 开始前必须先调用工具获取模拟数据
+2. You must call tools to retrieve simulation data before starting
-3. 请混合使用不同工具，不要只用一种
+3. Please mix different tools, do not use only one
-4. 报告内容必须来自检索结果，不要使用自己的知识
+4. Report content must come from retrieval results, do not use your own knowledge
 
-【⚠️ 格式警告 - 必须遵守】
+[⚠️ Format Warning - Must Be Followed]
-- ❌ 不要写任何标题（#、##、###、####都不行）
+- ❌ Do not write any headings (#, ##, ###, #### are all prohibited)
-- ❌ 不要写"{section_title}"作为开头
+- ❌ Do not write "{section_title}" as the beginning
-- ✅ 章节标题由系统自动添加
+- ✅ Section titles are automatically added by the system
-- ✅ 直接写正文，用**粗体**代替小节标题
+- ✅ Write body text directly, use **bold text** instead of sub-section headings
 
-请开始：
+Please begin:
-1. 首先思考（Thought）这个章节需要什么信息
+1. First think (Thought) about what information this section needs
-2. 然后调用工具（Action）获取模拟数据
+2. Then call a tool (Action) to retrieve simulation data
-3. 收集足够信息后输出 Final Answer（纯正文，无任何标题）"""
+3. After gathering enough information, output Final Answer (plain body text, no headings)"""
 
 # ── ReACT 循环内消息模板 ──
 
 REACT_OBSERVATION_TEMPLATE = """\
-Observation（检索结果）:
+Observation (Retrieval Result):
 
-═══ 工具 {tool_name} 返回 ═══
+═══ Tool {tool_name} Returned ═══
 {result}
 
 ═══════════════════════════════════════════════════════════════
-已调用工具 {tool_calls_count}/{max_tool_calls} 次（已用: {used_tools_str}）{unused_hint}
+Called tools {tool_calls_count}/{max_tool_calls} times (used: {used_tools_str}){unused_hint}
-- 如果信息充分：以 "Final Answer:" 开头输出章节内容（必须引用上述原文）
+- If information is sufficient: output section content starting with "Final Answer:" (must quote the above original text)
-- 如果需要更多信息：调用一个工具继续检索
+- If more information is needed: call a tool to continue retrieving
 ═══════════════════════════════════════════════════════════════"""
 
 REACT_INSUFFICIENT_TOOLS_MSG = (
-    "【注意】你只调用了{tool_calls_count}次工具，至少需要{min_tool_calls}次。"
+    "[Note] You have only called tools {tool_calls_count} time(s), at least {min_tool_calls} required. "
-    "请再调用工具获取更多模拟数据，然后再输出 Final Answer。{unused_hint}"
+    "Please call tools to get more simulation data before outputting Final Answer. {unused_hint}"
 )
 
 REACT_INSUFFICIENT_TOOLS_MSG_ALT = (
-    "当前只调用了 {tool_calls_count} 次工具，至少需要 {min_tool_calls} 次。"
+    "Currently only called tools {tool_calls_count} time(s), at least {min_tool_calls} required. "
-    "请调用工具获取模拟数据。{unused_hint}"
+    "Please call a tool to retrieve simulation data. {unused_hint}"
 )
 
 REACT_TOOL_LIMIT_MSG = (
-    "工具调用次数已达上限（{tool_calls_count}/{max_tool_calls}），不能再调用工具。"
+    "Tool call limit reached ({tool_calls_count}/{max_tool_calls}), no more tools can be called. "
-    '请立即基于已获取的信息，以 "Final Answer:" 开头输出章节内容。'
+    'Please immediately output section content starting with "Final Answer:" based on the information already gathered.'
 )
 
-REACT_UNUSED_TOOLS_HINT = "\n💡 你还没有使用过: {unused_list}，建议尝试不同工具获取多角度信息"
+REACT_UNUSED_TOOLS_HINT = "\n💡 You haven't used: {unused_list}, try different tools for multi-angle information"
 
-REACT_FORCE_FINAL_MSG = "已达到工具调用限制，请直接输出 Final Answer: 并生成章节内容。"
+REACT_FORCE_FINAL_MSG = "Tool call limit reached. Please directly output Final Answer: and generate section content."
 
