Is the atlas-agent-control-plane MCP server actively maintained?

The atlas-agent-control-plane MCP server was last committed 1mo ago and has 2 contributors. Issue health: 50% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

atlas-agent-control-plane MCP Server

Name: malevrigns/atlas-agent-control-plane
Rating: 3.3 (1 reviews)
Author: malevrigns

malevrigns/atlas-agent-control-plane

Score: 57.1 Rank #7720 Agent Framework

[![AgentRank](https://agentrank-ai.com/api/v1/badge/malevrigns--atlas-agent-control-plane)](https://agentrank-ai.com/tool/malevrigns--atlas-agent-control-plane/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge)

Are you the maintainer of malevrigns/atlas-agent-control-plane? Claim this listing →

AtlasAgent - an auditable AI agent control plane: evidence-backed memory, governed tool runtime, checkpoint DAG recovery, and a 55-chapter engineering tutorial. FastAPI / Next.js PWA / Textual TUI

Add AgentRank to Claude Code Discover and compare tools like malevrigns/atlas-agent-control-plane — your AI finds the right one automatically

Get API Access →

claude mcp add agentrank -- npx -y agentrank-mcp-server

Overview

malevrigns/atlas-agent-control-plane is a Python tool licensed under MIT. AtlasAgent - an auditable AI agent control plane: evidence-backed memory, governed tool runtime, checkpoint DAG recovery, and a 55-chapter engineering tutorial. FastAPI / Next.js PWA / Textual TUI Topics: agent, agent-skills, agentic-workflow, fastapi, typescript, agent-orchestration, agentic-ai, agent-framework, ai-agent-framework, ai-agents, auditable-ai, control-plane, llm-agent, long-term-memory, mcp, nextjs, python, rag, retrieval-augmented-generation, tool-use.

Ranked #7720 out of 24846 indexed tools.

Ecosystem

Python MIT

agentagent-skillsagentic-workflowfastapitypescriptagent-orchestrationagentic-aiagent-frameworkai-agent-frameworkai-agentsauditable-aicontrol-planellm-agentlong-term-memorymcpnextjspythonragretrieval-augmented-generationtool-use

Score Breakdown

Stars 15% 106

106 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → moderate activity

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 2

2 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 1

Description Detailed

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Freshness high impact

Last commit was 59 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

Badge all embed codes →

Markdown

[![AgentRank](https://agentrank-ai.com/api/v1/badge/malevrigns--atlas-agent-control-plane)](https://agentrank-ai.com/tool/malevrigns--atlas-agent-control-plane/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge)

HTML

<a href="https://agentrank-ai.com/tool/malevrigns--atlas-agent-control-plane/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge"><img src="https://agentrank-ai.com/api/v1/badge/malevrigns--atlas-agent-control-plane" alt="AgentRank"></a>

Embed Widget docs →

HTML

<script src="https://agentrank-ai.com/embed.js" data-tool="malevrigns/atlas-agent-control-plane"></script>

Details

Stars: 106
Forks: 1
Open Issues: 0
Closed Issues: 0
Contributors: 2
Dependents: —
Language: Python
License: MIT
Last Commit: 1mo ago

Matched Queries

topic:mcp

From the README

<div align="center">

<br><br>

<a href="https://github.com/malevrigns/atlas-agent-control-plane/stargazers"></a>&nbsp;
<a href="https://github.com/malevrigns/atlas-agent-control-plane/actions"></a>&nbsp;
<a href="LICENSE"></a>

[English](#english) · [中文](#中文) · [📖 完整教程](tutorial/README.md)

</div>

---

<a id="english"></a>

### Why I built this

When I deployed OpenClaw for the first time, the experience was unlike anything before. But a question followed me for weeks: these general agents are already this strong — is there any point in building another one?

The answer I settled on: a general agent is a good knife; a vertical agent is a machine assembled for one workflow. They don't compete. So this project is my attempt to build the machine — not a chat shell wired to an LLM, but a system that plans a task, executes it step by step, calls tools, observes results, and answers with evidence it can point to.

Along the way I kept running into the same wall: the demos were impressive,

Read full README on GitHub →