AI Skill Hub 推荐使用:人机协作AI 是一款优质的MCP工具。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
人机协作AI 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
人机协作AI 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/awaithumans/awaithumans-human-in-the-loop-ai-agents
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"----ai": {
"command": "npx",
"args": ["-y", "awaithumans-human-in-the-loop-ai-agents"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 人机协作AI 执行以下任务... Claude: [自动调用 人机协作AI MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"____ai": {
"command": "npx",
"args": ["-y", "awaithumans-human-in-the-loop-ai-agents"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/awaithumans/awaithumans-human-in-the-loop-ai-agents/main/docs/logo/dark.svg"> <img alt="awaithumans" src="https://raw.githubusercontent.com/awaithumans/awaithumans-human-in-the-loop-ai-agents/main/docs/logo/light.svg" width="520"> </picture>
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Your agents already await promises. Now they can await humans.
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Docs · Quickstart · Examples · Discord
</div>
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HITL infrastructure for AI agents — open source. A single primitive (await_human() / awaitHuman()) your agent calls when it needs a human. A real person reviews via Slack / email / a built-in dashboard, submits a typed response, and your agent resumes — like awaiting any other promise.
from awaithumans import await_human
from pydantic import BaseModel
class Decision(BaseModel):
approved: bool
note: str | None = None
decision = await await_human(
task="Approve refund request",
payload_schema=RefundRequest,
payload=RefundRequest(order_id="A-4721", amount_usd=180),
response_schema=Decision,
timeout_seconds=900,
)
if decision.approved:
process_refund(...)
The agent waits on decision like it waits on any other Promise or Future. A human gets notified (Slack, email, dashboard), reviews the request, and submits a typed response. The agent resumes with the typed answer.


---
Real production patterns this primitive collapses into a single function call:
verified: bool with notes. Pair with verifier=verify_with_claude(...) to pre-check the reviewer's reasoning.await_human() with the content + AI's reasoning + a Switch for keep/remove. Reviewer's decision flows back into the moderation pipeline.await_human() with the screenshot, a human solves it, agent resumes the scrape.Anything where an LLM's confidence is too low, the liability too high, or the source of truth lives outside the model's reach — it's HITL-shaped, and this primitive fits.
---
60 seconds. Two terminals. First-time setup needs a browser click; everything else is paste-and-run.
Terminal 1 — server + dashboard:
pip install "awaithumans[server]" && awaithumans dev
Click the setup URL it prints, create your operator account. The dashboard is now at http://localhost:3001.
Terminal 2 — paste this whole block:
pip install awaithumans pydantic && cat > /tmp/refund.py <<'PY'
from awaithumans import await_human_sync
from pydantic import BaseModel
class RefundRequest(BaseModel):
order_id: str
amount_usd: float
class Decision(BaseModel):
approved: bool
d = await_human_sync(
task="Approve refund of $180?",
payload_schema=RefundRequest,
payload=RefundRequest(order_id="A-4721", amount_usd=180),
response_schema=Decision,
timeout_seconds=300,
)
print("approved" if d.approved else "rejected")
PY
python /tmp/refund.py
The script blocks. Open the dashboard, click the pending task, hit Approve. The script unblocks with the typed Decision.
That's the full loop. From here, swap the schema for your own, add notify=["slack:#ops", "email:ops@yourco.com"] to route the task elsewhere, or wrap the call in a Temporal / LangGraph workflow.
More examples — refund, KYC, content moderation, Slack-native, Temporal, LangGraph — in examples/.
---
| Package | Registry | License |
|---|---|---|
awaithumans (Python SDK + server + CLI + dashboard) | PyPI | Apache 2.0 |
awaithumans (TypeScript SDK) | npm | Apache 2.0 |
ghcr.io/awaithumans/awaithumans-human-in-the-loop-ai-agents (container) | GHCR | Apache 2.0 |
License: Apache License 2.0. Permissive, OSI-approved, with an explicit patent grant. Use it in proprietary stacks, fork it, ship it inside paid products — no fee, no contact required. The only thing the license asks is that you preserve the notice and don't use the project's trademarks without permission.
---
If something isn't working, run the pre-flight check first:
awaithumans doctor
It scans your environment for the most common misconfigurations — missing or malformed PAYLOAD_KEY, Slack token issues, unreachable database, Docker bind-mount gotchas — and prints a clear report with actionable fixes. Catches ~80% of first-run issues in under 2 seconds.
---
创新的人机协作AI解决方案
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
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。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
总体来看,人机协作AI 是一款质量良好的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | awaithumans-human-in-the-loop-ai-agents |
| 原始描述 | 开源MCP工具:Pause your AI agent. Ask a human. Resume with their answer. Open source human-in。⭐13 · Python |
| Topics | ai-agentshuman-in-the-loopagent-memory |
| GitHub | https://github.com/awaithumans/awaithumans-human-in-the-loop-ai-agents |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-05-31 · 更新时间:2026-06-02 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端