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Invoke...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-02-25T08:04:22.419Z | auto\n\nInitial release of agent-evolver.\n\n- Enables agents to learn from experience, detect problems, and optimize strategies autonomously.\n- Provides command-line tools and Python API for error analysis, experience search, evolution statistics, and history tracking.\n- Supports persistent experience storage with SQLite, semantic search with vector embeddings, and dynamic strategy optimization.\n- Auto-triggers on task failures, user requests for performance improvement, or need for error analysis and continuous learning.\n- Includes configuration via environment variables and YAML, and supports multiple task types like code generation, data analysis, and more.\n\nArchive index:\n\nArchive v1.0.0: 11 files, 22031 bytes\n\nFiles: config/evolver_config.yaml (701b), config/skill_triggers.yaml (1939b), deploy.sh (4814b), evolver.sh (1518b), requirements.txt (220b), scripts/evolution_cli.py (9862b), scripts/evolver_core.py (22290b), scripts/experience_vectorizer.py (9950b), scripts/skill_registry.py (9066b), SKILL.md (6518b), _meta.json (132b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: agent-evolver\ndescription: AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke when users need to improve agent performance, analyze execution errors, or implement continuous learning capabilities.\ntriggers:\n  keywords:\n    - \"进化\"\n    - \"优化策略\"\n    - \"学习经验\"\n    - \"改进\"\n    - \"错误分析\"\n    - \"自学习\"\n    - \"经验\"\n    - \"策略优化\"\n    - \"持续学习\"\n    - \"性能改进\"\n  conditions:\n    - \"任务执行失败超过3次\"\n    - \"用户要求改进性能\"\n    - \"需要分析历史错误\"\n    - \"需要从经验中学习\"\n---\n\n# Agent Evolver Skill\n\nAI Agent 自进化引擎，让 Agent 具备自学习和持续改进能力。\n\n## 何时使用此技能\n\n### 自动触发条件\n\n1. **错误分析场景**\n   - 当任务执行失败时\n   - 当需要分析错误原因时\n   - 当需要查找相似历史错误时\n\n2. **性能优化场景**\n   - 当用户要求改进 Agent 性能时\n   - 当需要优化执行策略时\n   - 当需要提高成功率时\n\n3. **学习进化场景**\n   - 当需要从历史经验学习时\n   - 当需要积累知识时\n   - 当需要持续改进时\n\n## 使用方法\n\n### 1. 分析执行结果并提取经验\n\n```bash\npython3 scripts/evolution_cli.py analyze --result \"<错误信息>\"\npython3 scripts/evolution_cli.py analyze --result-file result.json\n```\n\n### 2. 搜索相似历史经验\n\n```bash\npython3 scripts/evolution_cli.py search --query \"负数平方计算错误\"\npython3 scripts/evolution_cli.py search --query \"ValueError\" --limit 10\n```\n\n### 3. 查看进化统计\n\n```bash\npython3 scripts/evolution_cli.py stats\npython3 scripts/evolution_cli.py stats --agent-id my_agent --json\n```\n\n### 4. 查看进化历史\n\n```bash\npython3 scripts/evolution_cli.py history --limit 20\npython3 scripts/evolution_cli.py history --task-type code_generation\n```\n\n### 5. 