
Byted Deepsearch
- 2 installs
- 408 repo stars
- Updated August 3, 2026
- volcengine/agentkit-samples
Runs a multi-round iterative web-search research agent that adjusts search direction and synthesizes a detailed analysis report on complex topics.
About
A workflow-only deep-research agent that performs multi-round iterative web search and synthesizes comprehensive reports on complex topics. A developer uses it to research a subject in depth using up-to-date web information.
- Multi-round iterative search with intelligent search-direction adjustment
- Workflow document with no scripts; depends on the byted-web-search skill
Byted Deepsearch by the numbers
- 2 all-time installs (skills.sh)
- Ranked #13,957 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 2 |
|---|---|
| repo stars | ★ 408 |
| Last updated | August 3, 2026 |
| Repository | volcengine/agentkit-samples ↗ |
What it does
Runs a multi-round iterative web-search research agent that adjusts search direction and synthesizes a detailed analysis report on complex topics.
Files
BytedDeepsearch
概述
深度研究代理系统社区版是一个基于工作流的深度研究系统,能够对复杂主题进行多轮迭代的网络搜索和综合分析。系统通过结合LLM智能分析和实时网络搜索,生成详细的研究报告。
依赖技能
- byted-web-search
工作流调用逻辑
系统架构
本系统是一个工作流描述文档,没有执行脚本。依赖以下外部技能:
- byted-web-search skill: 执行网页搜索`
- LLM技能: 用于智能分析和推理
工作流程
1. 输入接收阶段
- 用户输入: 研究主题(sys.query)和研究深度(depth)
- 默认深度: 3(可配置)
2. 初始化阶段
- 创建迭代数组,深度为指定的depth值
- 数组格式:
[0, 1, ..., depth-1]
3. 迭代搜索阶段(多轮执行)
每轮迭代执行以下步骤:
a) LLM智能分析
- 使用LLM分析当前研究状态
- 输入:用户查询、已收集的findings、已搜索的topics
- 输出:JSON格式,包含:
nextSearchTopic: 下一个搜索主题(字符串或None)shouldContinue: 是否继续搜索(布尔值)
b) JSON解析
- 提取
nextSearchTopic和shouldContinue字段 - 更新对话变量
c) 条件判断
- 如果
shouldContinue为True: - 执行byted-web-search:
python scripts/web_search.py "<nextSearchTopic>" - 将搜索结果追加到findings数组
- 更新搜索进度显示
- 继续下一轮迭代
- 如果
shouldContinue为False: - 结束当前迭代
- 输出中间结果
d) 变量管理
- 更新
nextSearchTopic和shouldContinue变量 - 将
nextSearchTopic追加到topics数组(记录已搜索主题) - 避免重复搜索相同主题
4. 综合分析阶段
- 所有迭代完成后,使用LLM综合分析所有收集到的findings
- 生成详细的综合分析报告
- 输出格式:Markdown格式的详细报告
5. 报告生成阶段
- 输出最终的研究分析结果
- 包含重要洞察、结论和剩余不确定性
- 适当引用来源
变量说明
系统维护以下对话变量:
| 变量名 | 类型 | 描述 |
|---|---|---|
topics | array[string] | 已搜索的主题列表 |
nextSearchTopic | string | 下一个要搜索的主题 |
findings | array[string] | 收集到的搜索结果列表 |
shouldContinue | string | 是否继续搜索的标志 |
网页搜索集成
当需要进行网络搜索时:
1. 使用nextSearchTopic作为查询参数 2. 运行byted-web-search技能:python scripts/web_search.py "<query>" 3. 根据返回的摘要列表组织答案,不新增或臆造内容 4. 将搜索结果追加到findings数组
注意: 不要使用任何搜索参数配置(如search_depth、topic、max_results、country、time_range、days等),仅保留核心输入query。
进度跟踪
系统实时显示搜索进度:
- 格式:
{index + 1}/{depth}th search executed. - 例如:
1/3th search executed.
