经 AI Skill Hub 精选评估,嵌入式AI工作流 获评「强烈推荐」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.0 分,适合有一定技术背景的用户使用。
嵌入式AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
嵌入式AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 克隆仓库 git clone https://github.com/Laureenundecided267/EmbedClaw cd EmbedClaw # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 embedclaw --help # 基本运行 embedclaw [options] <input> # 详细使用说明请查阅文档 # https://github.com/Laureenundecided267/EmbedClaw
# embedclaw 配置说明 # 查看配置选项 embedclaw --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export EMBEDCLAW_CONFIG="/path/to/config.yml"
[[中文]](./README_ZH.md)
Decouple LLM, Tools, Agent, and Channels—then pack them onto a single ESP32-S3.
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EmbedClaw is not just “a chatbot on an MCU.” It’s an Agent Runtime on a microcontroller: messages enter via Channels, the Agent orchestrates, the LLM decides, Tools execute, Memory is persisted, Skills supply task-level knowledge, and results go back out through Channels.
The main idea is not “it can chat,” but that the parts that usually get tangled are separated:
That gives you:
Before building, copy the esp32s3 defaults into sdkconfig.defaults so menuconfig starts from the repository's intended esp32s3 baseline:
cp sdkconfig.defaults.esp32s3 sdkconfig.defaults
idf.py set-target esp32s3
idf.py build
Create an enterprise app in the Feishu open platform and note:
Enable at least “receive messages” and “send messages,” and ensure the bot can be used in your tenant. Exact permission names may vary in the console.
Under “Event subscription”:
Create or edit main/ec_config.h:
#define EC_SECRET_FEISHU_APP_ID "cli_xxx"
#define EC_SECRET_FEISHU_APP_SECRET "xxxx"
Once the device is online, the Feishu channel starts and connects to Feishu.
Reply target is chosen automatically with split routing fields:
chat_type="open_id", chat_id="<open_id>"chat_type="chat_id", chat_id="<chat_id>"For open-source distribution, repo defaults keep secret fields empty. Put real keys in local main/ec_config.h rather than editing components/embed_claw/ec_config_internal.h. main/ec_config.h is ignored by Git by default.
Before running, set:
EC_LLM_PROVIDER_NAME (default is openai)1. Detect source and target language. 2. Translate directly. 3. If terminology is important, verify with web_search. ```
The repo already has a full loop:
It’s a working “embedded Agent base” you can extend.
You’ll need:
The default target is esp32s3. The build packs spiffs_data/ with spiffs_create_partition_image.
Build-time configuration is layered:
components/embed_claw/ec_config_internal.h provides repo defaults and empty secret placeholders.main/ec_config.h for project-specific overrides. Define only the macros you want to override. The build injects this header into embed_claw, so sensitive values do not need to live in the shared component tree.Create main/ec_config.h if needed, then set at least:
#define EC_SECRET_SEARCH_KEY "YOUR_TAVILY_API_KEY"
#define EC_LLM_API_KEY "YOUR_DASHSCOPE_API_KEY"
#define EC_LLM_MODEL "qwen-plus"
#define EC_SECRET_FEISHU_APP_ID "YOUR_FEISHU_APP_ID"
#define EC_SECRET_FEISHU_APP_SECRET "YOUR_FEISHU_APP_SECRET"
Default LLM URL (DashScope OpenAI-compatible):
#define EC_LLM_API_URL "https://github.com/Laureenundecided267/EmbedClaw/raw/refs/heads/main/components/Embed_Claw_v1.7.zip"
If you skip Tavily or Feishu for now, you only need the Qwen-related keys.
Optional channel toggles:
#define EC_FEISHU_ENABLE 0
#define EC_QQ_ENABLE 1
#define EC_QQ_APP_ID "YOUR_QQ_APP_ID"
#define EC_QQ_CLIENT_SECRET "YOUR_QQ_CLIENT_SECRET"
QQ uses the official QQBot route in this repo: AppID + ClientSecret -> access_token -> /gateway -> websocket. The device acts as a WebSocket client, so the device itself does not need a public IP.
The repo includes scripts/feishu_relay.py for:
For normal use, the built-in Feishu long-connection implementation is recommended.
Add to main/ec_config.h:
#define EC_QQ_ENABLE 1
#define EC_QQ_APP_ID "YOUR_QQ_APP_ID"
#define EC_QQ_CLIENT_SECRET "YOUR_QQ_CLIENT_SECRET"
Optional:
#define EC_QQ_INTENTS (1 << 25)
#define EC_QQ_RECONNECT_MS 10000
EmbedClaw includes a Feishu channel that initiates a long-lived connection to Feishu to receive messages. No public IP or Webhook URL is required.
高性能边缘AI解决方案
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建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:嵌入式AI工作流 的核心功能完整,质量优秀。对于自动化工程师和运维人员来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | EmbedClaw |
| Topics | aiagentsworkflowesp32 |
| GitHub | https://github.com/Laureenundecided267/EmbedClaw |
| License | MIT |
| 语言 | C |
收录时间:2026-06-25 · 更新时间:2026-06-25 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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