执行进化周期\n\n```bash\npython3 scripts/evolution_cli.py evolve \"计算 -5 的平方\" --task-type calculation\n```\n\n## 集成示例\n\n### Python API\n\n```python\nfrom evolver_core import EvolutionManager\n\n# 初始化进化管理器\nevolver = EvolutionManager(agent_id=\"main_agent\")\n\n# 执行任务后自动进化\ndef execute_with_evolution(task):\n    result = execute_task(task)\n    \n    # 自动分析并学习\n    evolver.run_evolution(\n        task_input=task,\n        task_type=\"general\"\n    )\n    \n    return result\n\n# 搜索历史经验\ndef find_similar_solutions(error_description):\n    similar = evolver.search_similar(error_description)\n    return similar\n\n# 获取进化统计\ndef get_evolution_stats():\n    return evolver.get_stats()\n```\n\n### 自动触发示例\n\n```python\n# 主 Agent 执行任务\nresult = execute_task(\"计算 -5 的平方\")\n\n# 失败后自动触发进化\nif result.status == \"failed\":\n    # 自动调用 agent-evolver 技能\n    evolver = get_skill(\"agent-evolver\")\n    evolver.analyze(result.error)\n    \n    # 搜索相似解决方案\n    similar = evolver.search_similar(result.error.message)\n    \n    # 应用建议的解决方案\n    if similar:\n        apply_solution(similar[0].solution)\n```\n\n## 功能特性\n\n### 1. 智能经验提取\n- 使用 LLM 自动分析错误原因\n- 生成针对性的解决方案\n- 提取关键词标签便于搜索\n\n### 2. 经验库持久化\n- SQLite 存储所有经验\n- 支持按类型、错误类型查询\n- 自动统计成功率、改进率\n\n### 3. 经验向量化\n- 使用 Embedding 模型向量化经验\n- 支持语义搜索相似经验\n- ChromaDB 向量存储\n\n### 4. 动态策略优化\n- 根据历史经验优化策略\n- 支持策略版本管理\n- 自动回滚机制\n\n### 5. 多任务类型支持\n- 代码生成 (code_generation)\n- 数据分析 (data_analysis)\n- 文档处理 (document_processing)\n- 数值计算 (calculation)\n- 通用任务 (general)\n\n## 输出格式\n\n所有命令支持 `--json` 参数输出 JSON 格式：\n\n```bash\npython3 scripts/evolution_cli.py stats --json\n```\n\n## 配置\n\n### 环境变量\n\n- `OPENAI_API_KEY` - OpenAI API 密钥（用于 LLM 分析和向量化）\n- `OPENAI_API_BASE` - API 基础 URL（可选，用于自定义端点）\n- `EVOLVER_DB_PATH` - 数据库路径（默认：~/.evolver/evolution.db）\n\n### 配置文件\n\n配置文件位于 `config/evolver_config.yaml`：\n\n```yaml\nllm:\n  model: gpt-3.5-turbo\n  temperature: 0.7\n\nvector:\n  model: text-embedding-3-small\n  enabled: true\n\nstorage:\n  db_path: ~/.evolver/evolution.db\n  vector_path: ~/.evolver/chroma\n```\n\n## 数据模型\n\n### 经验胶囊 (ExperienceCapsule)\n\n```json\n{\n  \"id\": \"exp_20260224_001\",\n  \"task_type\": \"code_generation\",\n  \"status\": \"failed\",\n  \"error_type\": \"ValueError\",\n  \"error_message\": \"不支持负数输入\",\n  \"solution\": \"使用绝对值处理负数\",\n  \"keywords\": [\"负数\", \"平方计算\", \"ValueError\"]\n}\n```\n\n## 示例场景\n\n### 场景 1：任务失败分析\n\n```\n用户: \"这个任务总是失败，帮我分析一下\"\nAgent: 我来使用 agent-evolver 技能分析错误...\n       [调用] python3 scripts/evolution_cli.py analyze --result \"ValueError: 不支持负数输入\"\n       [结果] 发现类似历史错误 3 次\n              建议解决方案：使用绝对值处理负数\n```\n\n### 场景 2：性能优化\n\n```\n用户: \"帮我优化一下 Agent 的性能\"\nAgent: 我来分析 Agent 的进化统计...\n       [调用] python3 scripts/evolution_cli.py stats\n       [结果] 成功率：85%，改进率：15%\n              常见错误：ValueError (5次), TypeError (3次)\n              建议：优先处理 ValueError 类型错误\n```\n\n### 场景 3：经验搜索\n\n```\n用户: \"之前遇到过类似的负数计算问题吗？\"\nAgent: 我来搜索历史经验...\n       [调用] python3 scripts/evolution_cli.py search --query \"负数计算\"\n       [结果] 找到 2 条相似经验：\n              1. 使用绝对值处理负数 (相似度: 95%)\n              2. 添加负数检查逻辑 (相似度: 87%)\n```\n\n## 技能发现机制\n\n当此技能安装后，主 Agent 会自动：\n1. 