星标 11400、fork 1500:吴恩达开源 OpenWorker
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TL;DR - Andrew Ng open-sourced OpenWorker, a local-first, model-agnostic "AI coworker" harness that delivers finished artifacts (docs, reports, HTML briefs) rather than chat replies; it has drawn ~11,400 GitHub stars and 1,500+ forks. It matters as an open, vendor-neutral reference for the agent harness layer — tool connectivity, approvals, orchestration, permissions — which the article argues is now the real competitive asset since models are commoditizing.
- Architecture treats an Agent as an orchestrable surface = system prompt + tool set + workspace. The Python backend (
coworker/) defines Code / Chat / Cowork surfaces plus a resident MyHelper; Cowork ships a deliberately minimal toolset of files / search / shell / todo. - Approval is an engine-level mechanism, not UI polish: each tool carries
risk_levelandrequires_approvalmetadata (shell writes = high + mandatory approval, read-only = low), and the TurnEngine suspends on high-risk calls pending human confirmation. Emails, calendar edits, and shell commands all gate on Approve. - Skills reuse Anthropic's SKILL.md spec (YAML frontmatter + markdown + optional scripts) with progressive disclosure — only names/descriptions are injected at session start, with bodies pulled on demand via
load_skill, so existing Claude skill packs are in principle portable. - Model-agnostic with bring-your-own keys: OpenAI, Anthropic, Gemini alongside first-class support for Kimi, GLM, DeepSeek, Qwen, MiniMax, plus Ollama for fully local runs; 25+ connectors (GitHub, Slack, Jira, Notion, Linear, HubSpot, Gmail, Calendar) and any MCP-reachable tool, each individually permissioned. Shell execution sits behind an
Executorabstraction (currentlyLocalExecutorwith a persistent shell) with source comments reservingContainerExecutor/VMExecutorfor future sandboxing. The README points builders toaisuiteas the underlying foundation.
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星标 11400、fork 1500:吴恩达开源 OpenWorker
TL;DR - Andrew Ng open-sourced OpenWorker, a local-first, model-agnostic "AI coworker" harness that delivers finished artifacts (docs, reports, HTML briefs) rather than chat replies; it has drawn ~11,400 GitHub stars and 1,500+ forks. It matters as an open, vendor-neutral reference for the agent harness layer — tool connectivity, approvals, orchestration, permissions — which the article argues is now the real competitive asset since models are commoditizing.
- Architecture treats an Agent as an orchestrable surface = system prompt + tool set + workspace. The Python backend (
coworker/) defines Code / Chat / Cowork surfaces plus a resident MyHelper; Cowork ships a deliberately minimal toolset of files / search / shell / todo. - Approval is an engine-level mechanism, not UI polish: each tool carries
risk_levelandrequires_approvalmetadata (shell writes = high + mandatory approval, read-only = low), and the TurnEngine suspends on high-risk calls pending human confirmation. Emails, calendar edits, and shell commands all gate on Approve. - Skills reuse Anthropic's SKILL.md spec (YAML frontmatter + markdown + optional scripts) with progressive disclosure — only names/descriptions are injected at session start, with bodies pulled on demand via
load_skill, so existing Claude skill packs are in principle portable. - Model-agnostic with bring-your-own keys: OpenAI, Anthropic, Gemini alongside first-class support for Kimi, GLM, DeepSeek, Qwen, MiniMax, plus Ollama for fully local runs; 25+ connectors (GitHub, Slack, Jira, Notion, Linear, HubSpot, Gmail, Calendar) and any MCP-reachable tool, each individually permissioned. Shell execution sits behind an
Executorabstraction (currentlyLocalExecutorwith a persistent shell) with source comments reservingContainerExecutor/VMExecutorfor future sandboxing. The README points builders toaisuiteas the underlying foundation.