linke-ai

linke-ai / hermes-agent-team

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基于 Hermes Agent 构建的本地多 Agent 团队协作 Web 系统

agent-team ai-agents flask hermes-agent kan-ban
18
8
89% credibility
Found May 22, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Hermes Agent Team is a multi-agent collaboration platform where one AI leader coordinates several worker agents to tackle complex tasks together. You submit a request through a web dashboard, the leader breaks it into subtasks, workers execute them in parallel, and the leader reviews and synthesizes the results into a final answer. The system includes a visual kanban board for tracking progress, real-time terminal access to watch agents work, and the ability to step in when agents need human input. It's designed for anyone who wants AI agents to work as a team rather than asking one assistant to do everything alone.

How It Works

1
👋 You discover a team of AI assistants

You hear about Hermes Agent Team - a system where multiple AI agents work together like a real team, with a leader who coordinates and workers who handle specialized tasks.

2
🤖 You set up your AI team

You create your team by adding a leader agent and one or more worker agents. Each worker gets a role and description so they know what they're good at.

3
📋 You submit a complex task

You type your request into the web dashboard - something that would normally take a lot of back-and-forth, like researching a topic, planning an event, or solving a multi-part problem.

4
⚡ Your team jumps into action

The leader agent instantly understands your request, breaks it into smaller pieces, and assigns each piece to the right worker - all visible on the kanban board.

5
Workers handle their tasks
👀
You watch quietly

You observe as workers complete their assignments one by one, with the leader tracking everyone's progress

💬
You help when needed

If a worker gets stuck or needs a decision, you answer their question and they continue

6
🔍 The leader reviews everything

Once all workers report back, the leader agent reviews every result, checks for completeness, and prepares a final answer for you.

✅ You get a complete answer

The leader synthesizes all the worker results into a clear, organized response - saving you hours of coordinating back and forth.

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AI-Generated Review

What is hermes-agent-team?

Hermes-agent-team is a Python web application that orchestrates multiple AI agents working together on tasks. Think of it as a command center for a team of AI workers: one leader agent coordinates work, while multiple worker agents execute specialized subtasks. The system uses a Kanban board to track tasks, supports real-time terminal access via WebSocket, and persists all state to SQLite. You interact through a browser-based dashboard that shows agent status, task progress, and system events. The system integrates with the hermes CLI tool and exposes an MCP server so leader agents can discover and dispatch work to workers programmatically.

Why is it gaining traction?

The multi-agent collaboration model addresses a real pain point: single-agent systems hit ceilings on complex tasks. This project lets you spin up a leader-worker team where the leader breaks down user requests and delegates to specialized workers, then synthesizes their results. The Kanban integration is particularly clever—it bridges the gap between agent orchestration and traditional project management, making it visible and auditable. Human-in-the-loop support means agents can pause and ask you questions when they need clarification. The skill installer lets you extend agents with custom capabilities from git repositories.

Who should use this?

Developers building AI-powered automation pipelines who need multiple agents cooperating on complex workflows. Teams evaluating multi-agent architectures for trading bots, SMM tools, or coding assistants. Researchers prototyping agent team dynamics. Not yet suitable for production unless you're comfortable with early-stage software and willing to contribute fixes.

Verdict

At 18 stars with a 0.9% credibility score, this is experimental software with promising architecture but limited community validation. The code structure is solid and the feature set is coherent, but documentation is sparse and test coverage unknown. Worth exploring if you're deep into multi-agent patterns; wait for more maturity if you need stability.

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