识别关键词触发（进化、优化、学习、改进等）\n2. 在任务失败时自动调用分析\n3. 定期检查进化统计\n4. 主动推荐优化建议\n\n## 依赖\n\n- Python 3.8+\n- OpenAI API（可选，用于 LLM 分析）\n- ChromaDB（可选，用于向量搜索）\n\n## 注意事项\n\n1. 首次使用需要设置 `OPENAI_API_KEY` 环境变量\n2. 经验库存储在 `~/.evolver/` 目录\n3. 向量搜索需要安装 `chromadb`\n4. 无 API 密钥时使用后备分析方案\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn79a7kh8zssw39drfxcg0ndhd80tf82\",\n  \"slug\": \"agent-evolver\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1772006662419\n}\n\nFile v1.0.0:config/evolver_config.yaml\n\n# Agent Evolver Configuration\n# 智能体自进化引擎配置\n\n# LLM 配置\nllm:\n  model: gpt-3.5-turbo\n  temperature: 0.7\n  max_tokens: 1000\n  api_base: ${OPENAI_API_BASE:https://api.openai.com/v1}\n\n# 向量化配置\nvector:\n  model: text-embedding-3-small\n  enabled: true\n  persist_directory: ~/.evolver/chroma\n\n# 存储配置\nstorage:\n  db_path: ~/.evolver/evolution.db\n  vector_index_path: ~/.evolver/vector_index.json\n\n# 进化配置\nevolution:\n  auto_optimize: true\n  max_history: 1000\n  similarity_threshold: 0.7\n  \n# 触发配置\ntriggers:\n  auto_analyze_on_failure: true\n  failure_threshold: 3\n  success_sample_rate: 0.1\n\n# 日志配置\nlogging:\n  level: INFO\n  file: ~/.evolver/evolver.log\n\nFile v1.0.0:config/skill_triggers.yaml\n\n# Skill Triggers Configuration\n# 技能触发规则配置\n\nskills:\n  agent-evolver:\n    # 关键词触发\n    keywords:\n      - \"进化\"\n      - \"优化\"\n      - \"学习\"\n      - \"改进\"\n      - \"错误分析\"\n      - \"自学习\"\n      - \"经验\"\n      - \"策略优化\"\n      - \"持续学习\"\n      - \"性能改进\"\n      - \"失败分析\"\n      - \"历史经验\"\n    \n    # 条件触发\n    conditions:\n      - type: \"error_count\"\n        threshold: 3\n        action: \"analyze\"\n        description: \"同一任务失败超过3次时触发分析\"\n      \n      - type: \"user_request\"\n        patterns:\n          - \"帮我改进\"\n          - \"分析一下错误\"\n          - \"优化一下策略\"\n          - \"为什么失败\"\n          - \"之前遇到过\"\n          - \"历史经验\"\n        action: \"evolve\"\n        description: \"用户请求改进时触发进化\"\n      \n      - type: \"performance_drop\"\n        threshold: 0.1\n        action: \"optimize\"\n        description: \"性能下降超过10%时触发优化\"\n      \n      - type: \"new_task_type\"\n        action: \"learn\"\n        description: \"遇到新任务类型时触发学习\"\n    \n    # 自动触发\n    auto_trigger:\n      - event: \"task_failed\"\n        action: \"extract_experience\"\n        probability: 1.0\n        description: \"任务失败时自动提取经验\"\n      \n      - event: \"task_success\"\n        action: \"extract_experience\"\n        probability: 0.1\n        description: \"任务成功时10%概率提取经验\"\n      \n      - event: \"periodic\"\n        interval: 3600\n        action: \"stats_report\"\n        description: \"每小时生成统计报告\"\n    \n    # 命令映射\n    commands:\n      analyze: \"python3 scripts/evolution_cli.py analyze\"\n      search: \"python3 scripts/evolution_cli.py search\"\n      stats: \"python3 scripts/evolution_cli.py stats\"\n      history: \"python3 scripts/evolution_cli.py history\"\n      evolve: \"python3 