 # ── Chat prompt ──
 
 CHAT_SYSTEM_PROMPT_TEMPLATE = """\
-你是一个简洁高效的模拟预测助手。
+You are a concise and efficient simulation prediction assistant.
 
-【背景】
+[Background]
-预测条件: {simulation_requirement}
+Prediction Condition: {simulation_requirement}
 
-【已生成的分析报告】
+[Generated Analysis Report]
 {report_content}
 
-【规则】
+[Rules]
-1. 优先基于上述报告内容回答问题
+1. Prioritize answering questions based on the report content above
-2. 直接回答问题，避免冗长的思考论述
+2. Answer directly, avoid lengthy reasoning
-3. 仅在报告内容不足以回答时，才调用工具检索更多数据
+3. Only call tools to retrieve more data when the report content is insufficient to answer
-4. 回答要简洁、清晰、有条理
+4. Answers should be concise, clear, and well-organized
 
-【可用工具】（仅在需要时使用，最多调用1-2次）
+[Available Tools] (use only when needed, maximum 1-2 calls)
 {tools_description}
 
-【工具调用格式】
+[Tool Call Format]
 <tool_call>
-{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
+{{"name": "tool_name", "parameters": {{"param_name": "param_value"}}}}
 </tool_call>
 
-【回答风格】
+[Response Style]
-- 简洁直接，不要长篇大论
+- Concise and direct, avoid lengthy explanations
-- 使用 > 格式引用关键内容
+- Use > format to quote key content
-- 优先给出结论，再解释原因"""
+- Lead with conclusions, then explain reasoning"""
 
-CHAT_OBSERVATION_SUFFIX = "\n\n请简洁回答问题。"
+CHAT_OBSERVATION_SUFFIX = "\n\nPlease answer the question concisely."
 
 
 # ═══════════════════════════════════════════════════════════════
@@ -923,32 +922,32 @@ class ReportAgent:
                 "name": "insight_forge",
                 "description": TOOL_DESC_INSIGHT_FORGE,
                 "parameters": {
-                    "query": "你想深入分析的问题或话题",
+                    "query": "The question or topic you want to analyze in depth",
-                    "report_context": "当前报告章节的上下文（可选，有助于生成更精准的子问题）"
+                    "report_context": "Current report section context (optional, helps generate more precise sub-questions)"
                 }
             },
             "panorama_search": {
                 "name": "panorama_search",
                 "description": TOOL_DESC_PANORAMA_SEARCH,
                 "parameters": {
-                    "query": "搜索查询，用于相关性排序",
+                    "query": "Search query, used for relevance ranking",
-                    "include_expired": "是否包含过期/历史内容（默认True）"
+                    "include_expired": "Whether to include expired/historical content (default True)"
                 }
             },
             "quick_search": {
                 "name": "quick_search",
                 "description": TOOL_DESC_QUICK_SEARCH,
                 "parameters": {
-                    "query": "搜索查询字符串",
+                    "query": "Search query string",
-                    "limit": "返回结果数量（可选，默认10）"
+                    "limit": "Number of results to return (optional, default 10)"
                 }
             },
             "interview_agents": {
                 "name": "interview_agents",
                 "description": TOOL_DESC_INTERVIEW_AGENTS,
                 "parameters": {
-                    "interview_topic": "采访主题或需求描述（如：'了解学生对宿舍甲醛事件的看法'）",
+                    "interview_topic": "Interview topic or requirement description (e.g., 'understand student views on the dormitory incident')",
-                    "max_agents": "最多采访的Agent数量（可选，默认5，最大10）"
+                    "max_agents": "Maximum number of agents to interview (optional, default 5, max 10)"
                 }
             }
         }
@@ -1055,11 +1054,11 @@ class ReportAgent:
                 return json.dumps(result, ensure_ascii=False, indent=2)
             
             else:
-                return f"未知工具: {tool_name}。请使用以下工具之一: insight_forge, panorama_search, quick_search"
+                return f"Unknown tool: {tool_name}. Please use one of the following tools: insight_forge, panorama_search, quick_search"
                 
         except Exception as e:
             logger.error(t('report.toolExecFailed', toolName=tool_name, error=str(e)))
-            return f"工具执行失败: {str(e)}"
+            return f"Tool execution failed: {str(e)}"
     
