huangdihd

huangdihd / call_me_as_agent

Public

像用agent一样调用我❤

39
4
89% credibility
Found May 18, 2026 at 38 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Vue
AI Summary

Call Me As Agent is a web service that acts as a checkpoint between your AI application and the AI company. When your AI tries to respond to someone, the service pauses the response and shows it to you in a dashboard. You can read it, edit it, add missing information, or approve it as-is. Once you're happy with the response, you click a button and it goes back to your AI app and reaches your users. It's designed for people who want human oversight on AI outputs - like content moderators, quality assurance teams, or anyone who wants to make sure AI responses are accurate before they go public.

How It Works

1
💡 You need to review AI responses

You want to check what your AI assistant says before it reaches your users, maybe for quality control or content safety.

2
🌐 You set up the review service

You launch a small web service that sits between your AI app and the AI company, like a checkpoint for every response.

3
🔗 Your AI app connects to the service

You point your AI application to the review service instead of directly to the AI company, and everything still works normally.

4
⏸️ The AI pauses and waits

When your AI tries to respond, the service stops it and shows you exactly what the AI wants to say in your dashboard.

5
You review the response
✏️
Edit the response

You can change the AI's text or fix any mistakes before it goes out.

🤖
Add tool instructions

If the AI wants to use tools, you can fill in the missing details.

✅
Approve as-is

If everything looks good, you just approve it and move on.

6
🚀 You send the response back

With one click, your approved response travels back to your AI app and your users see the result.

🎉 Your users get safe, reviewed content

Every AI response goes through human eyes first, giving you complete control over what your AI says.

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AI-Generated Review

What is call_me_as_agent?

This is a human-in-the-loop proxy server for LLM agents. When an AI agent (Claude, OpenAI, etc.) wants to make a tool call or send a response, the request hits this server first, which pauses execution and lets a human review, edit, or approve the output before it goes back to the agent. Built on Nuxt 3 with Vue, it exposes standard OpenAI and Claude API endpoints so your existing agent code works without changes. The admin dashboard shows pending requests, lets you craft manual responses, and configure tool parameters through a UI.

Why is it gaining traction?

The hook is simple: AI agents are powerful but unreliable, and sometimes you need a human in the loop without rebuilding your entire workflow. Instead of routing around this proxy, you point your agent at it, and now every tool call or response can be vetted before it executes. The streaming simulation feature lets you control how fast responses appear, which is useful for demos or for giving reviewers time to intervene. The project supports both OpenAI's chat completions and Claude's messages API, plus the newer OpenAI responses format, covering most agent frameworks.

Who should use this?

Devs building agentic workflows that need human oversight before sensitive actions (sending emails, making API calls, modifying files). Internal tools teams that want to add a review step without changing agent code. Anyone experimenting with agent github copilot or similar frameworks who wants a safety layer. It's less useful if your agents already have robust validation or if you need high throughput without human review.

Verdict

This fills a real niche for human-in-the-loop agent control, and the implementation is functional and thoughtful. However, with only 39 stars and a 0.899% credibility score, it's early-stage software with limited community validation. The codebase shows care in its design, but there's no visible test coverage, and documentation is sparse. Worth evaluating for controlled environments or internal tools, but don't bet production-critical workflows on it yet without thorough testing yourself.

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