scripts/evolution_cli.py evolve\"\n\nFile v1.0.0:requirements.txt\n\n# Agent Evolver Skill Dependencies\n# Core\nsqlite3>=3.35.0\npyyaml>=6.0\n\n# Vector Search\nchromadb>=0.4.0\nopenai>=1.0.0\n\n# LLM Integration\nrequests>=2.28.0\n\n# Optional: Local embedding models\n# sentence-transformers>=2.2.0","readmeExcerpt":"Skill: Agent Evolver Owner: lilei0311 Summary: AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-02-25T08:04:22.419Z | auto Initial release of agent-evolver. - Enables agents to learn from experience, detect problems, and optimize strategies autonomously. - Pr","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"python3 scripts/evolution_cli.py analyze --result \"<错误信息>\"\npython3 scripts/evolution_cli.py analyze --result-file result.json"},{"language":"bash","snippet":"python3 scripts/evolution_cli.py search --query \"负数平方计算错误\"\npython3 scripts/evolution_cli.py search --query \"ValueError\" --limit 10"},{"language":"bash","snippet":"python3 scripts/evolution_cli.py stats\npython3 scripts/evolution_cli.py stats --agent-id my_agent --json"},{"language":"bash","snippet":"python3 scripts/evolution_cli.py history --limit 20\npython3 scripts/evolution_cli.py history --task-type code_generation"},{"language":"bash","snippet":"python3 scripts/evolution_cli.py evolve \"计算 -5 的平方\" --task-type calculation"},{"language":"python","snippet":"from evolver_core import EvolutionManager\n\n# 初始化进化管理器\nevolver = EvolutionManager(agent_id=\"main_agent\")\n\n# 执行任务后自动进化\ndef execute_with_evolution(task):\n    result = execute_task(task)\n    \n    # 自动分析并学习\n    evolver.run_evolution(\n        task_input=task,\n        task_type=\"general\"\n    )\n    \n    return result\n\n# 搜索历史经验\ndef find_similar_solutions(error_description):\n    similar = evolver.search_similar(error_description)\n    return similar\n\n# 获取进化统计\ndef get_evolution_stats():\n    return evolver.get_stats()"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: agent-evolver\ndescription: AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke when users need to improve agent performance, analyze execution errors, or implement continuous learning capabilities.\ntriggers:\n  keywords:\n    - \"进化\"\n    - \"优化策略\"\n    - \"学习经验\"\n    - \"改进\"\n    - \"错误分析\"\n    - \"自学习\"\n    - \"经验\"\n    - \"策略优化\"\n    - \"持续学习\"\n    - \"性能改进\"\n  conditions:\n    - \"任务执行失败超过3次\"\n    - \"用户要求改进性能\"\n    - \"需要分析历史错误\"\n    - \"需要从经验中学习\"\n---\n\n# Agent Evolver Skill\n\nAI Agent 自进化引擎，让 Agent 具备自学习和持续改进能力。\n\n## 何时使用此技能\n\n### 自动触发条件\n\n1. **错误分析场景**\n   - 当任务执行失败时\n   - 当需要分析错误原因时\n   - 当需要查找相似历史错误时\n\n2. **性能优化场景**\n   - 当用户要求改进 Agent 性能时\n   - 当需要优化执行策略时\n   - 当需要提高成功率时\n\n3. **学习进化场景**\n   - 当需要从历史经验学习时\n   - 当需要积累知识时\n   - 当需要持续改进时\n\n## 使用方法\n\n### 1. 分析执行结果并提取经验\n\n```bash\npython3 scripts/evolution_cli.py analyze --result \"<错误信息>\"\npython3 scripts/evolution_cli.py analyze --result-file result.json\n```\n\n### 2. 