使用场景
适用场景
1. 复杂主题研究: 需要对特定主题进行深入、全面的研究 2. 最新信息分析: 需要基于最新网络信息生成详细分析报告 3. 多角度探索: 需要从不同角度和维度探索一个主题 4. 系统化调查: 需要系统化的调查和证据收集
典型用例
- 市场趋势分析
- 技术发展研究
- 竞争对手分析
- 学术文献综述
- 产品调研
技术特点
智能特性
1. 自适应搜索: 每轮搜索后由LLM分析结果,智能决定下一步搜索方向 2. 避免重复: 系统记录已搜索主题,避免重复搜索相同内容 3. 深度推理: 使用专门的推理模型进行综合分析
系统特性
1. 多轮迭代: 支持指定深度的多轮搜索 2. 并行能力: 支持最多10个并行搜索 3. 状态管理: 完整的变量管理和状态跟踪 4. 进度可视: 实时显示搜索进度和状态
集成特性
1. LLM集成: 结合GPT-4o进行智能分析,deepseek-reasoner进行深度推理 2. 网络搜索: 集成byted-web-search技能获取实时网络信息 3. JSON处理: 使用JSON解析工具处理结构化数据
工作流示例
输入示例
用户查询: "人工智能在医疗领域的最新发展"
研究深度: 3执行流程
1. 第1轮:
- LLM分析: 决定搜索"AI医疗诊断最新进展"
- Web搜索: 执行搜索并收集结果
- 状态更新: 记录主题,决定继续搜索
2. 第2轮:
- LLM分析: 基于第1轮结果,决定搜索"医疗影像AI技术突破"
- Web搜索: 执行搜索并收集结果
- 状态更新: 记录主题,决定继续搜索
3. 第3轮:
- LLM分析: 基于前两轮结果,决定搜索"AI药物研发应用"
- Web搜索: 执行搜索并收集结果
- 状态更新: 记录主题,决定结束搜索
4. 综合分析:
- LLM综合分析所有收集到的findings
- 生成关于"人工智能在医疗领域的最新发展"的详细报告
输出示例
# 人工智能在医疗领域的最新发展研究报告
## 执行摘要
[基于三轮搜索的综合分析...]
## 主要发现
1. AI在医疗诊断方面的最新进展
- [具体发现1]
- [具体发现2]
2. 医疗影像AI技术突破
- [具体发现3]
- [具体发现4]
3. AI在药物研发中的应用
- [具体发现5]
- [具体发现6]
## 结论与建议
[综合分析结论...]
## 未来研究方向
[基于研究发现提出的未来研究方向...]注意事项
工作流限制
1. 无执行脚本: 本skill是一个工作流描述文档,不包含可执行脚本 2. 外部依赖: 依赖byted-web-search技能执行实际搜索 3. 参数简化: 搜索时仅使用query参数,忽略其他搜索配置
使用建议
1. 深度设置: 根据研究复杂度设置合适的depth值 2. 查询优化: 提供清晰具体的研究主题 3. 结果验证: 对生成的报告进行必要的事实核查
最佳实践
1. 渐进式研究: 从宽泛主题开始,逐步深入具体方向 2. 多源验证: 结合多个来源的信息进行交叉验证 3. 及时更新: 对于快速发展的主题,建议定期重新研究
故障排除
常见问题
1. 搜索无结果: 检查query是否过于具体或专业,尝试更通用的搜索词 2. 迭代过早结束: 调整LLM的temperature参数或提供更多上下文 3. 结果重复: 系统已内置避免重复机制,如仍出现可手动干预
性能优化
1. 并行搜索: 充分利用系统的并行能力(最多10个并行) 2. 缓存利用: 对于相同主题的多次研究,可考虑结果缓存 3. 增量更新: 对于持续研究,可采用增量更新策略
---
重要提示: 本skill描述了一个深度研究工作流,实际执行需要依赖外部技能和配置。请确保已正确配置byted-web-search技能和相关LLM服务。
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