     # 合法的工具名称集合，用于裸 JSON 兜底解析时校验
     VALID_TOOL_NAMES = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}
@@ -1126,12 +1125,12 @@ class ReportAgent:
     
     def _get_tools_description(self) -> str:
         """生成工具描述文本"""
-        desc_parts = ["可用工具："]
+        desc_parts = ["Available tools:"]
         for name, tool in self.tools.items():
             params_desc = ", ".join([f"{k}: {v}" for k, v in tool["parameters"].items()])
             desc_parts.append(f"- {name}: {tool['description']}")
             if params_desc:
-                desc_parts.append(f"  参数: {params_desc}")
+                desc_parts.append(f"  Parameters: {params_desc}")
         return "\n".join(desc_parts)
     
     def plan_outline(
@@ -1163,7 +1162,7 @@ class ReportAgent:
         if progress_callback:
             progress_callback("planning", 30, t('progress.generatingOutline'))
         
-        system_prompt = f"{PLAN_SYSTEM_PROMPT}\n\n{get_language_instruction()}"
+        system_prompt = f"{get_language_instruction()}\n\n{PLAN_SYSTEM_PROMPT}"
         user_prompt = PLAN_USER_PROMPT_TEMPLATE.format(
             simulation_requirement=self.simulation_requirement,
             total_nodes=context.get('graph_statistics', {}).get('total_nodes', 0),
@@ -1209,12 +1208,12 @@ class ReportAgent:
             logger.error(t('report.outlinePlanFailed', error=str(e)))
             # 返回默认大纲（3个章节，作为fallback）
             return ReportOutline(
-                title="未来预测报告",
+                title="Future Prediction Report",
-                summary="基于模拟预测的未来趋势与风险分析",
+                summary="Future trends and risk analysis based on simulation predictions",
                 sections=[
-                    ReportSection(title="预测场景与核心发现"),
+                    ReportSection(title="Prediction Scenario and Core Findings"),
-                    ReportSection(title="人群行为预测分析"),
+                    ReportSection(title="Crowd Behavior Prediction Analysis"),
-                    ReportSection(title="趋势展望与风险提示")
+                    ReportSection(title="Trend Outlook and Risk Warnings")
                 ]
             )
     
@@ -1259,7 +1258,7 @@ class ReportAgent:
             section_title=section.title,
             tools_description=self._get_tools_description(),
         )
-        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
+        system_prompt = f"{get_language_instruction()}\n\n{system_prompt}"
 
         # 构建用户prompt - 每个已完成章节各传入最大4000字
         if previous_sections:
@@ -1270,7 +1269,7 @@ class ReportAgent:
                 previous_parts.append(truncated)
             previous_content = "\n\n---\n\n".join(previous_parts)
         else:
-            previous_content = "（这是第一个章节）"
+            previous_content = "(This is the first section)"
         
         user_prompt = SECTION_USER_PROMPT_TEMPLATE.format(
             previous_content=previous_content,
@@ -1314,7 +1313,7 @@ class ReportAgent:
                 # 如果还有迭代次数，添加消息并重试
                 if iteration < max_iterations - 1:
                     messages.append({"role": "assistant", "content": "（响应为空）"})
-                    messages.append({"role": "user", "content": "请继续生成内容。"})
+                    messages.append({"role": "user", "content": "Please continue generating content."})
                     continue
                 # 最后一次迭代也返回 None，跳出循环进入强制收尾
                 break
@@ -1339,11 +1338,11 @@ class ReportAgent:
                     messages.append({
                         "role": "user",
                         "content": (
-                            "【格式错误】你在一次回复中同时包含了工具调用和 Final Answer，这是不允许的。\n"
+                            "[Format Error] You included both a tool call and Final Answer in the same response, which is not allowed.\n"
-                            "每次回复只能做以下两件事之一：\n"
+                            "Each response can only do ONE of the following two things:\n"
-                            "- 调用一个工具（输出一个 <tool_call> 块，不要写 Final Answer）\n"
+                            "- Call a tool (output a <tool_call> block, do not write Final Answer)\n"
-                            "- 输出最终内容（以 'Final Answer:' 开头，不要包含 <tool_call>）\n"
+                            "- Output final content (start with 'Final Answer:', do not include <tool_call>)\n"
-                            "请重新回复，只做其中一件事。"
+                            "Please reply again, doing only one of the two."
                         ),
                     })
                     continue
@@ -1805,7 +1804,7 @@ class ReportAgent:
             report_content=report_content if report_content else "（暂无报告）",
             tools_description=self._get_tools_description(),
         )
-        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
+        system_prompt = f"{get_language_instruction()}\n\n{system_prompt}"
 