搜索相似历史经验\n\n```bash\npython3 scripts/evolution_cli.py search --query \"负数平方计算错误\"\npython3 scripts/evolution_cli.py search --query \"ValueError\" --limit 10\n```\n\n### 3. 查看进化统计\n\n```bash\npython3 scripts/evolution_cli.py stats\npython3 scripts/evolution_cli.py stats --agent-id my_agent --json\n```\n\n### 4. 查看进化历史\n\n```bash\npython3 scripts/evolution_cli.py history --limit 20\npython3 scripts/evolution_cli.py history --task-type code_generation\n```\n\n### 5. 执行进化周期\n\n```bash\npython3 scripts/evolution_cli.py evolve \"计算 -5 的平方\" --task-type calculation\n```\n\n## 集成示例\n\n### Python API\n\n```python\nfrom evolver_core import EvolutionManager\n\n# 初始化进化管理器\nevolver = EvolutionManager(agent_id=\"main_agent\")\n\n# 执行任务后自动进化\ndef execute_with_evolution(task):\n    result = execute_task(task)\n    \n    # 自动分析并学习\n    evolver.run_evolution(\n        task_input=task,\n        task_type=\"general\"\n    )\n    \n    return result\n\n# 搜索历史经验\ndef find_similar_solutions(error_description):\n    similar = evolver.search_similar(error_description)\n    return similar\n\n# 获取进化统计\ndef get_evolution_stats():\n    return evolver.get_stats()\n```\n\n### 自动触发示例\n\n```python\n# 主 Agent 执行任务\nresult = execute_task(\"计算 -5 的平方\")\n\n# 失败后自动触发进化\nif result.status == \"failed\":\n    # 自动调用 agent-evolver 技能\n    evolver = get_skill(\"agent-evolver\")\n    evolver.analyze(result.error)\n    \n    # 搜索相似解决方案\n    similar = evolver.search_similar(result.error.message)\n    \n    # 应用建议的解决方案\n    if similar:\n        apply_solution(similar[0].solution)\n```\n\n## 功能特性\n\n### 1. 智能经验提取\n- 使用 LLM 自动分析错误原因\n- 生成针对性的解决方案\n- 提取关键词标签便于搜索\n\n### 2. 经验库持久化\n- SQLite 存储所有经验\n- 支持按类型、错误类型查询\n- 自动统计成功率、改进率\n\n### 3. 经验向量化\n- 使用 Embedding 模型向量化经验\n- 支持语义搜索相似经验\n- ChromaDB 向量存储\n\n### 4. 动态策略优化\n- 根据历史经验优化策略\n- 支持策略版本管理\n- 自动回滚机制\n\n### 5. 多任务类型支持\n- 代码生成 (code_generation)\n- 数据分析 (data_analysis)\n- 文档处理 (document_processing)\n- 数值计算 (calculation)\n- 通用任务 (general)\n\n## 输出格式\n\n所有命令支持 `--json` 参数输出 JSON 格式：\n\n```bash\npython3 scripts/evolution_cli.py stats --json\n```\n\n## 配置\n\n### 环境变量\n\n- `OPENAI_API_KEY` - OpenAI API 密钥（用于"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn79a7kh8zssw39drfxcg0ndhd80tf82\",\n  \"slug\": \"agent-evolver\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1772006662419\n}"},{"path":"config/evolver_config.yaml","content":"# Agent Evolver Configuration\n# 智能体自进化引擎配置\n\n# LLM 配置\nllm:\n  model: gpt-3.5-turbo\n  temperature: 0.7\n  max_tokens: 1000\n  api_base: ${OPENAI_API_BASE:https://api.openai.com/v1}\n\n# 向量化配置\nvector:\n  model: text-embedding-3-small\n  enabled: true\n  persist_directory: ~/.evolver/chroma\n\n# 存储配置\nstorage:\n  db_path: ~/.evolver/evolution.db\n  vector_index_path: ~/.evolver/vector_index.json\n\n# 进化配置\nevolution:\n  auto_optimize: true\n  max_history: 1000\n  similarity_threshold: 0.7\n  \n# 触发配置\ntriggers:\n  auto_analyze_on_failure: true\n  failure_threshold: 3\n  success_sample_rate: 0.1\n\n# 日志配置\nlogging:\n  level: INFO\n  file: ~/.evolver/evolver.log"},{"path":"config/skill_triggers.yaml","content":"# Skill Triggers Configuration\n# 技能触发规则配置\n\nskills:\n  agent-evolver:\n    # 关键词触发\n    keywords:\n      - \"进化\"\n      - \"优化\"\n      - \"学习\"\n      - \"改进\"\n      - \"错误分析\"\n      - \"自学习\"\n      - \"经验\"\n      - \"策略优化\"\n      - \"持续学习\"\n      - \"性能改进\"\n      - \"失败分析\"\n      - \"历史经验\"\n    \n    # 条件触发\n    conditions:\n      - type: \"error_count\"\n        threshold: 3\n        action: \"analyze\"\n        description: \"同一任务失败超过3次时触发分析\"\n      \n      - type: \"user_request\"\n        patterns:\n          - \"帮我改进\"\n          - \"分析一下错误\"\n          - \"优化一下策略\"\n          - \"为什么失败\"\n          - \"之前遇到过\"\n          - \"历史经验\"\n        action: \"evolve\"\n        description: \"用户请求改进时触发进化\"\n      \n      - type: \"performance_drop\"\n        threshold: 0.1\n        action: \"optimize\"\n        description: \"性能下降超过10%时触发优化\"\n      \n      - type: \"new_task_type\"\n        action: \"learn\"\n        description: \"遇到新任务类型时触发学习\"\n    \n    # 自动触发\n    auto_trigger:\n      - event: \"task_failed\"\n        action: \"extract_experience\"\n        probability: 1.0\n        description: \"任务失败时自动提取经验\"\n      \n      - event: \"task_success\"\n        action: \"extract_experience\"\n        probability: 0.1\n        description: \"任务成功时10%概率提取经验\"\n      \n      - event: \"periodic\"\n        interval: 3600\n        action: \"stats_report\"\n        description: \"每小时生成统计报告\"\n    \n    # 命令映射\n    commands:\n      analyze: \"python3 scripts/evolution_cli.py analyze\"\n      search: \"python3 scripts/evolution_cli.py search\"\n      stats: \"python3 scripts/evolution_cli.py stats\"\n      history: \"python3 scripts/evolution_cli.py history\"\n      evolve: \"python3 scripts/evolution_cli.py evolve\""},{"path":"requirements.txt","content":"# Agent Evolver Skill Dependencies\n# Core\nsqlite3>=3.35.0\npyyaml>=6.0\n\n# Vector Search\nchromadb>=0.4.0\nopenai>=1.0.0\n\n# LLM Integration\nrequests>=2.28.0\n\n# Optional: Local embedding models\n# sentence-transformers>=2.2.0"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke... Skill: Agent Evolver Owner: lilei0311 Summary: AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-02-25T08:04:22.419Z | auto Initial release of agent-evolver. - Enables agents to learn from experience, detect problems, and optimize strategies autonomously. - Pr","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":761,"uniquenessScore":55,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-11T13:43:18.467Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-11T13:43:18.467Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-11T16:01:08.237Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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