         # 构建消息
         messages = [{"role": "system", "content": system_prompt}]

backend/scripts/run_parallel_simulation.py

@@ -1032,18 +1032,13 @@ def create_model(config: Dict[str, Any], use_boost: bool = False):
     print(f"{config_label} model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...")
     
     # camel-ai reads OPENAI_API_KEY from env automatically
-    # Pass api_key and base_url via model_config_dict
+    # AsyncOpenAI reads OPENAI_API_KEY and OPENAI_BASE_URL from env automatically
-    # camel-ai extracts these for the OpenAI client constructor
+    # Pass empty model_config_dict so nothing is spread to create() call
-    model_config = {}
-    if llm_api_key:
-        model_config["api_key"] = llm_api_key
-    if llm_base_url:
-        model_config["base_url"] = llm_base_url
     
     return ModelFactory.create(
         model_platform=ModelPlatformType.OPENAI,
         model_type=llm_model,
-        model_config_dict=model_config,
+        model_config_dict={},
     )
 
 

backend/scripts/run_reddit_simulation.py

@@ -461,18 +461,13 @@ class RedditSimulationRunner:
         
         print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...")
         
-        # Pass api_key and base_url via model_config_dict
+        # AsyncOpenAI reads OPENAI_API_KEY and OPENAI_BASE_URL from env automatically
-        # camel-ai extracts these for the OpenAI client constructor
+        # Pass empty model_config_dict so nothing is spread to create() call
-        model_config = {}
-        if llm_api_key:
-            model_config["api_key"] = llm_api_key
-        if llm_base_url:
-            model_config["base_url"] = llm_base_url
         
         return ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI,
             model_type=llm_model,
-            model_config_dict=model_config,
+            model_config_dict={},
         )
     
     def _get_active_agents_for_round(

backend/scripts/run_twitter_simulation.py

@@ -454,18 +454,13 @@ class TwitterSimulationRunner:
         
         print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...")
         
-        # Pass api_key and base_url via model_config_dict
+        # AsyncOpenAI reads OPENAI_API_KEY and OPENAI_BASE_URL from env automatically
-        # camel-ai extracts these for the OpenAI client constructor
+        # Pass empty model_config_dict so nothing is spread to create() call
-        model_config = {}
-        if llm_api_key:
-            model_config["api_key"] = llm_api_key
-        if llm_base_url:
-            model_config["base_url"] = llm_base_url
         
         return ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI,
             model_type=llm_model,
-            model_config_dict=model_config,
+            model_config_dict={},
         )
     
     def _get_active_agents_for_round(

locales/languages.json

@@ -1,11 +1,11 @@
 {
   "zh": {
     "label": "中文",
-    "llmInstruction": "请使用中文回答。"
+    "llmInstruction": "重要：你必须使用中文撰写所有内容。请使用中文回答。"
   },
   "en": {
     "label": "English",
-    "llmInstruction": "Please respond in English."
+    "llmInstruction": "IMPORTANT: You MUST write ALL content exclusively in English. Do NOT use any other language in your response."
   },
   "es": {
     "label": "Español",
@@ -29,6 +29,6 @@
   },
   "th": {
     "label": "ไทย",
-    "llmInstruction": "กรุณาตอบเป็นภาษาไทย"
+    "llmInstruction": "IMPORTANT: You MUST write ALL content exclusively in Thai language (ภาษาไทย). Do NOT use any other language in your response. กรุณาตอบเป็นภาษาไทยเท่านั้น ห้ามใช้ภาษาอื่น"
   }
 }